A signature of tumor DNA repair genes associated with the prognosis of surgically-resected lung adenocarcinoma

Background Lung cancer has the highest morbidity and mortality of cancers worldwide. Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer and surgery is its most common treatment. The dysregulated expression of DNA repair genes is found in a variety of cancers and has been shown to affect the origin and progression of these diseases. However, the function of DNA repair genes in surgically-treated LUAD is unclear. Methods We sought to determine the association between the signature of DNA repair genes for patients with surgical LUAD and their overall prognosis. We obtained gene expression data and corresponding clinical information of LUAD from The Cancer Genome Atlas (TCGA) database. The differently expressed DNA repair genes of surgically-treated LUAD and normal tissues were identified using the Wilcoxon rank-sum test. We used uni- and multivariate Cox regression analyses to shrink the aberrantly expressed genes, which were then used to construct the prognostic signature and the risk score formula associated with the independent prognosis of surgically-treated LUAD. We used Kaplan–Meier and Cox hazard ratio analyses to confirm the diagnostic and prognostic roles. Two validation sets (GSE31210 and GSE37745) were downloaded from the Gene Expression Omnibus (GEO) and were used to externally verify the prognostic value of the signature. OSluca online database verifies the hazard ratio for the DNA repair genes by which the signature was constructed. We investigated the correlation between the signature of the DNA repair genes and the clinical parameters. The potential molecular mechanisms and pathways of the prognostic signature were explored using Gene Set Enrichment Analysis (GSEA). Results We determined the prognostic signature based on six DNA repair genes (PLK1, FOXM1, PTTG1, CCNO, HIST3H2A, and BLM) and calculated the risk score based on this formula. Patients with surgically-treated LUAD were divided into high-risk and low-risk groups according to the median risk score. The high-risk group showed poorer overall survival than the low-risk group; the signature was used as an independent prognostic indicator and had a greater prognostic value in surgically-treated LUAD. The prognostic value was replicated in GSE31210 and GSE37745. OSluca online database analysis shows that six DNA repair genes were associated with poor prognosis in most lung cancer datasets. The prognostic signature risk score correlated with the pathological stage and smoking status in surgically-treated LUAD. The GSEA of the risk signature in high-risk patients showed pathways associated with the cell cycle, oocyte meiosis, mismatch repair, homologous recombination, and nucleotide excision repair. Conclusions A six-DNA repair gene signature was determined using TCGA data mining and GEO data verification. The gene signature may serve as a novel prognostic biomarker and therapeutic target for surgically-treated LUAD.

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Identification and external validation of a prognostic signature associated with DNA repair genes in gastric cancer

Scientific Reports ◽

10.1038/s41598-021-86504-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shimin Chen ◽

Wenbo Liu ◽

Yu Huang

Keyword(s):

Dna Repair ◽

Immune Cell ◽

Risk Group ◽

External Validation ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Prognostic Signature ◽

Cox Analysis ◽

Repair Genes

AbstractThe aim of this study was to construct and validate a DNA repair-related gene signature for evaluating the overall survival (OS) of patients with gastric cancer (GC). Differentially expressed DNA repair genes between GC and normal gastric tissue samples obtained from the TCGA database were identified. Univariate Cox analysis was used to screen survival-related genes and multivariate Cox analysis was applied to construct a DNA repair-related gene signature. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value. The prognostic model and the expression levels of signature genes were validated using an independent external validation cohort. Two genes (CHAF1A, RMI1) were identified to establish the prognostic signature and patients ware stratified into high- and low-risk groups. Patients in high-risk group presented significant shorter survival time than patients in the low-risk group in both cohorts, which were verified by the ROC curves. Multivariate analysis showed that the prognostic signature was an independent predictor for patients with GC after adjustment for other known clinical parameters. A nomogram incorporating the signature and known clinical factors yielded better performance and net benefits in calibration plot and decision curve analyses. Further, the logistic regression classifier based on the two genes presented an excellent diagnostic power in differentiating early HCC and normal tissues with AUCs higher than 0.9. Moreover, Gene Set Enrichment Analysis revealed that diverse cancer-related pathways significantly clustered in the high-risk and low-risk groups. Immune cell infiltration analysis revealed that CHAF1A and RMI1 were correlated with several types of immune cell subtypes. A prognostic signature using CHAF1A and RMI1 was developed that effectively predicted different OS rates among patients with GC. This risk model provides new clinical evidence for the diagnostic accuracy and survival prediction of GC.

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Identification of a Prognostic Six-Immune-Gene Signature and a Nomogram Model for Uveal Melanoma

10.21203/rs.3.rs-1148784/v1 ◽

2022 ◽

Author(s):

Binghua Yang ◽

Yuxia Fan ◽

Renlong Liang ◽

Yi Wu ◽

Aiping Gu

Keyword(s):

High Risk ◽

Uveal Melanoma ◽

Risk Score ◽

Risk Group ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Signature ◽

Functional Enrichment ◽

Immune Gene ◽

Prognostic Signature

Abstract Background: To identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma. Methods: The RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated.Results: We obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings.Conclusion: Our newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.619517 ◽

2021 ◽

Vol 11 ◽

Author(s):

Mu-xing Li ◽

Hang-yan Wang ◽

Chun-hui Yuan ◽

Zhao-lai Ma ◽

Bin Jiang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Regression Analysis ◽

Signaling Pathway ◽

Risk Score ◽

Prognostic Value ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature ◽

Phenotypic Switch

IntroductionMacrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.MethodsWe identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.ResultsWe detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P < 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.ConclusionsThe nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

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Analysis of DNA damage response gene signature for prognosis prediction of esophageal squamous cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16073 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16073-e16073

Author(s):

Weitao Zhuang ◽

Xiao-song Ben ◽

Dan Tian ◽

Zihao Zhou ◽

Gang Chen ◽

...

Keyword(s):

High Risk ◽

Dna Damage Response ◽

Squamous Cell ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Prognostic Signature ◽

Training Set ◽

Prognosis Prediction ◽

Damage Response

e16073 Background: Esophageal squamous cell cancer (ESCC) is a malignant tumor with a poor 5-year relative survival. A prognosis prediction signature associated with DNA Damage Response (DDR) genes in ESCC was explored in this study. Methods: The clinical and gene expression profiles of ESCC patients were downloaded from the GEO and TCGA database. Univariate Cox regression and 1000 iterations of 10-fold cross-validation of LASSO Cox regression with binomial deviance minimization criteria were used to identify DDR genes as potential object and a prognostic signature for ESCC survival prediction, followed by validation of the signature via TCGA cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: A signature of 8 DDR genes were constructed as being significantly associated with overall survival (OS) among patients with esophageal squamous cell carcinoma. The pronostic signature stratified ESCC patients into low- vs high-risk groups in terms of OS in the training set, testing set and the validation cohorts, and remained as an independent prognostic factor in multivariate analyses (hazard ratio (HR) in training set, 0.17 [95% CI, 0.09-0.35; P < 0 .001], HR in testing set, 0.38 [95% CI, 0.16-0.93; P = 0.029], HR in discovery cohort, 0.171 [95% CI, 0.03-0.48; P < 0 .001]) after adjusting for clinicopathological factors. The 8-DDR gene signature achieved a higher accuracy (C-index, 0.69; AUCs for 1-, 3- and 5-year OS, 0.74, 0.77 and 0.76, respectively) than 7 previously reported multigene signatures (C-index range, 0.53 to 0.60; AUCs range, 0.55to 0.66, 0.54 to 0.64 and 0.62 to 0.66, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor location, grade, adjuvant therapy and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the DNA repair was more prominently enriched in the high-risk group while the low-risk group had not enrichment of any process (P > 0.05 for all). Conclusions: Taken together, our study identified 8 DDR genes related to the prognosis of ESCC patients, and constructed a robust prognostic signature to effectively stratify ESCC patients with different survival rates, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

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