The incidence of Parkinson's disease (PD) increases with age. Previous pharmacological studies have shown the potential of Huatan Jieyu Granules (HGs) for the treatment of PD, but the exact mechanisms remain unclear. This study aimed to explore the effects of herbal treatment on PD using mouse models and single-cell sequencing.

In this study, we established in vivo 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD models in mice. Motor function was assessed through behavioral tests. Immunofluorescence was used to examine dopaminergic neuron loss. Single-cell sequencing was performed on mice from the blank, PD model and medication groups. After quality control and dimensionality reduction of the single-cell data, cells were clustered, and different cell types were identified. We then identified the intersection of differentially expressed genes (DEGs1) in the blank and model groups and DEGs2 in the model and medication groups, yielding intersected DEGs. Key drug targets were identified by intersecting these DEGs with the drug targets of active ingredients in TCM. Topological analysis of the PPI network was used to identify key genes. Cell types exhibiting high expression of these genes were designated as key cells. These key cells were subjected to cellular communication analysis and temporal analysis, after which they were classified into subtypes.

HGs significantly improved motor function and prevented dopaminergic neuronal loss in the substantia nigra (SN) of MPTP-treated mice. A total of 34 cell clusters were delineated, with 9 cell types identified, including oligodendrocytes (oligo), neurons, and T cells. We identified 758 intersected DEGs and 13 key drug targets, including Egfr, Ntrk2, Grm5, Htr2c, Bcl2l1. Oligo and neuronal cells were identified as key cells due to higher expression levels of these key genes. In the cellular communication analysis, oligo-neuronal interactions in the blank and model groups, and oligo-OPC and oligo-T cell interactions in the medication group, exhibited the most receptor-ligand interactions. In temporal analysis, both oligo and neuronal cells were differentiated into 9 states, with C1 being the most differentiated.

HGs demonstrate neuroprotective effects in MPTP-treated mice. Using single-cell sequencing, we identified five key genes (Egfr, Ntrk2, Grm5, Htr2c, Bcl2l1) and two key cell types (oligo and neuronal) related to HGs in PD. These findings provided a foundation for understanding the molecular mechanisms by which HGs treat PD.

This study aims to investigate the expression pattern and clinical significance of Piezo1 and Piezo2 in various cancers, focusing on gastric cancer (GC).

The study investigated the mRNA expression levels of Piezo1 and Piezo2 in tumor samples from different cancers using the BEST online database. The case-control studies about the relation between Piezo1 and Piezo2 and GC were retrieved from PubMed, Embase, Web of Science, and Cochrane Library. The retrieval time was from inception to October, 2023. The meta-analysis of the included literatures was conducted by the STATA 12.0 software. Additionally, the expression profiles of Piezo1 and Piezo2 in tumor and normal gastric tissues were analyzed, and their clinical drug relevance was assessed using the CPADS database. The research program has been registered with PROSPERO (CRD42023495836).

The analysis demonstrated elevated mRNA expression of both Piezo1 and Piezo2 in the majority of tumor samples. Of particular note was the significant increase observed in GC tissue compared to normal tissue (all p < 0.05). Additionally, the meta-analysis revealed a meaningful correlation between high expression levels of Piezo1 and Piezo2 and poor prognosis in patients with GC (HR = 1.48, 95% CI = 1.27-1.69, p < 0.0001). This study identified a significant correlation between high levels of Piezo1 expression and the TNM phase (OR = 1.87, 95% CI = 1.21-2.91, p = 0.005). Furthermore, enhanced Piezo2 expression was observed to be positively correlated with survival status (OR = 2.12, 95% CI = 1.31-3.44, p = 0.002). Piezo1 (p = 0.028, R2 = 0.12) and Piezo2 (p = 0.049, R2 = 0.09) have been identified as potential therapeutic targets for GC treatment, according to drug sensitivity analyses.

The findings of this study indicate that the expression levels of Piezo1 and Piezo2 have the potential to serve as diagnostic indicators or therapeutic targets for GC management.

CRD42023495836 (PROSPERO).

Liver hepatocellular carcinoma (LIHC) is a highly aggressive cancer with poor prognosis due to its complex tumor microenvironment (TME) and immune evasion. Regulatory T cells (Tregs) play a critical role in tumor progression. Suppressor of cytokine signaling 2 (SOCS2), a key immune regulator, may modulate Treg activity and impact LIHC growth and metastasis.

To explore how the SOCS2 affects Treg activity in LIHC and its impact on tumor growth and metastasis.

LIHC transcriptome data from The Cancer Genome Atlas database were analyzed using Gene Set Enrichment Analysis, Estimation of Stromal and Immune Cells in Malignant Tumors Using Expression Data, and Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts to evaluate immune pathways and Treg infiltration. Key prognostic genes were identified using Weighted Gene Co-expression Network Analysis and machine learning. In vitro, co-culture experiments, migration assays, apoptosis detection, and enzyme-linked immunosorbent assay were conducted. In vivo, tumor growth, metastasis, and apoptosis were assessed using subcutaneous and lung metastasis mouse models with hematoxylin and eosin staining, Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling, and immunohistochemistry analyses.

SOCS2 overexpression inhibited Treg cell activity, reducing LIHC cell migration and invasion while increasing apoptosis. In vivo, SOCS2 suppressed tumor growth and metastasis, confirming its therapeutic potential.

SOCS2 modulates CD4+ T function in the TME, contributing to LIHC progression. Targeting SOCS2 presents a potential therapeutic strategy for treating LIHC.

Aging is a complex, progressive, and irreversible biological process that entails numerous structural and functional changes in the organism. These changes affect all bodily systems, reducing their ability to respond and adapt to the environment. Chronic inflammation is one of the key factors driving the development of age-related diseases, ultimately causing a substantial decline in the functional abilities of older individuals. This persistent inflammatory state (commonly known as "inflammaging") is characterized by elevated levels of pro-inflammatory cytokines, an increase in oxidative stress, and a perturbation of immune homeostasis. Several factors, including cellular senescence, contribute to this inflammatory milieu, thereby amplifying conditions such as cardiovascular disease, neurodegeneration, and metabolic disorders. Exploring the mechanisms of chronic inflammation in aging is essential for developing targeted interventions aimed at promoting healthy aging. This review explains the strong connection between aging and chronic inflammation, highlighting potential therapeutic approaches like pharmacological treatments, dietary strategies, and lifestyle changes.

Recently, oxidative stress and inflammatory responses have been shown to directly impact tumor growth and the tumor microenvironment (TME). However, more research is necessary to fully understand the relationship between oxidative stress and inflammatory responses and colorectal cancer (CRC).

The FindCluster algorithm was used to extract CRC Single-cell RNA sequencing (scRNA-seq) data and identify tumor cell groupings. From the MSigDB database, genes associated with oxidative stress and the inflammatory response were taken. We identified molecular subtypes and built a predictive risk model with the LASSO-Cox method using the ConsensusClusterPlus software suite. We incorporated the prognostic risk model and other clinicopathological parameters into a column-line chart. Finally, we used Quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry to check the expression of the unreported hub model genes. Cell proliferation was assessed using EDU and colony formation assays. Reactive Oxygen Species (ROS) tests were used to quantitatively determine the ROS content in CRC cells. The ability of CRC cells to invade and migrate was examined using transwell experiments. The regulatory functions of hub model genes were discovered in vivo using a xenograft model tumor assay.

Oxidative stress and inflammatory response factors in monocytic/macrophages of CRC were significantly upregulated, and their oxidative stress and inflammatory response functions were significantly higher than those of other cell subgroups, as indicated by the enrichment score. These factors showed significant synergistic overexpression and enrichment in this cell population. We constructed a prognostic risk model consisting of seven signatures. The good and stable prognostic evaluation efficacy of the model was confirmed, and risk scores were determined to be independent prognostic factors for CRC. We explored the relationship between the risk score model and malignant progression of tumor cells, tumor immune microenvironment, genomic variation, chemotherapy resistance, and immune response. Further qPCR and immunohistochemistry analysis showed that the expression of ZNF385A was high in CRC tissues. The functional experiment results indicated that interfering with the expression of ZNF385A could suppress the proliferation, ROS, migration and invasion of SW620 cells in vitro and the growth of xenograft tumors in vivo.

In this study, we investigated the critical expression patterns of oxidative stress- and inflammatory response-related genes in CRC, which may contribute to the prognosis and immunotherapy of CRC. Additionally, we discovered ZNF385A to be a novel oncogene in CRC. These findings imply that this model may be applied to assess prognostic risk and identify potential therapeutic targets for CRC patients.

The miR-125a-5p has been reported influence the development of lung cancer, however, the link between it and chronic obstructive pulmonary disease (COPD) is still not well understood. Hence, this study was designed to investigate the molecular pathway by which miR-125a-5p related biomarkers were involved in COPD.

The differentially expressed genes (DEGs) and module genes related to COPD in GSE100153 were screened out by differential analysis and weighted gene co-expression network analysis, respectively. Then, the target genes of miR-125a-5p obtained from miRWalk database were intersected with DEGs and module genes, followed by identification of biomarkers through SVM-RFE algorithms. Moreover, the gene set enrichment analysis, immune infiltration analysis, construction of regulatory network, single-cell analysis and Mendelian randomization (MR) analysis were performed. At last, the expression levels of the biomarkers were further validated in GSE100153 and GSE146560 as well as in qRT-PCR.

A total of 10 genes were acquired by intersecting the 126 DEGs, the 3989 module genes, and 2329 target genes, of which PITHD1, CNTNAP2 and GUCD1 were identified as biomarkers. Enrichment analysis showed their roles in various cellular functions. In addition, significant associations were identified between 9 distinct cells and biomarkers. Subsequently, 5 TFs and 63 therapeutic agents were predicted as biomarkers. Moreover, GUCD1 and PITHD1 were significantly different between case and control in T cells and Alveolar cells. In COPD, GUCD1 and PITHD1 were significantly down-regulated in GSE100153 and GSE146560 datasets and confirmed by qRT-PCR.

In our study, PITHD1, CNTNAP2, and GUCD1 were recognized as biomarkers related to miR-125a-5p-related genes in COPD, providing new references for treatment of COPD.

Colorectal cancer (CRC) is a major health problem, with an alarming increase of early-onset CRC (EO-CRC) cases among individuals under 50 years of age. This trend shows the urgent need for understanding the underlying mechanisms leading to EO-CRC development and progression. There is significant evidence that the gut microbiome acts as a key player in CRC by triggering molecular changes in the colon epithelium, leading to tumorigenesis. However, a comprehensive collection and comparison of methods to study such tumor-microbiome interactions in the context of EO-CRC is sparse. This review provides an overview of the available in vivo, ex vivo as well as in vitro approaches to model EO-CRC and assess the effect of gut microbes on tumor development and growth. By comparing the advantages and limitations of each model system, it highlights that, while no single model is perfect, each is suitable for studying specific aspects of microbiome-induced tumorigenesis. Taken together, multifaceted approaches can simulate the human body's complexity, aiding in the development of effective treatment and prevention strategies for EO-CRC.

Aging and long non-coding RNAs (lncRNAs) are research hotspots in melanoma. However, no study has so far explored the relationship between melanoma prognosis and aging-related lncRNAs (ARLs).

The Cancer Genome Atlas database, the GTEx database, and the HAGR database were used in this study in a combined manner. Univariate and multivariate cox regression analyses were used to screen out lncRNA signatures associated with overall survival (OS) in the primary dataset. The risk scoring model was analyzed by risk stratification and tested internally. The protein expression levels of possible target genes of ARLs were verified by immunohistochemistry analysis in HPA database. Finally, gene enrichment analysis was performed.

In the primary dataset, five OS-related lncRNA signatures (AC011481.1, USP30-AS1, EBLN3P, LINC01527, HLA-DQB1-AS1) were screened out. The survival curve showed that the high-risk group had a worse prognosis than the low-risk group. The immunohistochemical analysis revealed that reduced expression of Epidermal Growth Factor Receptor (EGFR), along with increased expression of Activating Transcription Factor 2 (ATF2) and DNA Polymerase Delta 1 (POLD1), was linked to a worse prognosis. Finally, enrichment analysis revealed that OS-related DELs were significantly enriched in the regulation of reactive oxygen metabolism, etc. The ARGs were significantly activated in the SKCM tissues. The regulation of aging in melanoma cells may be realized through ferroptosis, immunity, and autophagy and so on.

The ARL signature obtained in this study had better prognostic ability than individual clinical features.

An increasing proportion of the aging population has led to a rapid increase in the number of elderly patients with ulcerative colitis (UC). However, the molecular mechanisms by which aging causes UC remain unclear. In this study, we explored the role of aging-related genes (ARGs) in UC pathogenesis and diagnosis prediction.

Gene expression data were obtained from four independent datasets (GSE75214, GSE87466, GSE94648, and GSE169568) in the GEO database, and ARGs were derived from multiple public databases. After identifying UC-related ARGs, consistent clustering was performed to screen aging-related molecular subtypes, followed by the exploration of differences in the immune microenvironment and pathways between distinct subtypes. Next, core module genes were screened using WGCNA and then the hub genes were characterized using LASSO and random forest methods. Besides, the associations between hub genes, immune cells, and key pathways were explored. Finally, the expression levels of key genes were determined in a dextran sulfate sodium (DSS)-induced UC mouse model by qRT-PCR.

UC samples were classified into two subtypes (1 and 2), which displayed significant differences in the immune landscape and JAK/STAT signaling pathways. A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. In addition, these hub genes were closely associated with the infiltration of specific immune cells (such as neutrophils, memory B cells, and M2 macrophages) as well as with the JAK/STAT pathway. Later, experimental validation confirmed that ETS1 expression was markedly increased in a mouse model of UC.

Overall, aging, immune dysregulation, and UC process are closely associated. The identified feature genes, particularly ETS1, could serve as novel diagnostic biomarkers for UC. These findings have the potential to enhance the understanding of the age-related mechanisms of UC.

Currently, nearly one-third of newly diagnosed Colorectal cancer (CRC) cases and associated deaths worldwide are from China. Among Chinese provinces, Fujian exhibited the highest CRC mortality rate. This study aims to analyze long-term trends of CRC epidemiology in Xiamen, Fujian, China.

Data were obtained from various cancer surveillance systems, including the Xiamen Cancer Register system, the Xiamen Death Register system, and the household registration system. Trends in incidence, mortality and survival rates from 2011 to 2020 were analyzed using annual percent changes (APC) and average annual percent changes (AAPC), stratified by sex and residency status.

A total of 7,406 new cases and 3,726 CRC-related deaths were reported from Xiamen. The age-standardized of incidence and mortality rates were 26.09 per 100,000 and 12.70 per 100,000, respectively. The observed 5-year survival rate was 46.65%, with an age-standardized survival of 51.24%. CRC incidence in Xiamen is significantly increasing (AAPC = 4.90%). In contrast, trends in CRC mortality and survival rates are not significant. Urban areas exhibited higher rates of CRC incidence, mortality, and survival rate compared to rural areas, though rural areas showed a more marked upward trend.

The burden of CRC in Xiamen is notably high, with a concerning increasing in incidence. This underscores the urgent need for targeted strategies to address this public health challenges.

Cellular senescence is a key promoter of tumorigenesis and malignant progression. This study aimed to develop a predictive model for assessing cellular senescence in gastric cancer (GC) outcomes. We identified senescence-related genes and lncRNAs from 375 stomach adenocarcinoma (STAD) patients and established a prognostic senescence score using multivariate Cox regression, validated in testing, TCGA-STAD and the combined TCGA-COAD and READ cohorts. The model's predictive efficacy was evaluated across clinical subgroups, tumour microenvironments and immune cell infiltration. A total of 116 senescence-related lncRNAs were filtered, and patients were clustered into two senescent subtypes. The lncRNA signature identified LINC01579 as an independent prognostic factor for GC. Low-risk groups showed prolonged overall survival, increased immune cell infiltration and reduced mutation load. Downregulation of LINC01579 using antisense oligonucleotides (ASOs) on normal human fibroblasts decreased cellular proliferation and migration in GC. Collectively, this study established and validated a promising prognostic model connecting senescence-related lncRNAs and clinical outcome in GC and provided potential senescence-related biomarkers for GC prognosis prediction.

The phosphoinositide 3-kinase (PI3K)-Akt axis is one of the most frequently activated pathways and is demonstrated as a therapeutic target in Kirsten rat sarcoma viral oncogene homolog (KRAS)-mutated colorectal cancer (CRC). Targeting the PI3K-Akt pathway has been a challenging undertaking through the decades. Here we unveiled an essential role of E3 ligase SMAD ubiquitylation regulatory factor 1 (Smurf1)-mediated phosphoinositide-dependent protein kinase 1 (PDK1) neddylation in PI3K-Akt signaling and tumorigenesis. Upon growth factor stimulation, Smurf1 immediately triggers PDK1 neddylation and the poly-neural precursor cell expressed developmentally downregulated protein 8 (poly-Nedd8) chains recruit methyltransferase SET domain bifurcated histone lysine methyltransferase 1 (SETDB1). The cytoplasmic complex of PDK1 assembled with Smurf1 and SETDB1 (cCOMPASS) consisting of PDK1, Smurf1 and SETDB1 directs Akt membrane attachment and T308 phosphorylation. Smurf1 deficiency dramatically reduces CRC tumorigenesis in a genetic mouse model. Furthermore, we developed a highly selective Smurf1 degrader, Smurf1-antagonizing repressor of tumor 1, which exhibits efficient PDK1-Akt blockade and potent tumor suppression alone or combined with PDK1 inhibitor in KRAS-mutated CRC. The findings presented here unveil previously unrecognized roles of PDK1 neddylation and offer a potential strategy for targeting the PI3K-Akt pathway and KRAS mutant cancer therapy.

Mucosal-associated invariant T (MAIT) cells are a predominant subset of innate-like T cells in humans, characterized by diverse gene expression profiles and functional capabilities. However, the factors influencing the transcriptomes and effector functions of MAIT cells, particularly at mucosal barriers, remain largely unclear.

In this study, we employed single-cell RNA sequencing (scRNA-seq) and functional assays to investigate the transcriptomic and functional characteristics of intestinal MAIT cells in mouse models during aging. We also extended scRNA-seq analysis to human intestinal MAIT cells to compare their gene expression patterns with those observed in aged mice.

Our findings demonstrated that the transcriptomes and functional capabilities of intestinal MAIT cells shifted from MAIT17 to MAIT1 profiles with aging in mouse models, with notable changes in the production of cytotoxic molecules. Further scRNA-seq analysis of human intestinal MAIT cells revealed a segregation into MAIT1 and MAIT17 subsets, displaying gene expression patterns that mirrored those seen in aged mouse models. The transcription factor RORγt was expressed in both MAIT1 and MAIT17 cells, acting to repress IFNγ production while promoting IL17 expression. Moreover, reduced expression of RORC and Il17A was correlated with poorer survival outcomes in colorectal cancer patients.

These results suggest that aging induces a functional shift between MAIT1 and MAIT17 cells, which may be influenced by transcriptional regulators like RORγt. The observed alterations in MAIT cell activity could potentially impact disease prognosis, particularly in colorectal cancer. This study provides new insights into the dynamics of MAIT cell responses at mucosal barriers, highlighting possible therapeutic targets for modulating MAIT cell functions in aging and disease.

The role of cellular senescence in the tumor microenvironment of pancreatic cancer (PC) remains unclear, particularly regarding its impact on prognosis and immunotherapy outcomes.

We utilized single-cell sequencing datasets (GSE155698 and GSE154778) for pancreatic cancer from the Gene Expression Omnibus (GEO) database and bulk RNA-seq data from the University of California, Santa Cruz (UCSC) and International Cancer Genome Consortium (ICGC) repositories, creating three patient cohorts: The Cancer Genome Atlas (TCGA) cohort, PAAD-AU cohort, and PAAD-CA cohort. Dimensionality reduction cluster analysis processed the single-cell data, while weighted gene co-expression network analysis (WGCNA) and differential expression gene analysis were applied to bulk RNA-seq data. Prognostic models were developed using Cox proportional hazards (COX) and least absolute shrinkage and selection operator (LASSO) regression, with validation through survival analysis, decision curve analysis, and principal component analysis (PCA). Tumor mutation data were analyzed using the "maftools" package, and the immune microenvironment was assessed with TIMER2 data.

We developed a senescence-related (SENR) six-gene prognostic signature for PC, which stratifies patients by risk, with high-risk groups showing poorer prognoses. This model also offers predictive insights into tumor mutations and immune microenvironment characteristics. Caveolin-1 (CAV1) emerged as a significant prognostic biomarker, with functional validation showing its role in promoting cancer cell proliferation and migration, highlighting its potential as a therapeutic target.

This study provides a novel senescence-related prognostic tool for PC, enhancing patient stratification for prognosis and immunotherapy, and identifies CAV1 as a key gene with clinical significance for targeted interventions.

Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa.

To examine the involvement of metabolism-related genes.

Gene expression datasets GSE110729 and GSE205668 were accessed from the GEO database. DEGs between obese and lean groups were identified through DESeq2. Metabolism-related genes and pathways were detected using enrichment analysis, WGCNA, Random Forest, and XGBoost. The identified signature genes were validated by real-time quantitative PCR (qRT-PCR) in mouse models.

A total of 389 genes exhibiting differential expression were discovered, showing significant enrichment in metabolic pathways, particularly in the propanoate metabolism pathway. The orangered4 module, which exhibited the highest correlation with propanoate metabolism, was identified using Weighted Correlation Network Analysis (WGCNA). By integrating the DEGs, WGCNA results, and machine learning methods, the identification of two metabolism-related genes, Storkhead Box 1 (STOX1), NACHT and WD repeat domain-containing protein 2(NWD2) was achieved. These signature genes successfully distinguished between obese and lean individuals. qRT-PCR analysis confirmed the downregulation of STOX1 and NWD2 in mouse models of obesity.

This study has analyzed the available GEO dataset in order to identify novel factors associated with obesity metabolism and found that STOX1 and NWD2 may serve as diagnostic biomarkers.

We investigated the mRNA expression of 124 cuproptosis-associated genes in 7489 biopsies from 20 different tumor types of The Cancer Genome Atlas (TCGA). The KM plotter algorithm has been used to calculate Kaplan-Meier statistics and false discovery rate (FDR) corrections. Interaction networks have been generated using Ingenuity Pathway Analysis (IPA). High mRNA expression of 63 out of 124 genes significantly correlated with shorter survival times of cancer patients across all 20 tumor types. IPA analyses revealed that their gene products were interconnected in canonical pathways (e.g., cancer, cell death, cell cycle, cell signaling). Four tumor entities showed a higher accumulation of genes than the other cancer types, i.e., renal clear cell carcinoma (n = 21), renal papillary carcinoma (n = 13), kidney hepatocellular carcinoma (n = 13), and lung adenocarcinoma (n = 9). These gene clusters may serve as prognostic signatures for patient survival. These signatures were also of prognostic value for tumors with high mutational rates and neoantigen loads. Cuproptosis is of prognostic significance for the survival of cancer patients. The identification of specific gene signatures deserves further exploration for their clinical utility in routine diagnostics.

The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications.

By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis.

Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation.

In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.

The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis.

It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC).

In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR).

There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187.

Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.

Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis.

Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS.

We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05).

The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.

Patients with colorectal cancer commonly experience disturbances in coagulation homeostasis. Activation of the coagulation system contributes to cancer-associated thrombosis as the second risk factor for death in cancer patients. This study intended to discover coagulation-related genes and construct a risk model for colorectal cancer patients' prognosis.

Coagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model's predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients.

Eight hundred forty-five genes were obtained by searching the theme term "coagulation" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction.

The prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.

Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival.

To investigate the prognosis of GMA and develop predictive model.

From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA's prognosis.

This model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors' use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98, P < 0.001)] and chemotherapy (HR: 0.62, 95%CI: 0.58-0.66, P < 0.001).

The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

Gastric cancer (GC) remains a formidable challenge in oncology, ranking as a leading cause of cancer mortality globally. This underscores an urgent need for innovative prognostic markers that can revolutionize patient management and outcomes. Recent insights into cancer biology have spotlighted the profound influence of lipid metabolism alterations on tumorigenesis, tumor progression, and the tumor microenvironment. These alterations not only fuel cancer cell growth and proliferation but also play a strategic role in evading immune surveillance and promoting metastasis. The intricate web of lipid metabolism in cancer cells, characterized by deregulated uptake, synthesis, and oxidation of fatty acids (FAs), opens new avenues for targeted therapeutic interventions and prognostic evaluations. Specifically, this study zeroes in on apolipoprotein A-I (APOA1), a key player in lipid metabolism, to unearth its prognostic value in GC. By delving into the role of lipid metabolism-related genes, particularly APOA1, we aim to unveil their potential as groundbreaking biomarkers for GC prognosis. This endeavor not only aims to enhance our understanding of the molecular underpinnings of GC but also to spearhead the development of lipid metabolism-based strategies for improved diagnostic, prognostic, and therapeutic outcomes.

Transcriptomic and clinical data from GC patients and healthy individuals were sourced from The Cancer Genome Atlas (TCGA) database, a comprehensive project that molecularly characterizes over 20,000 primary cancer and matched normal samples across 33 cancer types. Significantly differentially expressed lipid metabolism-related genes were identified using the "limma" package in R. Prognostic genes were selected via univariate Cox regression analysis. Differential gene enrichment analysis was performed using Metascape (http://www.metascape.org). The Human Protein Atlas (HPA, https://www.proteinatlas.org) provided information on APOA1 protein expression in GC and healthy tissues. Immune cell infiltration was analyzed using the CIBERSORT algorithm (http://cibersort.stanford.edu).

Significant differences in lipid metabolism-related gene expression were observed between GC and normal tissues, closely linked to FA metabolism, oxidoreductase activity, and sphingolipid metabolism. APOA1 emerged as a potential prognostic biomarker by intersecting prognostic and differentially expressed lipid metabolism genes. Immunohistochemical analysis confirmed APOA1 downregulation in GC. The receiver operating characteristic (ROC) analysis demonstrated its predictive value, with the area under the curve (AUC) being 0.64 [95% confidence interval (CI): 0.52-0.76]. APOA1 expression correlated with immune cell infiltrations. Clinical serum APOA1 results revealed lower levels in GC patients (1.38 vs. 1.26; P<0.05), associated with poor prognosis (hazard ratio =1.50; P<0.001) and clinical characteristics. ROC analysis of serum APOA1 demonstrated good diagnostic ability (AUC: 0.63, 95% CI: 0.61-0.65). Serum APOA1 levels significantly increased after treatment.

This study highlights lipid metabolism reprogramming in GC and identifies APOA1 as a potential diagnostic and prognostic biomarker, suggesting its clinical utility in managing GC.

Emerging evidence demonstrates the vital role of aging and long non-coding RNAs (lncRNAs) in breast cancer (BC) progression. Our study intended to develop a prognostic risk model based on aging-related lncRNAs (AG-lncs) to foresee BC patients' outcomes.

307 aging-related genes (AGs) were sequenced from the TCGA project. Then, 697 AG-lncs were identified by the co-expression analysis with AGs. Using multivariate and univariate Cox regression analysis, and LASSO, 6 AG-lncs, including al136531.1, mapt-as1, al451085.2, otud6b-as1, tnfrsf14-as1, and linc01871, were validated to compute the risk score and establish a risk signature. Expression levels of al136531.1, mapt-as1, al451085.2, tnfrsf14-as1, and linc01871 were higher in low-risk BC patients, whereas otud6b-as1 expression was higher in high-risk BC patients. In the training and testing set, high-risk patients performed shorter PFI, OS, and DFS than low-risk patients.

Our risk signature had the highest concordance index among other established prognostic signatures and displayed ideal predictive ability for 1-, 3- and 5-year patient OS in the nomogram. Additionally, BC patients with different risk score levels showed different immune statuses and responses to immunotherapy via GSEA, ssGSEA, ESTIMATE algorithm, and TIDE algorithm analysis. Of note, the qRT-PCR analysis validated that these 6 AG-lncs expressed quite differentially in BC tissues at various clinical stages.

The risk signature of 6 AG-lncs might offer a novel prognostic biomarker and promisingly enhance BC immunotherapy's effectiveness.

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

Gastric intestinal metaplasia (GIM) is an essential precancerous lesion. Although the reversal of GIM is challenging, it potentially brings a state-to-art strategy for gastric cancer therapeutics (GC). The lack of the appropriate in vitro model limits studies of GIM pathogenesis, which is the issue this work aims to address for further studies.

The air-liquid interface (ALI) model was adopted for the long-term culture of GIM cells in the present work. This study conducted Immunofluorescence (IF), quantitative real-time polymerase chain reaction (qRT-PCR), transcriptomic sequencing, and mucoproteomic sequencing (MS) techniques to identify the pathways for differential expressed genes (DEGs) enrichment among different groups, furthermore, to verify novel biomarkers of GIM cells.

Our study suggests that GIM-ALI model is analog to the innate GIM cells, which thus can be used for mucus collection and drug screening. We found genes MUC17, CDA, TRIM15, TBX3, FLVCR2, ONECUT2, ACY3, NMUR2, and MAL2 were highly expressed in GIM cells, while GLDN, SLC5A5, MAL, and MALAT1 showed down-regulated, which can be used as potential biomarkers for GIM cells. In parallel, these genes that highly expressed in GIM samples were mainly involved in cancer-related pathways, such as the MAPK signal pathway and oxidative phosphorylation signal pathway.

The ALI model is validated for the first time for the in vitro study of GIM. GIM-ALI model is a novel in vitro model that can mimic the tissue micro-environment in GIM patients and further provide an avenue for studying the characteristics of GIM mucus. Our study identified new markers of GIM as well as pathways associated with GIM, which provides outstanding insight for exploring GIM pathogenesis and potentially other related conditions.

Aging is a very complex physiological phenomenon, and sEVs are involved in the regulation of this mechanism. Serum samples from healthy individuals under 30 and over 60 years of age were collected to analyze differences in sEVs proteomics.

Based on PBA analysis, we found that sEVs from the serum of elderly individuals highly express TACSTD2 and identified a subpopulation marked by TACSTD2. Using ELISA, we verified the upregulation of TACSTD2 in serum from elderly human and aged mouse. In addition, we discovered that TACSTD2 was significantly increased in samples from tumor patients and had better diagnostic value than CEA. Specifically, 9 of the 13 tumor groups exhibited elevated TACSTD2, particularly for cervical cancer, colon cancer, esophageal carcinoma, liver cancer and thyroid carcinoma. Moreover, we found that serum sEVs from the elderly (especially those with high TACSTD2 levels) promoted tumor cell (SW480, HuCCT1 and HeLa) proliferation and migration.

TACSTD2 was upregulated in the serum of elderly individuals and patients with tumors, and could serve as a dual biomarker for aging and tumors.

The aim of this study was to determine the significance of elective neck dissection (END) for patients of different ages with T2N0M0 oral squamous cell carcinoma (OSCC) and sought to analyze the reasons behind it and its value for clinical guidance.

This study enrolled 391 patients with T2N0M0 OSCC who were surgically treated in our hospital and were divided into young-, moderate-, and advanced-age groups according to our previous study. The Chi-square test and Kaplan-Meier analysis were performed for statistical analysis.

Compared with moderate- and advanced-age patients, young patients with T2N0M0 OSCC had higher lymph node metastasis rates and lymph node ratios. Therefore, END significantly improved the recurrence (p = 0.001) and survival (p = 0.001) for young patients, but not for moderate-age patients. Advanced-age patients even benefit from watchful waiting. END significantly improved recurrence and survival in young patients with smoking or alcohol consumption habits.

END improved the prognosis of young patients, and it was related to their higher metastasis rate. However, advanced-age patients benefited from the wait-and-see policy. END is essential for the young patients with smoking or drinking habit, it is also highly recommended for nonsmokers and nondrinkers.

This study investigates the influence of aging-related genes on endometrial cancer, a prominent gynecological malignancy with rising incidence and mortality. By analyzing gene expression differences between cancerous and normal endometrial tissues, 42 aging-related genes were identified as differentially expressed. Utilizing the TCGA-UCEC sample, consensus clustering divided the samples into two molecular subgroups, Aging low and Aging high, based on their expression profiles. These subgroups showed distinct prognoses and survival rates, with the Aging high group associated with DNA repair and cell cycle pathways, and the Aging low group showing suppressed metabolic pathways and increased immune cell infiltration, suggesting a potential for better immunotherapy outcomes. Mutation analysis did not find significant differences in mutation frequencies between the groups, but a high Tumor Mutation Burden (TMB) correlated with better prognosis. A risk score model was also developed, showcasing significant prognostic power. Further analysis of the SIX1 gene revealed its overexpression in cancer cells. Drug sensitivity tests indicated that the low-risk group might respond better to chemotherapy. This research underscores the significance of aging-related genes in endometrial cancer, offering insights into their prognostic value and therapeutic potential, which could lead to personalized treatment approaches and enhanced patient management.

Skin cutaneous melanoma (SKCM) is an aggressive and life-threatening skin cancer. G-protein coupled receptor 143 (GPR143) belongs to the superfamily of G protein-coupled receptors.

We used the TCGA, GTEx, CCLE, and the Human Protein Atlas databases to examine the mRNA and protein expression of GPR143. In addition, we performed a survival analysis and evaluated the diagnostic efficacy using the Receiver-Operating Characteristic (ROC) curve. Through CIBERSORT, R programming, TIMER, Gene Expression Profiling Interactive Analysis, Sangerbox, and Kaplan-Meier plotter database analyses, we explored the relationships between GPR143, immune infiltration, and gene marker expression of immune infiltrated cells. Furthermore, we investigated the proteins that potentially interact with GPR143 and their functions using R programming and databases including STRING, GeneMANIA, and GSEA. Meanwhile, the cBioPortal, UALCNA, and the MethSurv databases were used to examine the genomic alteration and methylation of GPR143 in SKCM. The Connectivity Map database was used to discover potentially effective therapeutic molecules against SKCM. Finally, we conducted cell experiments to investigate the potential role of GPR143 in SKCM.

We demonstrated a significantly high expression level of GPR143 in SKCM compared with normal tissues. High GPR143 expression and hypomethylation status of GPR143 were associated with a poorer prognosis. ROC analysis showed that the diagnostic efficacy of the GPR143 was 0.900. Furthermore, GPR143 expression was significantly correlated with immune infiltration in SKCM. We identified 20 neighbor genes and the pathways they enriched were anabolic process of pigmentation, immune regulation, and so on. Genomic alteration analysis revealed significantly different copy number variations related to GPR143 expression in SKCM, and shallow deletion could lead to high expression of GPR143. Ten potential therapeutic drugs against SKCM were identified. GPR143 knockdown inhibited melanoma cell proliferation, migration, and colony formation while promoting apoptosis.

Our findings suggest that GPR143 serves as a novel diagnostic and prognostic biomarker and is associated with the progression of SKCM.

Colorectal cancer (CRC) is the second most frequently diagnosed cancer and the fifth leading cause of cancer-related death in China. However, resistance to multiple chemotherapeutics after surgery leads to failure of the main therapy to CRC. Natural killer (NK) cells are innate cytotoxic lymphocytes that exhibit strong cytotoxic activity against tumour cells. NK cell-based therapy, either alone or in combination with chemotherapy, has achieved favourable results and holds promise for addressing recurrence and metastasis in CRC patients after surgery.

This is a prospective, randomised controlled clinical trial to evaluate efficacy and safety of interleukin 2 activated NK cells injection combined with XELOX (capecitabine plus oxaliplatin)-based chemotherapy for postoperative CRC patients. Participants will be randomly divided into treatment group and control group, and every group includes 40 patients. The treatment group will also receive NK cells (5×109) with+XELOX-based chemotherapy, while the control group will receive only XELOX-based chemotherapy. This treatment will be repeated for eight cycles (6 months). The follow-up period lasts about 3 years, during which CEA, CA19-9, CA125, enhancement CT and colonoscopy will be conducted. The primary endpoints of this study are progression-free survival and overall survival, while the secondary endpoint is safety (number and severity of adverse events). Additionally, we aim to identify cancer stem cells in peripheral blood and predictive biomarkers (cytokines secreted by NK cells and activated markers of NK cells) that indicate patients who achieve an effective response.

The study has been approved by the Clinical Research Ethics Committee of our hospital (approval number 2023LLSC006) and the Chinese Clinical Trials. It will be conducted in accordance with the Declaration of Helsinki. Written informed consent will be obtained from all participants. The study findings will be submitted to peer-reviewed journals for publication.

Chinese Clinical Trials Registry (ChiCTR2300075861).

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure.

Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes.

TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response.

In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC.

We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored.

Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples.

We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

Osteosarcoma (OS) is one of the most prevalent bone tumors in adolescents, and the correlation between aging and OS remains unclear. Currently, few accurate and reliable biomarkers have been determined for OS prognosis. To address this issue, we carried out a detailed bioinformatics analysis based on OS with data from the Cancer Genome Atlas data portal and Human Aging Genomic Resources database, as well as in vitro experiments. A total of 88 OS samples with gene expression profiles and corresponding clinical characteristics were obtained. Through univariate Cox regression analysis and survival analysis, 10 aging-associated survival lncRNAs (AASRs) were identified to be associated with the overall survival of OS patients. Based on the expression levels of the 10 AASRs, the OS patients were classified into two clusters (Cluster A and Cluster B). Cluster A had a worse prognosis, while Cluster B had a better prognosis. Then, 5 AASRs were ultimately included in the signature through least absolute shrinkage and selection operator-Cox regression analysis. Kaplan‒Meier survival analysis verified that the high-risk group exhibited a worse prognosis than the low-risk group. Furthermore, univariate and multivariate Cox regression analyses confirmed that the riskScore was an independent prognostic factor for OS patients. Subsequently, we discovered that the risk signature was correlated with the properties of the tumor microenvironment and immune cell infiltration. Specifically, there was a positive association between the risk model and naïve B cells, resting dendritic cells and gamma delta T cells, while it was negatively related to CD8+ T cells. Finally, in vitro experiments, we found that UNC5B-AS1 inhibited OS cells from undergoing cellular senescence and apoptosis, thereby promoting OS cells proliferation. In conclusion, we constructed and verified a 5 AASR-based signature, that exhibited excellent performance in evaluating the overall survival of OS patients. In addition, we found that UNC5B-AS1 might inhibit the senescence process, thus leading to the development and progression of OS. Our findings may provide novel insights into the treatment of OS patients.

Ovarian cancer (OC) is one of the malignant diseases of the reproductive system in elderly women. Aging-related genes (ARGs) were involved in tumor malignancy and cellular senescence, but the specifics of these mechanisms in OC remain unknown.

ARGs expression and survival data of OC patients were collected from TCGA and CPTAC databases. Subtype classification was used to identify the roles of hub ARGs in OC progression, including function enrichment, immune infiltration, and drug sensitivity. LASSO regression was utilized to confirm the prognosis significance for these hub ARGs. MTT, EdU, Transwell, and wounding healing analysis confirmed the effect of IGFBP5 on the proliferation and migration ability of OC cells.

ARGs were ectopically expressed in OC tissues compared to normal ovary tissues. Three molecular subtypes were divided by ARGs for OC patients. There were significant differences in ferroptosis, m6A methylation, prognosis, immune infiltration, angiogenesis, differentiation level, and drug sensitivity among the three groups. LASSO regression indicated that 4 signatures, FOXO4, IGFBP5, OGG1 and TYMS, had important prognosis significance. Moreover, IGFBP5 was significantly correlated with immune infiltration. The hub ARG, IGFBP5, expression was significantly decreased in OC patients compared to normal women. IGFBP5 could also reduce the migration and proliferation ability of OC cells compared to vector and NC groups.

IGFBP5 was correlated with OC prognosis and associated with OC migration and proliferation. This gene may serve as potential prognostic biomarkers and therapeutic targets for OC patients.

As the most prevalent primary brain tumor in adults, glioma accounts for the majority of all central nervous system malignant tumors. The concept of PANoptosis is a relatively new, underlining the interconnection and synergy among three distinct pathways: pyroptosis, apoptosis and necroptosis.

We performed single-cell annotations of glioma cells and determined crucial signaling pathways through cell chat analysis. Using least absolute shrinkage and selection operator (LASSO) and Cox analyses, we identified a gene set with prognostic values. Our model was validated using independent external cohort. In addition, we employed single-sample gene set enrichment analysis and xCell analyses to describe the detailed profile of infiltrated immune cells and depicted the gene mutation landscape in the two groups.

We identified seven distinct cell clusters in glioma samples, including oligodendrocyte precursor cells (OPCs), myeloid cells, tumor cells, oligodendrocytes, astrocytes, vascular cells and neuronal cells. We found that myeloid cells showed the highest PANoptosis activity. An intense mutual cell communication pattern between the tumor cells and OPCs and oligodendrocytes was observed. Differentially expressed genes between the high-PANoptosis and low-PANoptosis cell groups were obtained, which were enriched to actin cytoskeleton, cell adhesion molecules and gamma R-mediated phagocytosis pathways. We determined a set of five genes of prognostic significance: SAA1, SLPI, DCX, S100A8 and TNR. The prognostic differences between the two groups in the internal and external sets were found to be statistically significant. We found a marked correlation between S100A8 and activated dendritic cell, macrophage, mast cell, myeloid derived suppressor cell and Treg infiltration. Moreover, we have observed a significant increase of PTEN mutation in the high risk (HR) group of glioma patients.

In the present study, we have constructed a prognostic model that is based on the PANoptosis, and we have demonstrated its significant efficacy in stratifying patients with glioma. This innovative prognostic model offers novel insights into precision immune treatments that could be used to combat this disease and improve patient outcomes, thereby providing a new avenue for personalized treatment options.

The purpose of this study is to determine what variables contribute to the early death of elderly colorectal cancer patients (ECRC) and to generate predictive nomograms for this population.

This retrospective cohort analysis included elderly individuals (≥75 years old) diagnosed with colorectal cancer (CRC) from 2010-2015 in the Surveillance, Epidemiology, and End Result databases (SEER) databases. The external validation was conducted using a sample of the Chinese population obtained from the China-Japan Union Hospital of Jilin University. Logistic regression analyses were used to ascertain variables associated with early death and to develop nomograms. The nomograms were internally and externally validated with the help of the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

The SEER cohort consisted of 28,111 individuals, while the Chinese cohort contained 315 cases. Logistic regression analyses shown that race, marital status, tumor size, Grade, T stage, N stage, M stage, brain metastasis, liver metastasis, bone metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for all-cause and cancer-specific early death in ECRC patients; The variable of sex was only related to an increased risk of all-cause early death, whereas the factor of insurance status was solely associated with an increased risk of cancer-specific early death. Subsequently, two nomograms were devised to estimate the likelihood of all-cause and cancer-specific early death among individuals with ECRC. The nomograms exhibited robust predictive accuracy for predicting early death of ECRC patients, as evidenced by both internal and external validation.

We developed two easy-to-use nomograms to predicting the likelihood of early death in ECRC patients, which would contribute significantly to the improvement of clinical decision-making and the formulation of personalized treatment approaches for this particular population.

Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.