Pyroptosis-Related Gene Signature Is a Novel Prognostic Biomarker for Sarcoma Patients

Sarcoma is a rare and an extremely aggressive form of cancer that originates from mesenchymal cells. Pyroptosis exerts a dual effect on tumours by inhibiting tumour cell proliferation while creating a microenvironment suitable for tumour cell development and proliferation. However, the significance of pyroptosis-related gene (PRG) expression in sarcoma has not yet been evaluated. Here, we conduct a retrospective analysis to examine PRG expression in 256 sarcoma samples from The Cancer Genome Atlas database. We identified the PRGs that had a significant correlation with overall patient survival in sarcoma by performing a univariate Cox regression analysis. Subsequently, we conducted a LASSO regression analysis and created a risk model for a six-PRG signature. As indicated from the Kaplan–Meier analysis, this signature revealed a significant difference between high- and low-risk sarcoma patients. A receiver operating characteristic curve analysis confirmed that this signature could predict overall patient survival in sarcoma patients with high sensitivity and specificity. Gene ontology annotation and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses revealed that five independent PRGs were closely associated with increased immune activity. Moreover, we also deciphered that increased number of immune cells infiltrated the tumour microenvironment in sarcoma. In brief, the PRG signature can effectively act as novel prognostic biomarker for sarcoma patients and is associated with the tumour immune microenvironment.

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A novel signature based on m6A related genes as the potential prognostic factor in hepatocellular carcinoma.

10.21203/rs.2.21963/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Hao Hu ◽

Ling Yu ◽

Zhirui Cao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Potential Biomarker ◽

Significant Difference

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.

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A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Journal of Oncology ◽

10.1155/2021/6653247 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Haoya Xu ◽

Ruoyao Zou ◽

Jiyu Liu ◽

Liancheng Zhu

Keyword(s):

Regression Analysis ◽

Endometrial Cancer ◽

Cancer Patients ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Cox Regression Analysis ◽

Gynecology And Obstetrics ◽

Related Risk ◽

Patient Prognosis

Purpose. To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNAsi-related risk signature for endometrial cancer. Methods. We collected mRNAsi data of endometrial cancer samples from The Cancer Genome Atlas (TCGA) and analyzed their relationship with the main clinicopathological characteristics and prognosis of endometrial cancer patients. We screened the top 50% of the genes in TCGA for weighted gene correlation network analysis (WGCNA) to explore mRNAsi-related gene sets. Among these mRNAsi-related genes, we further screened for those related to the prognosis of endometrial cancer patients via univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Using stepwise multivariate Cox regression analysis, a stemness index-related risk signature was constructed. Finally, we identified potential prognostic biomarkers for endometrial cancer by combining the GEO database and immunohistochemical staining. Results. The mRNAsi of endometrial cancer samples was significantly higher than that of normal samples and was related to the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, postoperative tumor status, and overall survival of endometrial cancer patients. We identified 21 mRNAsi-related gene modules, and 1,324 genes were obtained from the most relevant module. TCGA samples were divided into training and validation cohorts, and the training cohort was used to construct a nine-mRNAsi-related gene signature (B3GAT2, CD3EAP, DMC1, FRMPD3, LINC01224, LINC02068, LY6H, NR6A1, and TLE2). High-risk and low-risk patients had significant prognostic differences, and the risk signature could accurately predict their 1-, 3-, and 5-year survival. The nomogram composed of risk score and multiple clinicopathological features could accurately predict 1-, 3-, and 5-year survival. Finally, CD3EAP was found to be a novel prognostic biomarker for endometrial cancer. Conclusion. Endometrial cancer cell stemness is related to patient prognosis. The nine-gene risk signature is an independent prognostic factor and can accurately predict endometrial cancer patient prognosis.

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Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.612196 ◽

2020 ◽

Vol 11 ◽

Author(s):

Hao Zuo ◽

Luojun Chen ◽

Na Li ◽

Qibin Song

Keyword(s):

Pancreatic Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Transcriptional Level ◽

Cox Regression Analysis

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

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Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis

International Journal of Genomics ◽

10.1155/2020/1097602 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23

Author(s):

Yutao Wang ◽

Jiaxing Lin ◽

Kexin Yan ◽

Jianfeng Wang

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Test Set ◽

Cox Regression Analysis ◽

Significant Difference ◽

Robust Likelihood

Aim. In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method. We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P<0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the “Rbsurv” package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. Results. FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 − 0.737∗TACR2 − 0.651∗PLEK2 − 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC=0.6039) and GSE21032 GPL5188 (140 samples; AUC=0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. Conclusion. A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

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Identification of an 11-Autophagy-Related-Gene Signature as Promising Prognostic Biomarker for Bladder Cancer Patients

Biology ◽

10.3390/biology10050375 ◽

2021 ◽

Vol 10 (5) ◽

pp. 375

Author(s):

Chaoting Zhou ◽

Alex Heng Li ◽

Shan Liu ◽

Hong Sun

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Training Cohort ◽

Cox Regression Analysis ◽

Therapeutic Decisions

Background: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. Methods: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. Results: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancer patients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. Conclusion: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.

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Development and validation of a novel pseudogene pair-based prognostic signature for prediction of overall survival in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-020-07391-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yajuan Du ◽

Ying Gao

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Pearson Correlation ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

The Novel ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background There is growing evidence that pseudogenes may serve as prognostic biomarkers in several cancers. The present study was designed to develop and validate an accurate and robust pseudogene pairs-based signature for the prognosis of hepatocellular carcinoma (HCC). Methods RNA-sequencing data from 374 HCC patients with clinical follow-up information were obtained from the Cancer Genome Atlas (TCGA) database and used in this study. Survival-related pseudogene pairs were identified, and a signature model was constructed by Cox regression analysis (univariate and least absolute shrinkage and selection operator). All individuals were classified into high- and low-risk groups based on the optimal cutoff. Subgroups analysis of the novel signature was conducted and validated in an independent cohort. Pearson correlation analyses were carried out between the included pseudogenes and the protein-coding genes based on their expression levels. Enrichment analysis was performed to predict the possible role of the pseudogenes identified in the signature. Results A 19-pseudogene pair signature, which included 21 pseudogenes, was established. Patients in high-risk group demonstrated an increased the risk of adverse prognosis in the TCGA cohort and the external cohort (all P < 0.001). The novel pseudogene signature was independent of other conventional clinical variables used for survival prediction in HCC patients in the two cohorts revealed by the multivariate Cox regression analysis (all P < 0.001). Subgroup analysis further demonstrated the diagnostic value of the signature across different stages, grades, sexes, and age groups. The C-index of the prognostic signature was 0.761, which was not only higher than that of several previous risk models but was also much higher than that of a single age, sex, grade, and stage risk model. Furthermore, functional analysis revealed that the potential biological mechanisms mediated by these pseudogenes are primarily involved in cytokine receptor activity, T cell receptor signaling, chemokine signaling, NF-κB signaling, PD-L1 expression, and the PD-1 checkpoint pathway in cancer. Conclusion The novel proposed and validated pseudogene pair-based signature may serve as a valuable independent prognostic predictor for predicting survival of patients with HCC.

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A Novel Six Autophagy-Related Genes Signature Associated With Outcomes and Immune Microenvironment in Lower-Grade Glioma

Frontiers in Genetics ◽

10.3389/fgene.2021.698284 ◽

2021 ◽

Vol 12 ◽

Author(s):

Tao Lin ◽

Hao Cheng ◽

Da Liu ◽

Lei Wen ◽

Junlin Kang ◽

...

Keyword(s):

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Lower Grade ◽

Immune Microenvironment ◽

Cox Regression Analysis ◽

Significant Difference

Since autophagy and the immune microenvironment are deeply involved in the tumor development and progression of Lower-grade gliomas (LGG), our study aimed to construct an autophagy-related risk model for prognosis prediction and investigate the relationship between the immune microenvironment and risk signature in LGG. Therefore, we identified six autophagy-related genes (BAG1, PTK6, EEF2, PEA15, ITGA6, and MAP1LC3C) to build in the training cohort (n = 305 patients) and verify the prognostic model in the validation cohort (n = 128) and the whole cohort (n = 433), based on the data from The Cancer Genome Atlas (TCGA). The six-gene risk signature could divide LGG patients into high- and low-risk groups with distinct overall survival in multiple cohorts (all p < 0.001). The prognostic effect was assessed by area under the time-dependent ROC (t-ROC) analysis in the training, validation, and whole cohorts, in which the AUC value at the survival time of 5 years was 0.837, 0.755, and 0.803, respectively. Cox regression analysis demonstrated that the risk model was an independent risk predictor of OS (HR > 1, p < 0.05). A nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its robust predictive capacity. KM analysis revealed a significant difference in the subgroup analyses’ survival. Functional enrichment analysis revealed that these autophagy-related signatures were mainly involved in the phagosome and immune-related pathways. Besides, we also found significant differences in immune cell infiltration and immunotherapy targets between risk groups. In conclusion, we built a powerful predictive signature and explored immune components (including immune cells and emerging immunotherapy targets) in LGG.

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An Autophagy-related lncRNA Prognostic Risk Model for Thyroid Cancer

10.21203/rs.3.rs-831976/v1 ◽

2021 ◽

Author(s):

Yanan Shan ◽

Ran He ◽

Xiaowei Yang ◽

Siwen Zang ◽

Shan Yao ◽

...

Keyword(s):

Regression Analysis ◽

Thyroid Cancer ◽

Risk Score ◽

Noncoding Rna ◽

Risk Model ◽

Cox Regression ◽

Endocrine System ◽

The Cancer Genome Atlas ◽

Close Link ◽

Cox Regression Analysis

Abstract Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long noncoding RNA (lncRNA) in thyroid cancer and explored its prognostic value. A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb), respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1166 autophagy-associated lncRNAs. Subsequently we conducted univariate Cox regression analysis and multivariate Cox regression analysis, a nine-autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471, AL162231.2) significantly associated with prognosis was identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer. In this study, a nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.

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Identification of IGF2BP2 as a Prognostic Biomarker and Immunotherapeutic Target Based on Alternative Splicing Events in Glioblastoma

10.21203/rs.3.rs-900457/v1 ◽

2021 ◽

Author(s):

Xin-Yu Li ◽

Lei Hou ◽

Lu-yu Zhang ◽

Xue-yuan Li ◽

xi-tao Yang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Prognostic Model ◽

Prognostic Markers ◽

Risk Model ◽

Cox Regression ◽

Prognostic Biomarker ◽

Low Risk ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Aim: A glioblastoma (GBM) prognostic model was developed with GBM -related alternative splicing (AS) data and prognostic markers were identified. Methods: AS data and clinical data of GBM patients were retrieved from The Cancer Genome Atlas (TCGA) SpliceSeq database and TCGA database, respectively. The data from these two databases were intersected to screen the prognosis-associated AS events, which was subsequently examined in Univariate Cox regression models. To avoid model overfitting, LASSO regression analysis was conducted. On the basis of these AS events, we established a prognostic model of GBM with the use of multivariate Cox regression analysis. On the strength of this model, the patients were assigned into high-risk and low-risk groups with a median risk score as the threshold. Kaplan-Meier survival, receiver operating characteristic (ROC), and calibration curves were applied to evaluate the performance of this model. Finally, combined with the risk model and clinicopathological characteristics, Cox regression analysis was utilized to identify the independent prognostic markers of GBM, and a nomogram was constructed. Results: The AS and clinical data of 169 GBM patients from the TCGA SpliceSeq and TCGA databases were collected. Univariate Cox regression analysis identified 1000 prognosis-related AS events in GBM, and then Lasso regression analysis identified 16 AS events. A GBM prognostic risk model was constructed based on AS events of 7 genes (FAM86B1, ZNF302, C19orf57, RPL39L, CBLL1, RWDD1, IGF2BP2). Through this model, we found lower overall survival (OS) rates of the high-risk population versus the low-risk population (p < 0.05). ROC and calibration curve analyses demonstrated the good ability of this model to predict the OS of GBM patients. Cox regression analysis suggested risk score as an independent prognostic factor for GBM. We also found that IGF2BP2 is associated with patient prognosis and have a strong relationship with immunotherapy response. Conclusion: The prognostic model based on AS events can significantly distinguish the survival rate of high-risk and low-risk GBM patients and IGF2BP2 were identified as a novel prognostic biomarker and immunotherapeutic target.

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Identification of the Pyroptosis-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.771847 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bixian Luo ◽

Jianwei Lin ◽

Wei Cai ◽

Mingliang Wang

Keyword(s):

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Colon Adenocarcinoma ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Cox Regression Analysis ◽

Model Based ◽

Patient Prognosis

The prognosis of advanced colon adenocarcinoma (COAD) remains poor. However, existing methods are still difficult to assess patient prognosis. Pyroptosis, a lytic and inflammatory process of programmed cell death caused by the gasdermin protein, is involved in the development and progression of various tumors. Moreover, there are no related studies using pyroptosis-related genes to construct a model to predict the prognosis of COAD patients. Thus, in this study, bioinformatics methods were used to analyze the data of COAD patients downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to construct a risk model for the patient prognosis. TCGA database was used as the training set, and GSE39582 downloaded from GEO was used as the validation set. A total of 24 pyroptosis-related genes shown significantly different expression between normal and tumor tissues in COAD and seven genes (CASP4, CASP5, CASP9, IL6, NOD1, PJVK, and PRKACA) screened by univariate and LASSO cox regression analysis were used to construct the risk model. The receiver operating characteristic (ROC) and Kaplan–Meier (K–M curves) curves showed that the model based on pyroptosis-related genes can be used to predict the prognosis of COAD and can be validated by the external cohort well. Then, the clinicopathological factors were combined with the risk score to establish a nomogram with a C-index of 0.774. In addition, tissue validation results also showed that CASP4, CASP5, PRKACA, and NOD1 were differentially expressed between tumor and normal tissues from COAD patients. In conclusion, the risk model based on the pyroptosis-related gene can be used to assess the prognosis of COAD patients well, and the related genes may become the potential targets for treatment.

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