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

Identification of a prognostic DNA repair gene signature in esophageal cancer

Background

DNA repair plays a crucial role in the development and progression of different types of cancers. Nevertheless, little is known about the role of DNA repair-related genes (DRRGs) in esophageal cancer (EC). The present study aimed to identify a novel DRRGs prognostic signature in EC.

Methods

Gene set enrichment analysis (GSEA) was performed to screen 150 genes related to DNA repair, which is the most important enrichment gene set in EC. Univariate and multivariate Cox regression analyses were used to screen DRRGs closely associated with prognosis. The difference in the expression of hub DRRGs between tumor and normal tissues was analyzed. Combined with clinical indicators (including age, gender, and tumor stage), we evaluated whether the 4-DRRGs signature was an independent prognostic factor. In addition, we evaluated the prediction accuracy using a receiver operating characteristic (ROC) curve and visualized the model's performance via a nomogram.

Results

Four-DRRGs (NT5C3A, TAF9, BCAP31, and NUDT21) were selected by Cox regression analysis to establish a prognostic signature to effectively classify patients into high- and low-risk groups. The area under the curve (AUC) of the time-dependent ROC of the prognostic signature for 1- and 3-year was 0.769 and 0.720, respectively. Compared with other clinical characteristics, the risk score showed a robust ability to predict the prognosis in EC, especially in the early stage of EC. Furthermore, we constructed a nomogram to interpret the clinical application of the 4-DRRGs signature.

Conclusions

In conclusion, we identified a prognostic signature based on the DRRGs for patients with EC, which can contribute independent value in identifying clinical outcomes that complement the TNM system in EC.

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.

Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Alterations in DNA repair-related genes (DRGs) are observed in a variety of cancers and have been shown to affect the development and treatment of cancers. The aim of this study was to develop a DRG-related signature for predicting prognosis and therapeutic response in PAAD.

Methods

We constructed a DRG signature using least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCGA training set. GEO datasets were used as the validation set. A predictive nomogram was constructed based on multivariate Cox regression. Calibration curve and decision curve analysis (DCA) were applied to validate the performance of the nomogram. The CIBERSORT and ssGSEA algorithms were utilized to explore the relationship between the prognostic signature and immune cell infiltration. The pRRophetic algorithm was used to estimate sensitivity to chemotherapeutic agents. The CellMiner database and PAAD cell lines were used to investigate the relationship between DRG expression and therapeutic response.

Results

We developed a DRG signature consisting of three DRGs (RECQL, POLQ, and RAD17) that can predict prognosis in PAAD patients. A prognostic nomogram combining the risk score and clinical factors was developed for prognostic prediction. The DCA curve and the calibration curve demonstrated that the nomogram has a higher net benefit than the risk score and TNM staging system. Immune infiltration analysis demonstrated that the risk score was positively correlated with the proportions of activated NK cells and monocytes. Drug sensitivity analysis indicated that the signature has potential predictive value for chemotherapy. Analyses utilizing the CellMiner database showed that RAD17 expression is correlated with oxaliplatin. The dynamic changes in three DRGs in response to oxaliplatin were examined by RT-qPCR, and the results show that RAD17 is upregulated in response to oxaliplatin in PAAD cell lines.

Conclusion

We constructed and validated a novel DRG signature for prediction of the prognosis and drug sensitivity of patients with PAAD. Our study provides a theoretical basis for further unraveling the molecular pathogenesis of PAAD and helps clinicians tailor systemic therapies within the framework of individualized treatment.

DNA damage repair gene signature model for predicting prognosis and chemotherapy outcomes in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is prone to metastasis and likely to develop resistance to chemotherapeutic drugs. DNA repair has been reported to be involved in the progression and chemoresistance of LUSC. However, the relationship between LUSC patient prognosis and DNA damage repair genes is still unclear.

METHODS

The clinical information of LUSC patients and tumour gene expression level data were downloaded from the TCGA database. Unsupervised clustering and Cox regression were performed to obtain molecular subtypes and prognosis-related significant genes based on a list including 150 DNA damage repair genes downloaded from the GSEA database. The coefficients determined by the multivariate Cox regression analysis and the expression level of prognosis-related DNA damage repair genes were employed to calculate the risk score, which divided LUSC patients into two groups: the high-risk group and the low-risk group. Immune viability, overall survival, and anticarcinogen sensitivity analyses of the two groups of LUSC patients were performed by Kaplan-Meier analysis with the log rank test, ssGSEA and the pRRophetic package in R software. A time-dependent ROC curve was applied to compare the survival prediction ability of the risk score, which was used to construct a survival prediction model by multivariate Cox regression. The prediction model was used to build a nomogram, the discriminative ability of which was confirmed by C-index assessment, and its calibration was validated by calibration curve analysis. Differentially expressed DNA damage repair genes in LUSC patient tissues were retrieved by the Wilcoxon test and validated by qRT-PCR and IHC.

RESULT

LUSC patients were separated into two clusters based on molecular subtypes, of which Cluster 2 was associated with worse overall survival. A prognostic prediction model for LUSC patients was constructed and validated, and a risk score calculated based on the expression levels of ten DNA damage repair genes was employed. The clinical utility was evaluated by drug sensitivity and immune filtration analyses. Thirteen-one genes were upregulated in LUSC patient samples, and we selected the top four genes that were validated by RT-PCR and IHC.

CONCLUSION

We established a novel prognostic model based on DNA damage repair gene expression that can be used to predict therapeutic efficacy in LUSC patients.

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.

Identification of a DNA Repair Gene Signature and Establishment of a Prognostic Nomogram Predicting Biochemical-Recurrence-Free Survival of Prostate Cancer

Background: The incidence of prostate cancer (PCa) is high and increasing worldwide. The prognosis of PCa is relatively good, but it is important to identify the patients with a high risk of biochemical recurrence (BCR) so that additional treatment could be applied.

Method: Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) to serve as training data. The GSE84042 dataset was used as a validation set. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a DNA repair gene (DRG) signature. The performance of the DRG signature was assessed based on Kaplan–Meier curve, receiver operating characteristic (ROC), and Harrell’s concordance index (C-index). Furtherly, a prognostic nomogram was established and evaluated likewise.

Results: A novel four DRG signature was established to predict BCR of PCa, which included POLM, NUDT15, AEN, and HELQ. The ROC and C index presented good performance in both training dataset and validation dataset. The patients were stratified by the signature into high- and low-risk groups with distinct BCR survival. Multivariate Cox analysis revealed that the DRG signature is an independent prognostic factor for PCa. Also, the DRG signature high-risk was related to a higher homologous recombination deficiency (HRD) score. The nomogram, incorporating the DRG signature and clinicopathological parameters, was able to predict the BCR with high efficiency and showed superior performance compared to models that consisted of only clinicopathological parameters.

Conclusion: Our study identified a DRG signature and established a prognostic nomogram, which were reliable in predicting the BCR of PCa. This model could help with individualized treatment and medical decision making.

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Background

Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.

Methods

The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.

Results

Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.

Conclusions

The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-020-02116-y.