ADP-ribosyl transferases (ARTs) are a family of enzymes which catalyze the addition of a chain (PARylation) or a single moiety (MARylation) of ADP-ribose to their substrates. PARP7 is a mono-ADP-ribosyl transferase (mono-ART) which has recently gained attention due to its emerging role as a negative regulator of the type I interferon (IFN-I) and nuclear receptor signaling, and due to its aberrant expression in cancer, contributing to disease progression and immune evasion. PARP7-mediated ADP-ribosylation can differentially affect protein stability. On the one hand, PARP7-mediated ADP-ribosylation of the transcription factor FRA1 protects it from proteosomal degradation and thereby supports its function in negatively regulating IRF1 and the expression of apoptosis and immune signaling genes. On the other hand, PARP7-mediated ADP-ribosylation of aryl hydrocarbon receptor (AHR) and estrogen receptor (ER) marks them for proteosomal degradation. PARP7 also ADP-ribosylates the ligand-bound androgen receptor (AR), which is recognized by DTX3L-PARP9 that modulate the AR transcriptional activity. In this review, we discuss PARP7 enzymatic properties, biological functions and known substrates, its role in various cancers, and its targeting by specific inhibitors.

Within the tumor microenvironment (TME), the association of B lymphocytes (B cells) with prognosis and therapy response in gastric cancer (GC) remains poorly characterized. We investigated the predictive and prognostic value of B cells, including their spatial organization within the TME, in one of the largest multi-cohort studies to date.

Using CD20 immunohistochemistry, we evaluated B cell density in resection specimens from 977 patients with resectable GC across three cohorts, including the randomized phase III Korean CLASSIC trial. The relationship between CD20 density, clinicopathological characteristics, and overall survival (OS) was analyzed. Digital spatial profiling of 1063 regions of interest from 15 patients was performed to characterize B cell distribution within different regions of interest (ROIs) using the NanoString GeoMx platform.

CD20 density was significantly higher in diffuse-type GC compared to intestinal-type (p = 0.000012). Patients with CD20-low diffuse-type GC had the shortest OS in the CLASSIC trial (median OS: 49 vs 62 months, HR: 1.9, 95% CI: 1.2-3.0, p = 0.003) and in a Japanese cohort (median OS: 49 vs 67 months, HR: 2.2, 95% CI: 1.2-4.0, p = 0.011). This survival difference was not seen in patients treated with adjuvant chemotherapy (median OS: 62 vs 63 months, HR: 1.8, 95% CI: 0.88-3.5, p = 0.108). Spatial profiling revealed significant B cell enrichment within tumor ROIs compared to the stroma, particularly in diffuse-type GC.

Low CD20 positivity, especially in diffuse-type GC, is linked to poor prognosis and may identify patients who could benefit from chemotherapy. These findings underscore the role of B cells in GC.

Background/Objectives: Recent growth in the number and applications of high-throughput "omics" technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. Methods: Here we present the first algorithm, PLASMA, that can learn to predict time-to-event outcomes from multiomics data sets, even when some samples have only been assayed on a subset of the omics data sets. PLASMA uses two layers of existing partial least squares algorithms to first select components that covary with the outcome and then construct a joint Cox proportional hazards model. Results: We apply PLASMA to the stomach adenocarcinoma (STAD) data from The Cancer Genome Atlas. We validate the model both by splitting the STAD data into training and test sets and by applying them to the subset of esophageal cancer (ESCA) containing adenocarcinomas. We use the other half of the ESCA data, which contains squamous cell carcinomas dissimilar to STAD, as a negative comparison. Our model successfully separates both the STAD test set (p = 2.73 × 10-8) and the independent ESCA adenocarcinoma data (p = 0.025) into high-risk and low-risk patients. It does not separate the negative comparison data set (ESCA squamous cell carcinomas, p = 0.57). The performance of the unified multiomics model is superior to that of individually trained models and is also superior to an unsupervised method (Multi-Omics Factor Analysis; MOFA), which finds latent factors to be used as putative predictors in a post hoc survival analysis. Conclusions: Many of the factors that contribute strongly to the PLASMA model can be justified from the biological literature.

Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

Clustering is an important task in biomedical science, and it is widely believed that different data sets are best clustered using different algorithms. When choosing between clustering algorithms on the same data set, reseachers typically rely on global measures of quality, such as the mean silhouette width, and overlook the fine details of clustering. However, the silhouette width actually computes scores that describe how well each individual element is clustered. Inspired by this observation, we developed a novel clustering method, called SillyPutty. Unlike existing methods, SillyPutty uses the silhouette width for individual elements as a tool to optimize the mean silhouette width. This shift in perspective allows for a more granular evaluation of clustering quality, potentially addressing limitations in current methodologies. To test the SillyPutty algorithm, we first simulated a series of data sets using the Umpire R package and then used real-workd data from The Cancer Genome Atlas. Using these data sets, we compared SillyPutty to several existing algorithms using multiple metrics (Silhouette Width, Adjusted Rand Index, Entropy, Normalized Within-group Sum of Square errors, and Perfect Classification Count). Our findings revealed that SillyPutty is a valid standalone clustering method, comparable in accuracy to the best existing methods. We also found that the combination of hierarchical clustering followed by SillyPutty has the best overall performance in terms of both accuracy and speed. Availability: The SillyPutty R package can be downloaded from the Comprehensive R Archive Network (CRAN).

Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.

TUBB can encode a beta-tubulin protein. At present, the role of TUBB has not been ascertained in cancers. Hence, the importance of further systematic pan-cancer analyses is stressed to explore its value in the diagnosis, prognosis, and immune function of cancers.

By collecting and handling integrative data from the TCGA, Firehose, UCSC Xena, cBioPortal, GEO, CPTAC, TIMER2.0, TISCH, CellMiner, GDSC, and CTRP databases, we explored the potential diagnostic and prognostic roles of TUBB in pan-cancers from multiple angles. Moreover, the GSEA analysis was conducted to excavate the biological functions of TUBB in pan-cancers. In addition, survival profiles were described, and the differential expressions of TUBB in different molecular subtypes were discussed. Also, we utilized the cMAP function to search drugs or micro-molecules that have an impact on TUBB expressions.

Based on the TCGA data, we found that TUBB was differentially expressed in a variety of tumors and showed an early-diagnostic value. Mutations, somatic copy number alterations, and DNA methylation would lead to its abnormal expression. TUBB expressions had relations with many clinical features. What's more, TUBB expressions were validated to be related to many metabolism-related, metastasis-related, and immune-related pathways. High TUBB expressions were proved to have a great impact on the prognosis of various types of cancers and would affect the sensitivity of some drugs. We also demonstrated that the expression of TUBB was significantly correlated to immunoregulator molecules and biomarkers of lymphocyte subpopulation infiltration.

TUBB and its regulatory genes were systemically analyzed in this study, showing that TUBB had satisfying performances in disease diagnosing and prognosis predicting of multiple cancers. It could remodel the tumor microenvironment and play an integral role in guiding cancer therapies and forecasting responses to chemotherapy.

The intravesical instillation of the paclitaxel-hyaluronan conjugate ONCOFID-P-B™ in patients with bacillus Calmette-Guérin (BCG)-unresponsive bladder carcinoma in situ (CIS; NCT04798703 phase I study), induced 75 and 40% of complete response (CR) after 12 weeks of intensive phase and 12 months of maintenance phase, respectively. The aim of this study was to provide a detailed description of the tumor microenvironment (TME) of ONCOFID-P-B™-treated BCG-unresponsive bladder CIS patients enrolled in the NCT04798703 phase I study, in order to identify predictive biomarkers of response.

The composition and spatial interactions of tumor-infiltrating immune cells and the expression of the most relevant hyaluronic acid (HA) receptors on cancer cells, were analyzed in biopsies from the 20 patients enrolled in the NCT04798703 phase I study collected before starting ONCOFID-P-B™ therapy (baseline), and after the intensive and the maintenance phases. Clinical data were correlated with cell densities, cell distribution and cell interactions. Associations between immune populations or HA receptors expression and outcome were analyzed using univariate Cox regression and log-rank analysis.

In baseline biopsies, patients achieving CR after the intensive phase had a lower density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL), but also fewer interactions between CTL and macrophages or T-regulatory cells, as compared to non-responders (NR). NR expressed higher levels of the HA receptors CD44v6, ICAM-1 and RHAMM. The intra-tumoral macrophage density was positively correlated with the expression of the pro-metastatic and aggressive variant CD44v6, and the combined score of intra-tumoral macrophage density and CD44v6 expression had an AUC of 0.85 (95% CI 0.68-1.00) for patient response prediction.

The clinical response to ONCOFID-P-B™ in bladder CIS likely relies on several components of the TME, and the combined evaluation of intra-tumoral macrophages density and CD44v6 expression is a potentially new predictive biomarker for patient response. Overall, our data allow to advance a potential rationale for combinatorial treatments targeting the immune infiltrate such as immune checkpoint inhibitors, to make bladder CIS more responsive to ONCOFID-P-B™ treatment.

Colorectal cancer (CRC) imposes a significant healthcare burden globally, prompting the quest for innovative biomarkers to enhance diagnostic and therapeutic strategies. This study investigates the G-protein signaling modulator (GPSM) family across several cancers and presents a comprehensive pan-cancer analysis of the GPSM2 gene across several gastrointestinal (GI) cancers. Leveraging bioinformatics methodologies, we investigated GPSM2 expression patterns, protein interactions, functional enrichments, prognostic implications, genetic alterations, and immune infiltration associations. Furthermore, the expression of the GPSM2 gene was analyzed using real-time analysis. Our findings reveal a consistent upregulation of GPSM2 expression in all GI cancer datasets analyzed, suggesting its potential as a universal biomarker in GI cancers. Functional enrichment analysis underscores the involvement of GPSM2 in vital pathways, indicating its role in tumor progression. The prognostic assessment indicates that elevated GPSM2 expression correlates with adverse overall and disease-free survival outcomes across multiple GI cancer types. Genetic alteration analysis highlights the prevalence of mutations, particularly missense mutations, in GPSM2. Furthermore, significant correlations between GPSM2 expression and immune cell infiltration are observed, suggesting its involvement in tumor immune evasion mechanisms. Collectively, our study underscores the multifaceted role of GPSM2 in GI cancers, particularly in CRC, emphasizing its potential as a promising biomarker for prognosis and therapeutic targeting. Further functional investigations are warranted to elucidate its clinical utility and therapeutic implications in CRC management.

Endometrial cancer (EC) is the most prevalent gynecological cancer in high-income countries. Its incidence is skyrocketing due to the increase in risk factors such as obesity, which represents a true pandemic. This study aimed to evaluate microRNA (miRNA) expression in obesity-related EC to identify potential associations between this specific cancer type and obesity. miRNA levels were analyzed in 84 EC patients stratified based on body mass index (BMI; ≥30 or <30) and nine noncancer women with obesity. The data were further tested in The Cancer Genome Atlas (TCGA) cohort, including 384 EC patients, 235 with BMI ≥30 and 149 with BMI <30. Prediction of miRNA targets and analysis of their expression were also performed to identify the potential epigenetic networks involved in obesity modulation. In the EC cohort, BMI ≥30 was significantly associated with 11 deregulated miRNAs. The topmost deregulated miRNAs were first analyzed in 84 EC samples by single miRNA assay and then tested in the TCGA dataset. This independent validation provided further confirmation about the significant difference of three miRNAs (miR-199a-5p, miR-449a, miR-449b-5p) in normal-weight EC patients versus EC patients with obesity, resulting significantly higher expressed in the latter. Moreover, the three miRNAs were significantly correlated with grade, histological type, and overall survival. Analysis of their target genes revealed that these miRNAs may regulate obesity-related pathways. In conclusion, we identified specific miRNAs associated with BMI that are potentially involved in modulating obesity-related pathways and that may provide novel implications for the clinical management of obese EC patients.

Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the role of PTC in pathogenesis requires studying the various gene expressions to find out which particular molecular biomarkers will be helpful. The authors conducted a comprehensive search on the PubMed microarray database and a meta-analysis approach on the remaining ones to determine the differentially expressed genes between PTC and normal tissues, along with the analyses of overall survival (OS) and recurrence-free survival (RFS) rates in patients with PTC. We considered the associated genes with MAPK, Wnt, and Notch signaling pathways. Two GEO datasets have been included in this research, considering inclusion and exclusion criteria. Nineteen genes were found to have higher differences through the meta-analysis procedure. Among them, ten genes were upregulated, and nine genes were downregulated. The expression of 19 genes was examined using the GEPIA2 database, and the Kaplan-Meier plot statistics were used to analyze RFS and the OS rates. We discovered seven significant genes with the validation: PRICKLE1, KIT, RPS6KA5, GADD45B, FGFR2, FGF7, and DTX4. To further explain these findings, it was discovered that the mRNA expression levels of these seven genes and the remaining 12 genes were shown to be substantially linked with the results of the experimental literature investigations on the PTC. Our research found nineteen panels of genes that could be involved in the PTC progression and metastasis and the immune system infiltration of these cancers.

Prior to the next generation sequencing and characterization of the tumor genome landscape, mutations in the SWI/SNF chromatin remodeling complex and the KEAP1-NRF2 signaling pathway were underappreciated. While these two classes of mutations appeared to independently contribute to tumor development, recent reports have demonstrated a mechanistic link between these two regulatory mechanisms in specific cancer types and cell models. In this work, we expand upon these data by exploring the relationship between mutations in BAF and PBAF subunits of the SWI/SNF complex and activation of NRF2 signal transduction across many cancer types. ARID1A/B mutations were strongly associated with NRF2 transcriptional activity in head and neck squamous carcinomas (HNSC). Many additional tumor types showed significant association between NRF2 signaling and mutation of specific components of the SWI/SNF complex. Different effects of BAF and PBAF mutations on the polarity of NRF2 signaling were observed. Overall, our results support a context-dependent functional link between SWI/SNF and NRF2 mutations across human cancers and implicate ARID1A inactivation in HPV-negative HNSC in promoting tumor progression and survival through activation of the KEAP1-NRF2 signaling pathway. The tumor-specific effects of these mutations open a new area of study for how mutations in the KEAP1-NRF2 pathway and the SWI/SNF complex contribute to cancer.

The tumor microenvironment is characterized by inflammation-like and immunosuppression situations. Although cancer-associated fibroblasts (CAFs) are among the major stromal cell types in various solid cancers, including colon cancer, the interactions between CAFs and immune cells remains largely uncharacterized. Pentraxin 3 (PTX3) is responsive to proinflammatory cytokines and modulates immunity and tissue remodeling, but its involvement in tumor progression appears to be context-dependent and is unclear.

Open-access databases were utilized to examine the association of PTX3 expression and the fibroblast signature in colon cancer. Loss-of-function assays, including studies in tamoxifen-induced Ptx3 knockout mice and treatment with an anti-PTX3 neutralizing antibody (WHC-001), were conducted to assess the involvement of PTX3 in colon cancer progression as well as its immunosuppressive effect. Finally, bioinformatic analyses and in vitro assays were performed to reveal the downstream effectors and decipher the involvement of the CREB1/CEBPB axis in response to PTX3 and PTX3-induced promotion of M2 macrophage polarization.

Clinically, higher PTX3 expression was positively correlated with fibroblasts and inflammatory response signatures and associated with a poor survival outcome in colon cancer patients. Blockade of PTX3 significantly reduced stromal cell-mediated tumor development. The decrease of the M2 macrophage population and an increase of the cytotoxic CD8+ T-cell population were observed following PTX3 inactivation in allografted colon tumors. We further revealed that activation of cyclic AMP-responsive element-binding protein 1 (CREB1) mediated the PTX3-induced promotion of M2 macrophage polarization.

PTX3 contributes to stromal cell-mediated protumor immunity by increasing M2-like macrophage polarization, and inhibition of PTX3 with WHC-001 is a potential therapeutic strategy for colon cancer.

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality.

Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive.

The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation.

The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).

Human papillomavirus (HPV) is a major causative factor of head and neck squamous cell carcinoma (HNSCC), and the incidence of HPV- associated HNSCC is increasing. The role of tumor microenvironment in viral infection and metastasis needs to be explored further. We studied the molecular characteristics of primary tumors (PTs) and lymph node metastatic tumors (LNMTs) by stratifying them based on their HPV status. Eight samples for single-cell RNA profiling and six samples for spatial transcriptomics (ST), composed of matched primary tumors (PT) and lymph node metastases (LNMT), were collected from both HPV- negative (HPV- ) and HPV-positive (HPV+ ) patients. Using the 10x Genomics Visium platform, integrative analyses with single-cell RNA sequencing were performed. Intracellular and intercellular alterations were analyzed, and the findings were confirmed using experimental validation and publicly available data set. The HPV+ tissues were composed of a substantial amount of lymphoid cells regardless of the presence or absence of metastasis, whereas the HPV- tissue exhibited remarkable changes in the number of macrophages and plasma cells, particularly in the LNMT. From both single-cell RNA and ST data set, we discovered a central gene, pyruvate kinase muscle isoform 1/2 (PKM2), which is closely associated with the stemness of cancer stem cell-like populations in LNMT of HPV- tissue. The consistent expression was observed in HPV- HNSCC cell line and the knockdown of PKM2 weakened spheroid formation ability. Furthermore, we found an ectopic lymphoid structure morphology and clinical effects of the structure in ST slide of the HPV+ patients and verified their presence in tumor tissue using immunohistochemistry. Finally, the ephrin-A (EPHA2) pathway was detected as important signals in angiogenesis for HPV- patients from single-cell RNA and ST profiles, and knockdown of EPHA2 declined the cell migration. Our study described the distinct cellular composition and molecular alterations in primary and metastatic sites in HNSCC patients based on their HPV status. These results provide insights into HNSCC biology in the context of HPV infection and its potential clinical implications.

Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomics feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomics features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomics features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomics feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the outcome of the association analysis has revealed potential gene mutations and radiomics feature candidates that warrant further investigation in future research endeavors.

Diffuse gliomas are tumors that arise from glial or glial progenitor cells. They are currently classified as astrocytoma isocitrate dehydrogenase (IDH)-mutant or oligodendroglioma IDH-mutant, and 1p/19q-codeleted, both slower-growing tumors, or glioblastoma (GBM), a more aggressive tumor. Despite advances in the diagnosis and treatment of gliomas, the median survival time after diagnosis of GBM remains low, approximately 15 months, with a 5-year overall survival rate of only 6.8%. Therefore, new biomarkers that could support the earlier diagnosis and prognosis of these tumors would be of great value. MUC17, a membrane-bound mucin, has been identified as a potential biomarker for several tumors. However, the role of this mucin in adult gliomas has not yet been explored. Here, we show for the first time, in a retrospective study and by in silico analysis that MUC17 is one of the relevant mutant genes in adult gliomas. Moreover, that an increase in MUC17 methylation correlates with an increase in glioma malignancy grade. Patients with MUC17 mutations had a poorer prognosis than their wild-type counterparts in both GBM and non-GBM glioma cohorts. We also analyzed mutational profiles that correlated strongly with poor survival. Therefore, in this study, we present a new potential biomarker for further investigation, especially for the prognosis of adult diffuse gliomas.

We analyzed transcriptional data from 104 HPV+ (Human papillomavirus) HNSCC (head and neck squamous cell carcinoma) tumors together with two publicly available sources to identify highly robust transcriptional programs (modules) which could be detected consistently despite heterogeneous sequencing and quantification methodologies. Among 22 modules identified, we found a single module that naturally subclassifies HPV+ HNSCC tumors based on a bimodal pattern of gene expression, clusters all atypical features of HPV+ HNSCC biology into a single subclass, and predicts patient outcome in four independent cohorts. The subclass-defining gene set was strongly correlated with Nuclear factor kappa B (NF-κB) target expression. Tumors with high expression of this NF-κB module were rarely associated with activating PIK3CA alterations or viral integration, and also expressed higher levels of HPHPV E2 and had decreased APOBEC mutagenesis. Alternatively, they harbored inactivating alterations of key regulators of NF-κB, TNF receptor associated factor 3 (TRAF3), and cylindromatosis (CYLD), as well as retinoblastoma protein (RB1). HPV+ HNSCC cells in culture with experimental depletion of TRAF3 or CYLD displayed increased expression of the subclass-defining genes, as well as robust radio-sensitization, thus recapitulating both the tumor transcriptional state and improved treatment response observed in patient data. Across all gene sets investigated, methylation to expression correlations were the strongest for the subclass-defining, NF-κB-related genes. Increased tumor-infiltrating CD4+ T cells and increased Estrogen receptors alpha (ERα) expression were identified in NF-κB active tumors. Based on the relatively high rates of cure in HPV+ HNSCC, deintensification of therapy to reduce treatment-related morbidity is being studied at many institutions. Tumor subclassification based on oncogenic subtypes may help guide the selection of therapeutic intensity or modality for patients with HPV+ HNSCC.

In the last decade, there has been a boost in autophagy reports due to its role in cancer progression and its association with tumor resistance to treatment. Despite this, many questions remain to be elucidated and explored among the different tumors. Here, we used omics-based cancer datasets to identify autophagy genes as prognostic markers in cancer. We then combined these findings with independent studies to further characterize the clinical significance of these genes in cancer. Our observations highlight the importance of innovative approaches to analyze tumor heterogeneity, potentially affecting the expression of autophagy-related genes with either pro-tumoral or anti-tumoral functions. In silico analysis allowed for identifying three genes (TBC1D12, KERA, and TUBA3D) not previously described as associated with autophagy pathways in cancer. While autophagy-related genes were rarely mutated across human cancers, the expression profiles of these genes allowed the clustering of different cancers into three independent groups. We have also analyzed datasets highlighting the effects of drugs or regulatory RNAs on autophagy. Altogether, these data provide a comprehensive list of targets to further the understanding of autophagy mechanisms in cancer and investigate possible therapeutic targets.

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

Background: Breast cancer is the foremost cancer in worldwide incidence, surpassing lung cancer notwithstanding the gender bias. One in four cancer cases among women are attributable to cancers of the breast, which are also the leading cause of death in women. Reliable options for early detection of breast cancer are needed. Methods: Using public-domain datasets, we screened transcriptomic profiles of breast cancer samples, and identified progression-significant linear and ordinal model genes using stage-informed models. We then applied a sequence of machine learning techniques, namely, feature selection, principal components analysis, and k-means clustering, to train a learner to discriminate "cancer" from "normal" based on expression levels of identified biomarkers. Results: Our computational pipeline yielded an optimal set of nine biomarker features for training the learner, namely, NEK2, PKMYT1, MMP11, CPA1, COL10A1, HSD17B13, CA4, MYOC, and LYVE1. Validation of the learned model on an independent test dataset yielded a performance of 99.5% accuracy. Blind validation on an out-of-domain external dataset yielded a balanced accuracy of 95.5%, demonstrating that the model has effectively reduced the dimensionality of the problem, and learnt the solution. The model was rebuilt using the full dataset, and then deployed as a web app for non-profit purposes at: https://apalania.shinyapps.io/brcadx/. To our knowledge, this is the best-performing freely available tool for the high-confidence diagnosis of breast cancer, and represents a promising aid to medical diagnosis.

Zwilch kinetochore protein (ZWILCH) plays a key role in proper cell proliferation. The upregulation of the ZWILCH gene was observed in many types of cancers, but the association of ZWILCH with adrenocortical carcinoma (ACC) was not investigated so far. The main aim of the presented study was to verify if the enhanced level of the ZWILCH gene can be used as a diagnostic marker for ACC development and progression, as well as a predictor of survival time for ACC patients. The performed analyses included investigation of the ZWILCH expression profile in tumors with publicly available TCGA (The Cancer Genome Atlas) datasets and transcriptomic data from the Gene Expression Omnibus (GEO) database, as well as, in human biological samples of normal adrenal, adrenocortical carcinoma and in commercially available tissue microarrays. The findings demonstrate statistically significant higher ZWILCH gene expression in ACC tissue in comparison with normal adrenal glands. Furthermore, there is a strong correlation between ZWILCH upregulation and tumor mitotic rate and the probability of patient survival. The enhanced ZWILCH level is also connected with the activation of genes involved in cell proliferation and the inhibition of genes related to the immune system. This work contributes to a better understanding of the role of ZWILCH as an ACC biomarker and diagnostic tool.

Somatic cell reprogramming using the microRNAs miR-200c, miR-302s, and miR-369s leads to increased expression of cyclin-dependent kinase inhibitors in human colorectal cancer (CRC) cells and suppressed tumor growth. Here, we investigated whether these microRNAs inhibit colorectal tumorigenesis in CPC;Apc mice, which are prone to colon and rectal polyps. Repeated administration of microRNAs inhibited polyp formation. Microarray analysis indicated that c-MAF, which reportedly shows oncogene-like behavior in multiple myeloma and T cell lymphoma, decreased in tumor samples but increased in microRNA-treated normal mucosa. Immunohistochemistry identified downregulation of c-MAF as an early tumorigenesis event in CRC, with low c-MAF expression associated with poor prognosis. Of note, c-MAF expression and p53 protein levels were inversely correlated in CRC samples. c-MAF knockout led to enhanced tumor formation in azoxymethane/dextran sodium sulfate-treated mice, with activation of cancer-promoting genes. c-MAF may play a tumor-suppressive role in CRC development.

Clinically relevant immunological biomarkers that discriminate between diverse hypofunctional states of tumor-associated CD8⁺ T cells remain disputed. Using multiomics analysis of CD8⁺ T cell features across multiple patient cohorts and tumor types, we identified tumor niche-dependent exhausted and other types of hypofunctional CD8⁺ T cell states. CD8⁺ T cells in "supportive" niches, like melanoma or lung cancer, exhibited features of tumor reactivity-driven exhaustion (CD8⁺ T_EX). These included a proficient effector memory phenotype, an expanded T cell receptor (TCR) repertoire linked to effector exhaustion signaling, and a cancer-relevant T cell-activating immunopeptidome composed of largely shared cancer antigens or neoantigens. In contrast, "nonsupportive" niches, like glioblastoma, were enriched for features of hypofunctionality distinct from canonical exhaustion. This included immature or insufficiently activated T cell states, high wound healing signatures, nonexpanded TCR repertoires linked to anti-inflammatory signaling, high T cell-recognizable self-epitopes, and an antiproliferative state linked to stress or prodeath responses. In situ spatial mapping of glioblastoma highlighted the prevalence of dysfunctional CD4⁺:CD8⁺ T cell interactions, whereas ex vivo single-cell secretome mapping of glioblastoma CD8⁺ T cells confirmed negligible effector functionality and a promyeloid, wound healing-like chemokine profile. Within immuno-oncology clinical trials, anti-programmed cell death protein 1 (PD-1) immunotherapy facilitated glioblastoma's tolerogenic disparities, whereas dendritic cell (DC) vaccines partly corrected them. Accordingly, recipients of a DC vaccine for glioblastoma had high effector memory CD8⁺ T cells and evidence of antigen-specific immunity. Collectively, we provide an atlas for assessing different CD8⁺ T cell hypofunctional states in immunogenic versus nonimmunogenic cancers.

FBXW7 is a member of the F-box protein family, which functions as the substrate recognition component of the SCF E3 ubiquitin ligase. FBXW7 is a main tumor suppressor due to its ability to control proteasome-mediated degradation of several oncoproteins such as c-Jun, c-Myc, Cyclin E1, mTOR, and Notch1-IC. FBXW7 inactivation in human cancers results from a somatic mutation or downregulation of its protein levels. This work describes a novel regulatory mechanism for FBXW7 dependent on the serine/threonine protein kinase DYRK2. We show that DYRK2 interacts with and phosphorylates FBXW7 resulting in its proteasome-mediated degradation. DYRK2-dependent FBXW7 destabilization is independent of its ubiquitin ligase activity. The functional analysis demonstrates the existence of DYRK2-dependent regulatory mechanisms for key FBXW7 substrates. Finally, we provide evidence indicating that DYRK2-dependent regulation of FBXW7 protein accumulation contributes to cytotoxic effects in response to chemotherapy agents such as Doxorubicin or Paclitaxel in colorectal cancer cell lines and to BET inhibitors in T-cell acute lymphoblastic leukemia cell lines. Altogether, this work reveals a new regulatory axis, DYRK2/FBXW7, which provides an understanding of the role of these two proteins in tumor progression and DNA damage responses.

Since it was first described >20 yr ago, the SPOC domain (Spen paralog and ortholog C-terminal domain) has been identified in many proteins all across eukaryotic species. SPOC-containing proteins regulate gene expression on various levels ranging from transcription to RNA processing, modification, export, and stability, as well as X-chromosome inactivation. Their manifold roles in controlling transcriptional output implicate them in a plethora of developmental processes, and their misregulation is often associated with cancer. Here, we provide an overview of the biophysical properties of the SPOC domain and its interaction with phosphorylated binding partners, the phylogenetic origin of SPOC domain proteins, the diverse functions of mammalian SPOC proteins and their homologs, the mechanisms by which they regulate differentiation and development, and their roles in cancer.

We evaluated the relevance of PD-1⁺CD8⁺ T-cells in gastric cancer (GC) including prognostic significance, association with chemotherapy and immunotherapy sensitivity and correlations with the tumor microenvironment (TME).

Discovery cohort: GC samples were evaluated for AE1/3, CD8, PD-1, Ki-67 and Granzyme-B expression with fluorescence-based multiplex immunohistochemistry (mIHC). Validation cohorts: we analyzed bulk RNAseq GC datasets from TCGA, the "3G" chemotherapy trial and an immunotherapy phase 2 trial. The cox proportional hazards model was used to identify factors that influenced overall survival (OS). To study the TME, we analyzed single-cell RNAseq performed on GCs.

In the discovery cohort of 350 GCs, increased PD-1 expression of CD8 T-cells was prognostic for OS (HR 0.822, p = 0.042). PD-1 expression in CD8 T-cells highly correlated with cytolytic [Granzyme-B⁺] (r = 0.714, p < 0.001) and proliferative [Ki-67⁺] (r = 0.798, p < 0.001) activity. Analysis of bulk RNAseq datasets showed tumors with high PD-1 and CD8A expression levels had improved OS when treated with immunotherapy (HR 0.117, p = 0.036) and chemotherapy (HR 0.475, p = 0.017). Analysis of an scRNAseq dataset of 152,423 cells from 40 GCs revealed that T-cell and NK-cell proportions were higher (24% vs 18% and 19% vs 15%, p < 0.0001), while macrophage proportions were lower (7% vs 11%, p < 0.0001) in CD8PD-1_high compared to CD8PD-1_low tumors.

This is one of the largest GC cohorts of mIHC combined with analysis of multiple datasets providing orthogonal validation of the clinical relevance of PD-1⁺CD8⁺ T-cells being associated with improved OS. CD8PD-1_high tumors have distinct features of an immunologically active, T-cell inflamed TME.

Colorectal cancer (CRC) is a deadly and commonly diagnosed cancer. Cell-free circulating tumor DNAs (ctDNA) have been used in the diagnosis and treatment of CRC, but there are open questions about the relationship between ctDNAs and CRC. Although mutations of genes detected by ctDNA in CRC have been studied, the quantitative relationship between ctDNA mutations and ctDNA concentration has not been addressed.

We hypothesized that there was an association between mutations of genes identified in ctDNAs and ctDNA concentration. His study examined this association in a population of CRC patients.

In 85 CRC patients, we sampled 282 mutations in 36 genes and conducted an association study based on a Random forest model between mutations and ctDNA concentrations in all patients.

This association study showed that mutations on five genes, ALK, PMS2, KDR, MAP2K1, and MSH2, were associated with the ctDNA concentrations in CRC patients' blood samples. Because ctDNA mutations correlate with ctDNA level, we can infer the tumor burden or tumor size from ctDNA mutations, as well as the survival time for prognosis.

Our findings shed light on the associations between mutations of genes identified in ctDNAs and ctDNA concentration in the blood of CRC patients. This discovery provides information regarding the tumor burden or tumor size based on ctDNA mutations.

A dysregulation of cholesterol homeostasis is often seen in various cancer cell types, and elevated cholesterol content and that of its metabolites appears to be crucial for cancer progression and metastasis. Cholesterol is a precursor of various steroid hormones and a key plasma membrane component especially in lipid-rafts, also modulating many intracellular signaling pathways.

To provide an insight of dysregulated cholesterol regulatory genes, their transcript levels were analyzed in different cancers and their influence was correlated with the overall survival of cancer patients using cancer database analysis.

This analysis found a set of genes (e.g., ACAT1, RXRA, SOAT1 and SQLE) that were not only often dysregulated, but also had been associated with poorer overall survival in most cancer types. Quantitative reverse transcriptase-polymerase chain reaction analysis revealed elevated SQLE and SOAT1 transcript levels and downregulated expression of RXRA and ACAT1 genes in triple negative breast cancer tissues compared to adjacent control tissues, indicating that this dysregulated expression of the gene signature is a diagnostic marker for breast cancer.

For the first time, the present study identified a gene signature associated with the dysregulation of cholesterol homeostasis in cancer cells that may not only be used as a diagnostic marker, but also comprise a promising drug target for the advancement of cancer therapy.

Pathologic complete response (pCR) to neoadjuvant chemoradiation for locally advanced esophageal adenocarcinoma (EAC) confers significantly improved survival. The ability to infer pCR may spare esophagectomy in some patients. Currently, there are no validated biomarkers of pCR. This study sought to evaluate whether a distinct signature of DNA copy number alterations (CNA) can be predictive of pCR in EAC. Pretreatment biopsies from 38 patients with locally advanced EAC (19 with pCR and 19 with pathologic partial/poor response) were assessed for CNA using OncoScan assay. A novel technique was employed where within every cytogenetic band, the quantity of bases gained by each sample was computed as the sum of gained genomic segment lengths weighted by the surplus copy number of each segment. A threefold cross-validation was used to assess association with pCR or pathologic partial/poor response. Forty patients with locally advanced EAC from The Cancer Genome Atlas (TCGA) constituted an independent validation cohort. Gains in the chromosomal loci 14q11 and 17p11 were preferentially associated with pCR. Average area under the receiver operating characteristic curve (AUC) for predicting pCR was 0.80 among the threefold cross-validation test sets. Using 0.3 megabases as the cutoff that optimizes trade-off between sensitivity (63%) and specificity (89%) in the discovery cohort, similar prediction performance for clinical and radiographic response was demonstrated in the validation cohort from TCGA (sensitivity 61%, specificity 82%). Copy number gains in the 14q11 and 17p11 loci may be useful for prediction of pCR, and, potentially, personalization of esophagectomy in EAC.

Immune checkpoint blockade (ICB) therapies induce durable responses in approximately 15% of colorectal cancer (CRC) patients who exhibit microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR). However, more than 80% of CRC patients do not respond to current immunotherapy. The main challenge with these patients is lack of MHC-I signaling to unmask their cancer cells so the immune cells can detect them. Here, we started by comparing IFNγ signature genes and MHC-I correlated gene lists to determine the potential candidates for MHC-I regulators. Then, the protein expression level of listed potential candidates in normal and cancer tissue was compared to select final candidates with enough disparity between the two types of tissues. ISG15 and DDX60 were further tested by wet-lab experiments. Overexpression of DDX60 upregulated the expression of MHC-I, while knockdown of DDX60 reduced the MHC-I expression in CRC cells. Moreover, DDX60 was downregulated in CRC tissues, and lower levels of DDX60 were associated with a poor prognosis. Our data showed that DDX60 could regulate MHC-I expression in CRC; thus, targeting DDX60 may improve the effects of immunotherapy in some patients.

Thyroid cancer (THCA) is the most prevalent malignant disease of the endocrine system, in which 5-year survival can attain about 95%, but patients with metastasis have a poor prognosis. Very little is known about the role of CAPN8 in the metastasis of THCA. In particular, the effect of CAPN8 on the tumor immune microenvironment (TIME) and immunotherapy response is unclear.

Multiome datasets and multiple cohorts were acquired for analysis. Firstly, the expression and the prognostic value of CAPN8 were explored in public datasets and in vitro tumor tissues. Then, hierarchical clustering analysis was performed to identify the immune subtypes of THCA according to the expression of CAPN8 and the activities of related pathways. Subsequent analyses explored the different patterns of TIME, genetic alteration, DNA replication stress, drug sensitivity, and immunotherapy response among the three immune phenotypes. Finally, five individual cohorts of thyroid cancer were utilized to test the robustness and extrapolation of the three immune clusters.

CAPN8 was found to be a significant risk factor for THCA with a markedly elevated level of mRNA and protein in tumor tissues. This potential oncogene could induce the activation of epithelial-mesenchymal transition and E2F-targeted pathways. Three subtypes were identified for THCA, including immune exhausted, inflamed, and immune desert phenotypes. The exhausted type was characterized by a markedly increased expression of inhibitory receptors and infiltration of immune cells but was much more likely to respond to immunotherapy. The immune desert type was resistant to common chemotherapeutics with extensive genomic mutation and copy number variance.

The present study firstly explored the role of CAPN8 in the metastasis of THCA from the aspects of TIME. Three immune subtypes were identified with quite different patterns of prognosis, immunotherapy response, and drug sensitivity, providing novel insights for the treatment of THCA and helping understand the cross-talk between CAPN8 and tumor immune microenvironment.

The tumor suppressor p53 oscillates in response to DNA double-strand breaks, a behavior that has been suggested to be essential to its anti-cancer function. Nearly all human cancers have genetic alterations in the p53 pathway; a number of these alterations have been shown to be oncogenic by experiment. These alterations include somatic mutations and copy number variations as well as germline polymorphisms. Intriguingly, they exhibit a mixed pattern of interactions in tumors, such as co-occurrence, mutual exclusivity, and paradoxically, mutual antagonism. Using a differential equation model of p53-Mdm2 dynamics, we employ Hopf bifurcation analysis to show that these alterations have a common mode of action, to abolish the oscillatory competence of p53, thereby, we suggest, impairing its tumor suppressive function. In this analysis, diverse genetic alterations, widely associated with human cancers clinically, have a unified mechanistic explanation of their role in oncogenesis.

XBP1 variant 1 (Xv1) is the most abundant XBP1 variant and is highly enriched across cancer types but nearly none in normal tissues. Its expression is associated with poor patients' survival and is specifically required for survival of malignant cells, but the underlying mechanism is not known. Here we report that Xv1 upregulates the polyglutamylase tubulin tyrosine ligase-like 6 (TTLL6) and promotes mitosis of cancer cells. Like the canonical XBP1, Xv1 mRNA undergoes unconventional splicing by IRE1α under endoplasmic reticulum stress, but it is also constitutively spliced by IRE1β. The spliced Xv1 mRNA encodes the active form of Xv1 protein (Xv1s). RNA sequencing in HeLa cells revealed that Xv1s overexpression regulates expression of genes that are not involved in the canonical unfolded protein response, including TTLL6 as a highly upregulated gene. Gel shift assay and chromatin immunoprecipitation revealed that Xv1s bind to the TTLL6 promoter region. Knockdown of TTLL6 caused death of cancer cells but not benign and normal cells, similar to the effects of knocking down Xv1. Moreover, overexpression of TTLL6 partially rescued BT474 cells from apoptosis induced by either TTLL6 or Xv1 knockdown, supporting TTLL6 as an essential downstream effector of Xv1 in regulating cancer cell survival. TTLL6 is localized in the mitotic spindle of cancer cells. Xv1 or TTLL6 knockdown resulted in decreased spindle polyglutamylation and interpolar spindle, as well as congression failure, mitotic arrest and cell death. These findings suggest that Xv1 is essential for cancer cell mitosis, which is mediated, at least in part, by increasing TTLL6 expression.