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

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

PeerJ ◽

10.7717/peerj.10418 ◽

2020 ◽

Vol 8 ◽

pp. e10418

Author(s):

Xiongtao Yang ◽

Guohui Wang ◽

Runchuan Gu ◽

Xiaohong Xu ◽

Guangying Zhu

Keyword(s):

Lung Cancer ◽

Dna Repair ◽

High Risk ◽

Risk Score ◽

Prognostic Value ◽

Risk Group ◽

Gene Signature ◽

Dna Repair Genes ◽

Prognostic Signature ◽

Repair Genes

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 of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11074 ◽

2021 ◽

Vol 9 ◽

pp. e11074

Author(s):

Jin Duan ◽

Youming Lei ◽

Guoli Lv ◽

Yinqiang Liu ◽

Wei Zhao ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Cell Analysis ◽

Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

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Establishment and Validation of a Ferroptosis-Related Gene Signature to Predict Overall Survival in Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.793636 ◽

2022 ◽

Vol 12 ◽

Author(s):

Su Wang ◽

Zhen Xie ◽

Zenghong Wu

Keyword(s):

Overall Survival ◽

High Risk ◽

Lung Adenocarcinoma ◽

Risk Group ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Functional Enrichment ◽

Related Gene ◽

Prognostic Signature

Background: Lung adenocarcinoma (LUAD) is the most common and lethal subtype of lung cancer. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a target in cancer therapy. However, the prognostic value of ferroptosis-related genes (FRGs)x in LUAD remains to be explored.Methods: In this study, we used RNA sequencing data and relevant clinical data from The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset to construct and validate a prognostic FRG signature for overall survival (OS) in LUAD patients and defined potential biomarkers for ferroptosis-related tumor therapy.Results: A total of 86 differentially expressed FRGs were identified from LUAD tumor tissues versus normal tissues, of which 15 FRGs were significantly associated with OS in the survival analysis. Through the LASSO Cox regression analysis, a prognostic signature including 11 FRGs was established to predict OS in the TCGA tumor cohort. Based on the median value of risk scores calculated according to the signature, patients were divided into high-risk and low-risk groups. Kaplan–Meier analysis indicated that the high-risk group had a poorer OS than the low-risk group. The area under the curve of this signature was 0.74 in the TCGA tumor set, showing good discrimination. In the GEO validation set, the prognostic signature also had good predictive performance. Functional enrichment analysis showed that some immune-associated gene sets were significantly differently enriched in two risk groups.Conclusion: Our study unearthed a novel ferroptosis-related gene signature for predicting the prognosis of LUAD, and the signature may provide useful prognostic biomarkers and potential treatment targets.

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The Prognostic Value of the DNA Repair Gene Signature in Head and Neck Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.710694 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ruijie Ming ◽

Enhao Wang ◽

Jiahui Wei ◽

Jinxiong Shen ◽

Shimin Zong ◽

...

Keyword(s):

Dna Repair ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Dna Repair Genes ◽

Repair Gene ◽

Cox Regression Analysis ◽

Dna Repair Gene ◽

Repair Genes

PurposeTo construct a prognostic signature composed of DNA repair genes to effectively predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).MethodsAfter downloading the transcriptome and clinical data of HNSCC from the Cancer Genome Atlas (TCGA), 499 patients with HNSCC were equally divided into training and testing sets. In the training set, 13 DNA repair genes were screened using univariate proportional hazard (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a risk model, which was validated in the testing set.ResultsIn the training and testing sets, there were significant differences in the clinical outcomes of patients in the high- and low-risk groups showed by Kaplan-Meier survival curves (P < 0.001). Univariate and multivariate Cox regression analyses showed that the risk score had independent prognostic predictive ability (P < 0.001). At the same time, the immune cell infiltration, immune score, immune-related gene expression, and tumor mutation burden (TMB) of patients with HNSCC were also different between the high- and low-risk groups (P < 0.05). Finally, we screened several chemotherapeutics for HNSCC, which showed significant differences in drug sensitivity between the high- and low-risk groups (P < 0.05).ConclusionThis study constructed a 13-DNA-repair-gene signature for the prognosis of HNSCC, which could accurately and independently predict the clinical outcome of the patient. We then revealed the immune landscape, TMB, and sensitivity to chemotherapy drugs in different risk groups, which might be used to guide clinical treatment decisions.

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Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

10.21203/rs.3.rs-49217/v1 ◽

2020 ◽

Author(s):

Jianfeng Zheng ◽

Jinyi Tong ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Training Set ◽

Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

BioMed Research International ◽

10.1155/2020/3641231 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Jianfeng Zheng ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu ◽

Jinyi Tong

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Immune Cell ◽

Risk Group ◽

Pearson Correlation ◽

Characteristic Curve ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Gynecological Malignancy

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression ( p < 0.01 ). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values ( p < 0.05 ) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

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Effects of Inflammation on the Immune Microenvironment in Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.690298 ◽

2021 ◽

Vol 11 ◽

Author(s):

Weidan Zhao ◽

Mingqing Liu ◽

Mingyue Zhang ◽

Yachen Wang ◽

Yingli Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Inflammatory Response ◽

Immune Cell ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Cancer Prognosis ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Immune Factor

BackgroundChronic inflammation and immune cell dysfunction in the tumor microenvironment are key factors in the development and progression of gastric tumors. However, inflammation-related genes associated with gastric cancer prognosis and their relationship with the expression of immune genes are not fully understood.MethodIn this study, we established an inflammatory response model score called “Riskscore”, based on differentially expressed genes in gastric cancer. We used Survival and Survminer packages in R to analyze patient survival and prognosis in risk groups. The survival curve was plotted using the Kaplan–Meier method, and the log-rank test was used to assess statistical significance, and we performed the ROC analysis using the R language package to analyze the 1-, 3-, and 5-year survival of patients in the GEO and TCGA databases. Single-factor and multi-factor prognostic analyses were carried out for age, sex, T, N, M, and risk score. Pathway enrichment analysis indicated immune factor-related pathway enrichment in both patient groups. Next, we screened for important genes that are involved in immune cell regulation. Finally, we created a correlation curve to explore the correlation between Riskscore and the expression of these genes.ResultsThe prognosis was significantly different between high- and low-risk groups, and the survival rate and survival time of the high-risk group were lower than those of the low-risk group. we found that the pathways related to apoptosis, hypoxia, and immunity were most enriched in the risk groups. we found two common tumor-infiltrating immune cell types (i.e., follicular helper T cells and resting dendritic cells) between the two risk groups and identified 10 genes that regulate these cells. Additionally, we found that these 10 genes are positively associated with the two risk groups.ConclusionFinally, a risk model of the inflammatory response in gastric cancer was established, and the inflammation-related genes used to construct the model were found to be directly related to immune infiltration. This model can improve the gastric cancer prognosis prediction. Our findings contribute to the development of immunotherapy for the treatment of gastric cancer patients.

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Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

Cancer Cell International ◽

10.1186/s12935-021-02231-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pu Wu ◽

Jinyuan Shi ◽

Wei Sun ◽

Hao Zhang

Keyword(s):

Thyroid Cancer ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Checkpoint Inhibitors ◽

Risk Groups ◽

Low Risk ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

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Novel Hypoxia-Related Gene Signature for Risk Stratification and Prognosis in Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.613890 ◽

2021 ◽

Vol 12 ◽

Author(s):

Quanxiao Li ◽

Limin Jin ◽

Meng Jin

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Stratification ◽

Risk Group ◽

External Validation ◽

Gene Signature ◽

Cancer Genome ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Individual Risk ◽

Tumor Node Metastasis Stage

Hepatocellular carcinoma (HCC) is the most common form of liver cancer with limited therapeutic options and low survival rate. The hypoxic microenvironment plays a vital role in progression, metabolism, and prognosis of malignancies. Therefore, this study aims to develop and validate a hypoxia gene signature for risk stratification and prognosis prediction of HCC patients. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used as a training cohort, and one Gene Expression Omnibus database (GSE14520) was served as an external validation cohort. Our results showed that eight hypoxia-related genes (HRGs) were identified by the least absolute shrinkage and selection operator analysis to develop the hypoxia gene signature and demarcated HCC patients into the high- and low-risk groups. In TCGA, ICGC, and GSE14520 datasets, patients in the high-risk group had worse overall survival outcomes than those in the low-risk group (all log-rank P < 0.001). Besides, the risk score derived from the hypoxia gene signature could serve as an independent prognostic factor for HCC patients in the three independent datasets. Finally, a nomogram including the gene signature and tumor-node-metastasis stage was constructed to serve clinical practice. In the present study, a novel hypoxia signature risk model could reflect individual risk classification and provide therapeutic targets for patients with HCC. The prognostic nomogram may help predict individualized survival.

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Complete Identification of Autophagy-Related lncRNA Prognostic Signature for Breast Cancer

10.21203/rs.3.rs-347298/v1 ◽

2021 ◽

Author(s):

Wenxi Wang ◽

Na Li ◽

Lin Shen ◽

Qin Zhou ◽

Zhanzhan Li ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

Pearson Correlation ◽

Risk Groups ◽

Cell Types ◽

High Risk Group ◽

Low Risk ◽

High Morbidity

Abstract Purpose: Breast cancer (BC) has a relatively high morbidity and mortality for women. The research about BC prognosis is significant. Autophagy is an essential process for tumor progression, which could play its role with lncRNA, a kind of ncRNA that have regulatory roles in multiple tumors. Therefore, constructing an autophagy-related prognostic model for breast cancer is meaningful.Methods: We download data from the TCGA and HADb. Pearson correlation analysis was performed to excavate autophagy-related lncRNA. Then with gene expression difference analysis, etc. we explored the relationship between clinical features and the signature. We applied Cytoscape as well as KEGG, etc. to explore expression condition. And the autophagy status of our signature was investigated by GSEA, etc. We also searched the immune distinction by CIBERSORTx to extend our study and preliminarily verified our study in the end.Results: Firstly, we got an independent autophagy-related lncRNA prognostic model, by which BC patients were divided into high- and low-risk groups. We found that the OS of high-risk group is significantly lower than that of low-risk group, which was identical to those within various clinical subgroups. Then, the KEGG and GO analysis enriched several pathways including autophagy. PCA and GSEA analysis demonstrated the autophagy status. Several distinguishing immune cell types in separated groups were revealed by immunity analysis. Then the verification in the end proved the feasibility of our signature.Conclusion: In this study, we acquired an independent autophagy-related lncRNA signature involving 12 lncRNAs, which contributes to the prediction of prognosis of BC patients.

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