• Female
• Humans
• Biomarkers, Tumor/genetics
• DNA Methylation/genetics
• Genital Neoplasms, Female/genetics
• Genital Neoplasms, Female/mortality
• Genital Neoplasms, Female/pathology
• Genital Neoplasms, Female/therapy
• Genomics/methods
• Homologous Recombination/genetics
• Multiomics/methods
• Ovarian Neoplasms/genetics
• Ovarian Neoplasms/mortality
• Ovarian Neoplasms/pathology
• Ovarian Neoplasms/therapy
• Phenotype
• Risk Assessment/methods
• Autoencoder

• Ferroptosis
• Immune checkpoints
• Long non-coding RNAs
• Lower-grade glioma
• Risk score

• 2021Y2014 Fujian Province Gastrointestinal, Respiratory, Genitourinary Malignant Tumor Radiotherapy Radiation and Treatment Clinical Medical Research Center
• 2020Y20101 Fujian Provincial Clinical Medical Research Center for Tumor Precision Radiotherapy
• 2021Y9216 Fujian Province Science and Technology Innovation Joint Funding Project
• 2021J01438 and 2022J01433 Fujian Province Natural Science Foundation
• 2021YN10 Fujian Cancer Hospital In-Hospital Funding Program
• 2020Y2012 Fujian Provincial Clinical Research Center for Cancer Radiotherapy and Immunotherapy
• 2020QNA013 Fujian provincial health and family planning research talent training program

• DNA methylation
• Hi-C
• breast cancer
• enhancer
• lncRNA
• promoter

• Humans
• RNA, Long Noncoding/genetics
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• DNA Methylation
• Promoter Regions, Genetic
• Female
• Enhancer Elements, Genetic
• CpG Islands/genetics
• Chromatin/genetics
• Chromatin/metabolism
• Gene Expression Regulation, Neoplastic
• Prognosis
• Biomarkers, Tumor/genetics

• 62361047 National Natural Science Foundation of China
• 32160216 National Natural Science Foundation of China

• Genomic instability
• Lung adenocarcinoma
• Overall survival
• lncRNA

• Humans
• RNA, Long Noncoding/genetics
• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/mortality
• Adenocarcinoma of Lung/pathology
• Biomarkers, Tumor/genetics
• Prognosis
• Lung Neoplasms/genetics
• Lung Neoplasms/mortality
• Lung Neoplasms/pathology
• Genomic Instability
• Gene Expression Regulation, Neoplastic
• Male
• Female
• Gene Expression Profiling
• Middle Aged

• A2023267 Medical Science and Technology Foundation of Guangdong Province

• cervical cancer
• ion channel
• lncRNA
• prognostic model
• tumor microenvironment

• Humans
• RNA, Long Noncoding/genetics
• Uterine Cervical Neoplasms/genetics
• Uterine Cervical Neoplasms/pathology
• Uterine Cervical Neoplasms/mortality
• Female
• Prognosis
• Ion Channels/genetics
• Ion Channels/metabolism
• Gene Expression Regulation, Neoplastic
• Tumor Microenvironment/genetics
• Biomarkers, Tumor/genetics
• Gene Expression Profiling
• Middle Aged
• Transcriptome

• 2019ZD033 Tianjin Municipal Education Commission of China

• Diagnosis
• Genomic instability
• Hepatocellular carcinoma
• Long noncoding RNA
• Prognosis

• LncRNAs
• Pan-renal cell carcinoma
• genomic instability
• immunology
• renal cell carcinoma
• somatic mutation.

• Genomic Instability
• Humans
• Databases, Genetic
• RNA, Untranslated/genetics
• Carcinoma, Renal Cell/genetics
• Carcinoma, Renal Cell/therapy
• Kidney Neoplasms/genetics
• Kidney Neoplasms/pathology
• Kidney Neoplasms/therapy
• Genome, Human
• Mutation
• Prognosis
• Gene Expression Regulation, Neoplastic
• Neoplastic Stem Cells

• Derived signature
• Genomic instability
• Non-small cell lung cancer
• Prognosis

• Humans
• Carcinoma, Non-Small-Cell Lung/genetics
• Prognosis
• Lung Neoplasms/genetics
• Computational Biology
• Genomic Instability
• Membrane Proteins
• Protein-Tyrosine Kinases
• Protein Serine-Threonine Kinases

• Biomarker
• Breast cancer
• Enhancer RNA
• Genomic instability

• Humans
• Prognosis
• Oncogenes
• Carcinogenesis/genetics
• Cell Transformation, Neoplastic
• Genomic Instability/genetics

• Cancer biomarker
• Genomic instability
• Glioma
• Long non-coding RNAs
• Prognostic model

• Humans
• RNA, Long Noncoding/genetics
• Glioblastoma
• Prognosis
• Genomic Instability/genetics
• Mutation
• Glioma/genetics

• Breast neoplasms
• Long noncoding RNA
• Prognostic factors
• Tumor biomarkers

• Female
• Humans
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Prognosis
• Signal Transduction
• Biomarkers
• Gene Expression Regulation, Neoplastic/genetics
• Biomarkers, Tumor/genetics

• genomic instability
• hepatocellular carcinoma
• immune environment
• lncRNAs
• prognosis

• immunotherapy
• lung adenocarcinoma
• oxidative stress
• prognostic risk model
• redox
• tumor microenvironment

• cuproptosis
• head and neck squamous cell carcinoma
• immunotherapy
• long non-coding RNA
• prognosis

• Discovery
• Epigenetic
• Exosome
• Intergenic
• LINC02892
• Long non-coding RNA

• 991511 Mashhad University of Medical Sciences

• RNA in situ hybridization
• Stomach cancer
• advanced gastric cancer
• lnc-ATMIN-4:2
• long non-coding RNA
• prognosis
• whole transcriptome sequencing

Cancer immune function and tumor microenvironment are governed by long noncoding RNAs (lncRNAs). Nevertheless, it has yet to be established whether lncRNAs play a role in tumor-associated neutrophils (TANs). Here, a computing framework based on machine learning was used to identify neutrophil-specific lncRNA with prognostic significance in squamous cell carcinoma and lung adenocarcinoma using univariate Cox regression to comprehensively analyze immune, lncRNA, and clinical characteristics. The risk score was determined using LASSO Cox regression analysis. Meanwhile, we named this risk score as “TANlncSig.” TANlncSig was able to distinguish between better and worse survival outcomes in various patient datasets independently of other clinical variables. Functional assessment of TANlncSig showed it is a marker of myeloid cell infiltration into tumor infiltration and myeloid cells directly or indirectly inhibit the anti-tumor immune response by secreting cytokines, expressing immunosuppressive receptors, and altering metabolic processes. Our findings highlighted the value of TANlncSig in TME as a marker of immune cell infiltration and showed the values of lncRNAs as indicators of immunotherapy.

• TCGA
• bioinformatics
• cell renal cell carcinoma
• long non-coding RNA
• prognosis

• National Natural Science Foundation of China

• Humans
• Prognosis
• Microsatellite Instability
• Colonic Neoplasms/pathology
• Adenocarcinoma/pathology
• Immune Checkpoint Inhibitors
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism

Recent discoveries have revealed that fibronectin type III domain containing 3B (FNDC3B) acts as an oncogene in various cancers; however, its role in glioma remains unclear.

In this study, we comprehensively investigated the expression, prognostic value, and immune significance of FNDC3B in glioma using several databases and a variety of machine learning algorithms. RNA expression data and clinical information of 529 patients from the Cancer Genome Atlas (TCGA) and 1319 patients from Chinese Glioma Genome Atlas (CGGA) databases were downloaded for further investigation. To evaluate whether FNDC3B expression can predict clinical prognosis of glioma, we constructed a clinical nomogram to estimate long-term survival probabilities. The predicted nomogram was validated by CGGA cohorts. Differentially expressed genes (DEGs) were detected by the Wilcoxon test based on the TCGA-LGG dataset and the weighted gene co-expression network analysis (WGCNA) was implemented to identify the significant module associated with the expression level of FNDC3B. Furthermore, we investigated the correlation between FNDC3B with cancer immune infiltrates using TISIDB, ESTIMATE, and CIBERSORTx.

Higher FNDC3B expression displayed a remarkably worse overall survival and the expression level of FNDC3B was an independent prognostic indicator for patients with glioma. Based on TCGA LGG dataset, a co-expression network was established and the hub genes were identified. FNDC3B expression was positively correlated to the tumor-infiltrating lymphocytes and immune infiltration score, and high FNDC3B expression was accompanied by the increased expression of B7-H3, PD-L1, TIM-3, PD-1, and CTLA-4. Moreover, expression of FNDC3B was significantly associated with infiltrating levels of several types of immune cells and most of their gene markers in glioma.

This study demonstrated that FNDC3B may be involved in the occurrence and development of glioma and can be regarded as a promising prognostic and immunotherapeutic biomarker for the treatment of glioma.

• Humans
• Prognosis
• B7-H1 Antigen
• Hepatitis A Virus Cellular Receptor 2
• CTLA-4 Antigen
• Brain Neoplasms
• Programmed Cell Death 1 Receptor
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Glioma
• Machine Learning
• RNA
• Fibronectins

• genomic instability
• immune infiltration
• long non-coding RNA
• non-small cell lung cancer
• prognosis

• epithelial-mesenchymal transition
• genome instability
• long non-coding RNAs
• pancreatic ductal adenocarcinoma
• tumor microenvironment

• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Carcinoma, Pancreatic Ductal/diagnosis
• Carcinoma, Pancreatic Ductal/genetics
• Carcinoma, Pancreatic Ductal/pathology
• Gene Expression Regulation, Neoplastic
• Genomic Instability
• Humans
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms/genetics
• Pancreatic Neoplasms/pathology
• Prognosis
• RNA, Long Noncoding/metabolism
• Tumor Microenvironment/genetics

• National Natural Science Foundation of China

Ferroptosis is an iron-dependent form of cell death. In this study, we identified ferroptosis-related long noncoding RNAs (FRlncRNAs) to investigate their association with hepatocellular carcinoma (HCC) in prognosis, tumor immune environment, and genome instability.

Transcriptome profile data of HCC were retrieved from a public database. FRlncRNAs were identified by co-expression analysis. Patients were randomly divided into training and test cohorts. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were performed to construct a risk model. Patients were divided into high- and low-risk groups based on the risk model. AUC and C index were used to assess the risk model. Survival analysis, immune status, and genome instability were compared between the two groups.

Sixteen FRlncRNAs were identified and used to construct an FRlncRNA signature for the risk model. The Kaplan-Meier analysis revealed that patients in the high-risk group had poorer overall survival than patients in the low-risk group. The area under curve of the risk model was 0.879, 0.809, and 0.757 in the training cohort and 0.635, 0.688, and 0.739 in the test cohort at 1, 2, and 3 years, respectively. The risk model was an independent prognostic predictor and showed excellent prediction of prognosis compared with clinicopathological features. For the differentially expressed ferroptosis-related genes, many enriched metabolic pathways were identified in the functional enrichment analysis. Immune cells such as CD8+ T cells, macrophages M1, natural killer cells, and B cells, which may be associated with antitumor immune responses, differed between the high- and low-risk groups. Genome instability based on the risk model was also explored. A total of 61 genes were differently mutated between the two risk groups, and among them, TP53, HECW2, TRIM66, MCTP2, and KIAA1551 had the most significant mutation frequency differences.

The FRlncRNA signature is closely related with overall survival, tumor immune environment, and genome instability in HCC.

• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Breast Neoplasms/genetics
• Breast Neoplasms/therapy
• Carcinogenesis/genetics
• Female
• Gene Expression Regulation, Neoplastic
• Humans
• Immunotherapy
• Prognosis
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Signal Transduction/genetics
• Tumor Microenvironment/genetics

Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.

Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.

Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.

Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

• National Natural Science Foundation of China

Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.

Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan–Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF–lncRNA interactions.

A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value.

In summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.

Numerous studies have reported that long non-coding RNAs (lncRNAs) play important roles in immune-related pathways in cancer. However, immune-related lncRNAs and their roles in predicting immunotherapeutic response and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy remain largely unexplored.

Transcriptomic data from NSCLC patients were used to identify novel lncRNAs by a custom pipeline. ImmuCellAI was utilized to calculate the infiltration score of immune cells. The marker genes of immunotherapeutic response-related (ITR)-immune cells were used to identify immune-related (IR)-lncRNAs. A co-expression network was constructed to determine their functions. LASSO and multivariate Cox analyses were performed on the training set to construct an immunotherapeutic response and immune-related (ITIR)-lncRNA signature for predicting the immunotherapeutic response and prognosis of NSCLC. Four independent datasets involving NSCLC and melanoma patients were used to validate the ITIR-lncRNA signature.

In total, 7,693 novel lncRNAs were identified for NSCLC. By comparing responders with non-responders, 154 ITR-lncRNAs were identified. Based on the correlation between the marker genes of ITR-immune cells and lncRNAs, 39 ITIR-lncRNAs were identified. A co-expression network was constructed and the potential functions of 38 ITIR-lncRNAs were annotated, most of which were related to immune/inflammatory-related pathways. Single-cell RNA-seq analysis was performed to confirm the functional prediction results of an ITIR-lncRNA, LINC01272. Four-ITIR-lncRNA signature was identified and verified for predicting the immunotherapeutic response and prognosis of NSCLC. Compared with non-responders, responders had a lower risk score in both NSCLC datasets (P<0.05). NSCLC patients in the high-risk group had significantly shorter PFS/OS time than those in the low-risk group in the training and testing sets (P<0.05). The AUC value was 1 of responsiveness in the training set. In melanoma validation datasets, patients in the high-risk group also had significantly shorter OS/PFS time than those in the low-risk group (P<0.05). The ITIR-lncRNA signature was an independent prognostic factor (P<0.001).

Thousands of novel lncRNAs in NSCLC were identified and characterized. In total, 39 ITIR-lncRNAs were identified, 38 of which were functionally annotated. Four ITIR-lncRNAs were identified as a novel ITIR-lncRNA signature for predicting the immunotherapeutic response and prognosis in NSCLC patients treated with immunotherapy.

Different cancer types not only have common characteristics but also have their own characteristics respectively. The mechanism of these specific and common characteristics is still unclear. Pan-cancer analysis can help understand the similarities and differences among cancer types by systematically describing different patterns in cancers and identifying cancer-specific and cancer-common molecular biomarkers. While long non-coding RNAs (lncRNAs) are key cancer modulators, there is still a lack of pan-cancer analysis for lncRNA methylation dysregulation. In this study, we integrated lncRNA methylation, lncRNA expression and mRNA expression data to illuminate specific and common lncRNA methylation patterns in 23 cancer types. Then, we screened aberrantly methylated lncRNAs that negatively regulated lncRNA expression and mapped them to the ceRNA relationship for further validation. 29 lncRNAs were identified as diagnostic biomarkers for their corresponding cancer types, with lncRNA AC027601 was identified as a new KIRC-associated biomarker, and lncRNA ACTA2-AS1 was regarded as a carcinogenic factor of KIRP. Two lncRNAs HOXA-AS2 and AC007228 were identified as pan-cancer biomarkers. In general, the cancer-specific and cancer-common lncRNA biomarkers identified in this study may aid in cancer diagnosis and treatment.

• DNA methylation
• biomarker
• ceRNA
• lncRNA
• pan-cancer

Aberrant expression of long non-coding RNAs (lncRNAs), caused by alterations in DNA methylation, is a driving factor in several cancers. Interplay between lncRNAs’ aberrant methylation and expression in prostate cancer (PC) progression still remains largely elusive. Therefore, this study characterized the genome-wide epigenetic landscape and expression profiles of lncRNAs and their clinical impact by integrating multi-omics data implementing bioinformatics approaches. We identified 62 differentially methylated CpG-sites (DMCs) and 199 differentially expressed lncRNAs (DElncRNAs), where 32 DElncRNAs contain 32 corresponding DMCs within promoter regions. Significant negative correlation was observed between 8 DElncRNAs-DMCs pairs. 3 (cg23614229, cg23957912, and cg11052780) DMCs and 4 (CACNA1G-AS1, F11-AS1, NNT-AS1, and MSC-AS1) DElncRNAs were identified as high-risk factors for poor prognosis of PC patients. Overexpression of hypo-methylated CACNA1G-AS1, F11-AS1, and NNT-AS1 and down-regulation of hyper-methylated MSC-AS1 significantly lower the survival of PC patients and could be a potential prognostic and therapeutic biomarker. These DElncRNAs were found to be associated with several molecular functions whose deregulation can lead to cancer. Involvement of these epigenetically deregulated DElncRNAs in cancer-related biological processes was also noticed. These findings provide new insights into the understanding of lncRNA regulation by aberrant DNA methylation which will help to clarify the epigenetic mechanisms underlying PC.

• MDNAsi
• TCGA
• genome instability
• glioma
• lncRNA
• tumor microenvironment

Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

• genomic instability
• hepatocellular carcinoma
• immune infiltration
• long non-coding RNAs
• prognosis
• signature
• tumor immune activity

Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown. In this study, we performed the genome-wide investigation of lncRNA-associated plasma cfDNA 5hmC changes in PAAD by plotting 5hmC reads against lncRNA genes, and identified six PAAD-specific lncRNAs with abnormal 5hmC modifications compared with healthy individuals. Then we applied machine-learning and Cox regression approaches to develop predictive diagnostic (5hLRS) and prognostic (5hLPS) models using the 5hmC-modified lncRNAs. The 5hLRS demonstrated excellent performance in discriminating PAAD from healthy controls with an area under the curve (AUC) of 0.833 in the training cohort and 0.719 in the independent testing cohort. The 5hLPS also effectively divides PAAD patients into high-risk and low-risk groups with significantly different clinical outcomes in the training cohort (log-rank test p = 0.04) and independent testing cohort (log-rank test p = 0.0035). Functional analysis based on competitive endogenous RNA (ceRNA) and enrichment analysis suggested that these differentially regulated 5hmC modified lncRNAs were associated with angiogenesis, circulatory system process, leukocyte differentiation and metal ion homeostasis that are known important events in the development and progression of PAAD. In conclusion, our study indicated the potential clinical utility of 5hmC profiles at lncRNA loci as valuable biomarkers for non-invasive diagnosis and prognostication of cancers.

Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC.

This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients.

The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels.

A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts.

The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.

Background: Genomic instability of N6-methyladenosine (m⁶A)–related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m⁶A-associated lncRNA signature and revealed its prognostic role in LUAD.

Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The “Limma” R package was used to identify a network of regulatory m⁶A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability–derived m⁶A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m⁶A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m⁶A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m⁶A-related lncRNAs and the immune microenvironment. Prognostic m⁶A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m⁶A-related lncRNAs in LUAD.

Results: A total of 42 genomic instability–derived m⁶A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability–derived m⁶A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m⁶A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m⁶A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD.

Conclusion: A signature of genomic instability of m⁶A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.

Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.

Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N⁶-methyladenosine (m⁶A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).

Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m⁶A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.

Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m⁶A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

• genome instability
• long noncoding RNA
• lower-grade glioma
• multi-omics analysis
• signature

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignant cancers worldwide, and accurate prognostic models are urgently needed. Emerging evidence revealed that long non-coding RNAs (lncRNAs) are related to genomic instability. We sought to identify and validate a genomic instability-associated lncRNA prognostic signature to assess HNSCC patient survival outcomes.

Methods: RNA-sequencing data, somatic mutation files, and patient clinical data were downloaded from The Cancer Genome Atlas database. A total of 491 patients with completely clinical files were randomly divided into training and testing sets. In the training set, genomic instability-associated lncRNAs were screened through univariate Cox regression analyses and least absolute shrinkage and selection operator regression analyses to build a genomic instability-associated lncRNA signature (GILncSig). In addition, time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier survival curve, and clinical stratification analyses were used to evaluate the signature’s reliability. Finally, in situ hybridization experiments were performed to validate GILncSig expression levels between adjacent non-tumor tissues and tumor tissues from HNSCC patients.

Results: Four genomic instability-associated lncRNAs (AC023310.4, AC091729.1, LINC01564, and MIR3142HG) were selected for the prognostic signature. The model was successfully validated using the testing cohort. ROC analysis demonstrated its strong predictive ability for HNSCC prognosis. Univariate and multivariate Cox analyses revealed that the GILncSig was an independent predictor of prognosis. HNSCC patients with a low-risk score showed a substantially better prognosis than the high-risk groups. The in situ hybridization experiments using human HNSCC tissue revealed high GILncSig expression in HNSCC tissues compared with adjacent non-tumor tissues.

Conclusion: We developed a novel GILncSig for prognosis prediction in HNSCC patients, and the components of that signature might be therapeutic targets for HNSCC.

• Foundation for Innovative Research Groups of the National Natural Science Foundation of China
• Hunan Provincial Science and Technology Department