The use of DNA repair genes as prognostic indicators of gastric cancer

Development and validation of a DNA damage repair-related gene-based prediction model for the prognosis of lung adenocarcinoma

Background

Lung cancer is the leading cause of morbidity and mortality among all cancer types, with lung adenocarcinoma (LUAD) being the most prevalent subtype. DNA damage repair (DDR)-related genes are closely associated with cancer progression and treatment, with emerging evidence highlighting their correlation with tumor development. However, the relationship between LUAD prognosis and DDR-related genes remains unclear.

Methods

RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The GSE31210 dataset, utilized for external validation, was retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed DDR genes were identified, and a DDR-related prognostic model was established and validated using Kaplan-Meier (KM) survival analysis, time-dependent receiver operating characteristic (ROC) curves, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, and immune cell infiltration. A P value of less than 0.05 was considered statistically significant.

Results

A total of 514 patients with LUAD from TCGA database were divided into distinct subtypes to characterize the diversity within the DDR pathway. DDR-activated and DDR-suppressed subgroups showed distinct clinical characteristics, molecular characteristics, and immune profiles. Nine genes were identified as hub DDR-related genes, including CASP14, DKK1, ECT2, FLNC, HMMR, IGFBP1, KRT6A, TYMS, and FCER2. By using the expression levels of these selected genes, the corresponding risk scores for each sample was predicted. In the training group, KM survival analysis revealed that the high-risk group exhibited significantly diminished overall survival (OS) [hazard ratio (HR) =3.341, P=1.38e-08]. The corresponding area under the curve (AUC) values for the 1-year follow-up periods was 0.767, respectively. Upon validation in the external cohort, patients with higher risk scores manifested significantly reduced OS (HR =2.372, P=1.87e-03). The AUC values of the ROC curves for the 1-year OS in the validation cohort was 0.87, respectively. Moreover, advanced DDR risk score was correlated with increased TMB scores, a heightened frequency of TP53 mutations, an increased abundance of cancer-testicular antigens (CTAs), and a lower tumor immune dysfunction and exclusion (TIDE) score in patients with LUAD (P<0.05).

Conclusions

A nine-gene risk signature associated with DDR in LUAD was effectively developed, demonstrating its potential as a robust and reliable classification tool for clinical practice. This model exhibited the capability to accurately predict the prognosis and survival outcomes of LUAD patients.

Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma

DNA repair is a critical factor in tumor progression as it impacts tumor mutational burden, genome stability, PD-L1 expression, immunotherapy response, and tumor-infiltrating lymphocytes (TILs). In this study, we present a prognostic model for hepatocellular carcinoma (HCC) that utilizes genes related to the DNA damage response (DDR). Patients were stratified based on their risk score, and groups with lower risk scores demonstrated better survival rates compared to those with higher risk scores. The prognostic model's accuracy in predicting 1-, 3-, and 5-year survival rates for HCC patients was analyzed using receiver operator curve analysis (ROC). Results showed good accuracy in predicting survival rates. Additionally, we evaluated the prognostic model's potential as an independent factor for HCC prognosis, along with tumor stage. Furthermore, nomogram was employed to determine the overall survival year of patients with HCC based on this independent factor. Gene set enrichment analysis (GSEA) revealed that in the high-risk group, apoptosis, cell cycle, MAPK, mTOR, and WNT cascades were highly enriched. We used training and validation datasets to identify potential molecular subtypes of HCC based on the expression of DDR genes. The two subtypes differed in terms of checkpoint receptors for immunity and immune cell filtration capacity.Collectively, our study identified potential biomarkers of HCC prognosis, providing novel insights into the molecular mechanisms underlying HCC.

Integrated Bioinformatics Analysis of Serine Racemase as an Independent Prognostic Biomarker in Endometrial Cancer

Endometrial cancer (EC) kills about 76,000 women worldwide, with the highest incidence in industrialized countries. Because of the rise in disease mortality and new diagnoses, EC is now a top priority for women’s health. Serine racemase (SRR) is thought to play a role in the central nervous system, but its role in cancers, particularly in EC, is largely unknown. The current study starts with a pan-cancer examination of SRR’s expression and prognostic value before delving into SRR’s potential cancer-suppressing effect in patients with EC. SRR may affect the endometrial tumor immune microenvironment, according to subsequent immune-related analysis. SRR expression is also linked to several genes involved in specific pathways such as ferroptosis, N6-methyladenosine methylation, and DNA damage repair. Finally, we used the expression, correlation, and survival analyses to investigate the upstream potential regulatory non-coding RNAs of SRR. Overall, our findings highlight the prognostic significance of SRR in patients with EC, and we can formulate a reasonable hypothesis that SRR influences metabolism and obstructs key carcinogenic processes in EC.

Dynamic Changes in Myofibroblasts Affect the Carcinogenesis and Prognosis of Bladder Cancer Associated With Tumor Microenvironment Remodeling

Bladder cancer (BLCA) is a tumor that possesses significant heterogeneity, and the tumor microenvironment (TME) plays an important role in the development of BLCA. The TME chiefly consists of tumor cells and tumor-infiltrating immune cells admixed with stromal components. Recent studies have revealed that stromal components, especially cancer-associated fibroblasts (CAFs), affect immune cell infiltration and modulate the extracellular matrix in the TME of BLCA, ultimately impacting the prognosis and therapeutic efficacy of BLCA. Among the subgroups of CAFs, myofibroblasts (myCAFs) were the most abundant and were demonstrated to play an essential role in affecting the prognosis of various tumors, including BLCA. However, the dynamic changes in myCAFs during carcinogenesis and tumor progression have been less discussed previously. With the help of bioinformatics algorithms, we discussed the roles of myCAFs in the carcinogenesis and prognosis of BLCA in this manuscript. Our study highlighted the pathogenesis of BLCA was accompanied by a decrease in the abundance of myCAFs, revealing potential protective properties of myCAFs in the carcinogenesis of BLCA. Meanwhile, the reduced expressions of myCAFs marker genes were highly accurate in predicting tumorigenesis. In contrast, we also demonstrated that myCAFs regulated extracellular matrix remodeling, tumor metabolism, cancer stemness, and oncological mutations, ultimately impacting the treatment responsiveness and prognosis of BLCA patients. Thus, our research revealed the bimodal roles of myCAFs in the development of BLCA, which may be associated with the temporal change of the TME. The in-depth study of myofibroblasts and the TME may provide potential diagnostic biomarkers and therapeutic targets for BLCA.

A Novel DNA Damage Repair-Related Gene Signature for Predicting Glioma Prognosis

Background

Glioma is one of the most prevalent tumors in the central nervous system of adults and shows a poor prognosis. This study aimed to develop a DNA damage repair (DDR)-related gene signature to evaluate the prognosis of glioma patients.

Methods

Differentially expressed genes (DEGs) were extracted based on 276 DDR genes. Then, a gene signature was developed for the survival prediction in glioma patients by means of univariate, multivariate Cox, and least absolute shrinkage and selector operation (Lasso) analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. A total of 693 gliomas from the Chinese Glioma Genome Atlas (CGGA) were used for external validation. In addition, we used glioma tumor tissues for qPCR experiment to verify.

Results

A 12-DDR-related gene signature was identified from the 75 DEGs to stratify the survival risk of glioma patients. The overall survival of high-risk group was significantly shorter than that of low-risk group (P < 0.001). Besides, according to the risk score assessment, patients in high- or low-risk group also had significant correlations with clinicopathological parameters, including age (P < 0.01), grade (P < 0.001), IDH status (P < 0.001) and 1p19q codeletion status (P < 0.001). The nomogram provided favorable C-index and calibration plots. The C-index of training set and verification set was 0.761 and 0.746, respectively, and the calibration curve also showed that both training set and verification set were close to the standard curve. The qPCR results showed that there were significant differences in the expression of some typical DDR-related genes in tumor tissues and paracancer tissues (P_(WEE1)=0.0002, P_(RECQL)=0.0117, P_(RPA1)=0.021, P_(RRM1)=0.0035, P_(PARP4)=0.0006, P_(ELOA)=0.0023).

Conclusion

Our study developed a novel 12 DDR-related gene signature as a practical prognostic predictor for glioma patients. A nomogram combining the signature and clinical parameters was established as an individual clinical prediction tool.

Pan-cancer analysis of non-oncogene addiction to DNA repair

Cancer cells usually depend on the aberrant function of one or few driver genes to initiate and promote their malignancy, an attribute known as oncogene addiction. However, cancer cells might become dependent on the normal cellular functions of certain genes that are not oncogenes but ensure cell survival (non-oncogene addiction). The downregulation or silencing of DNA repair genes and the consequent genetic and epigenetic instability is key to promote malignancy, but the activation of the DNA-damage response (DDR) has been shown to become a type of non-oncogene addiction that critically supports tumour survival. In the present study, a systematic evaluation of DNA repair addiction at the pan-cancer level was performed using data derived from The Cancer Dependency Map and The Cancer Genome Atlas (TCGA). From 241 DDR genes, 59 were identified as commonly essential in cancer cell lines. However, large differences were observed in terms of dependency scores in 423 cell lines and transcriptomic alterations across 18 cancer types. Among these 59 commonly essential genes, 14 genes were exclusively associated with better overall patient survival and 19 with worse overall survival. Notably, a specific molecular signature among the latter, characterized by DDR genes like UBE2T, RFC4, POLQ, BRIP1, and H2AFX showing the weakest dependency scores, but significant upregulation was strongly associated with worse survival. The present study supports the existence and importance of non-oncogenic addiction to DNA repair in cancer and may facilitate the identification of prognostic biomarkers and therapeutic opportunities.

Unsupervised Hierarchical Clustering Identifies Immune Gene Subtypes in Gastric Cancer

Objectives: The pathogenesis of heterogeneity in gastric cancer (GC) is not clear and presents as a significant obstacle in providing effective drug treatment. We aimed to identify subtypes of GC and explore the underlying pathogenesis. Methods: We collected two microarray datasets from GEO (GSE84433 and GSE84426), performed an unsupervised cluster analysis based on gene expression patterns, and identified related immune and stromal cells. Then, we explored the possible molecular mechanisms of each subtype by functional enrichment analysis and identified related hub genes. Results: First, we identified three clusters of GC by unsupervised hierarchical clustering, with average silhouette width of 0.96, and also identified their related representative genes and immune cells. We validated our findings using dataset GSE84426. Subtypes associated with the highest mortality (subtype 2 in the training group and subtype C in the validation group) showed high expression of SPARC, COL3A1, and CCN. Both subtypes also showed high infiltration of fibroblasts, endothelial cells, hematopoietic stem cells, and a high stromal score. Furthermore, subtypes with the best prognosis (subtype 3 in the training group and subtype A in the validation group) showed high expression of FGL2, DLGAP1-AS5, and so on. Both subtypes also showed high infiltration of CD4⁺ T cells, CD8⁺ T cells, NK cells, pDC, macrophages, and CD4⁺ T effector memory cells. Conclusion: We found that GC can be classified into three subtypes based on gene expression patterns and cell composition. Findings of this study help us better understand the tumor microenvironment and immune milieu associated with heterogeneity in GC and provide practical information to guide personalized treatment.

DNA Repair-Based Gene Expression Signature and Distinct Molecular Subtypes for Prediction of Clinical Outcomes in Lung Adenocarcinoma

Objective: To conduct a robust prognostic gene expression signature and characterize molecular subtypes with distinct clinical characteristics for lung adenocarcinoma (LUAD).

Methods: Based on DNA repair genes from the GSEA database, a prognostic signature was conducted in the TCGA-LUAD training set via univariate and multivariate cox regression analysis. Its prediction power was validated by overall survival analysis, relative operating characteristic (ROC) curves and stratification analysis in the GSE72094 verification set. Involved pathways in the high- and low-risk groups were analyzed by GSEA. A nomogram was built based on the signature and clinical features and its performance was assessed by calibration plots. LUAD samples were clustered via the ConsensusClusterPlus package. The differences in clinical outcomes, single nucleotide polymorphism (SNP) and sensitivity to chemotherapy drugs between molecular subtypes were analyzed.

Results: A 13-DNA repair gene-signature was constructed for LUAD prognosis. Following validation, it can robustly and independently predict patients' clinical outcomes. The GSEA results exhibited the differences in pathways between high- and low- risk groups. A nomogram combining the signature and stage could accurately predict 1-, 3-, and 5-year survival probability. Two distinct molecular subtypes were characterized based on DNA repair genes. Patients in the Cluster 2 exhibited a worse prognosis and were more sensitive to common chemotherapy than those in the Cluster 1.

Conclusion:This study proposed a 13-DNA repair gene-signature as a prognostic factor for LUAD patients, which can independently predict clinical outcomes by complement of the stage. Moreover, we characterized two LUAD subtypes with distinct clinical outcomes, somatic gene mutations, and drug sensitivity in cancer based on DNA repair genes.

Development and validation of a DNA repair gene signature for prognosis prediction in Colon Cancer

Aberrant expression of DNA repair genes (DRGs) can be related to tumor progression and clinical outcomes in colon cancer. Here, we aimed to establish a DRGs signature to identify the vital prognostic DRGs in colon cancer. Firstly, gene set enrichment analysis (GSEA) was performed to demonstrate the association between abnormal expression level of DRGs and tumorigenesis. Then, a total of 476 DRGs were obtained for detecting candidate biomarkers in randomly selected 295 cases from The Cancer Genome Atlas (TCGA) colon cancer cohort. Eleven genes were screened by LASSO Cox regression analyses to develop the prognostic model. Then, the prognostic model and the expression levels of the eleven genes were validated using the internal validation dataset (the rest 125 cases in TCGA cohort) and an external validation dataset (obtained from Gene Expression Omnibus dataset). Further analysis revealed the independent prognostic capacity of the prognostic model in relation to other clinical characteristics. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model. Furthermore, we provided a nomogram for interpreting the clinical application of the 11-DRG signature. In conclusion, we propose a newly developed 11-DRG signature as a practical prognostic predictor for patients with colon cancer, which can facilitate the individualized counselling and treatment.