Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

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A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1871520621666210713112630 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yongmei Wang ◽

Guimin Zhang ◽

Ruixian Wang

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Differential Expression ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Differential Expression Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

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5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.775304 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ke Wang ◽

Weibo Zhong ◽

Zining Long ◽

Yufei Guo ◽

Chuanfan Zhong ◽

...

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Biochemical Recurrence ◽

Prognostic Model ◽

Cox Regression ◽

Functional Enrichment ◽

Lasso Regression ◽

Clinical Value ◽

Cox Regression Analysis

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

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Construction of a Prognostic Model for Predicting Overall Survival of Patients with Colorectal Cancer

10.21203/rs.3.rs-26225/v1 ◽

2020 ◽

Author(s):

Shuwen Han ◽

Kefeng Ding

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Levulinic Acid ◽

Prognostic Model ◽

Cox Regression ◽

Aminolevulinic Acid ◽

Risk Group ◽

Prognostic Models ◽

Cox Regression Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies. The purpose of this study is to construct a prognostic model for predicting the overall survival (OS) in patients with CRC. Methods: The mRNA-seq and miRNA-seq data of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) between tumor and normal tissues were screened. The Kaplan-Meier and univariate Cox regression analysis were used to screen the survival-related genes. Functional enrichment analysis of survival-related genes was conducted, followed by protein-protein interaction (PPI) analysis. Subsequently, the potential drugs targeting differentially expressed mRNAs (DE-mRNAs) were investigated. Multivariate Cox regression analysis was then conducted to screen the independent prognostic factors, and these genes were used to establish a prognostic model. A receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) value of ROC was calculated to evaluate the specificity and sensitivity of the model. Results: A total of 855 survival-related genes were screened. These genes were mainly enriched in Gene Ontology (GO) terms, such as methylation, synapse organization, and methyltransferase activity; and pathway analysis showed that these genes were significantly involved in N-Glycan biosynthesis and the calcium signaling pathway. PPI analysis showed that aminolevulinate dehydratase (ALAD) and cholinergic receptor muscarinic 2 (CHRM2) served vital roles in the development of CRC. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for CRC treatment. The prognostic models were built and the patients were divided into high-risk and low-risk groups based on the median of risk score (RS) as screening threshold. The OS for patients in the high-risk group was markedly shorter than that for patients in the low-risk group. Meanwhile, kazal type serine peptidase inhibitor domain 1 (KAZALD1), hippocalcin like 4 (HPCAL4), cadherin 8 (CDH8), synaptopodin 2 (SYNPO2), cyclin D3 (CCND3), and hsa_mir_26b may be independent prognostic factors that could be considered as therapeutic targets for CRC.Conclusion: We established prognostic models that could predict the OS for CRC patients and may assist clinicians in providing personalized and precision treatment in this patient population.Highlights:1. ALAD served a vital role in the development of CRC.2. CHRM2 played a role in CRC development by affecting the calcium signaling pathway.3. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for treating CRC.4. KAZALD1 and HPCAL4 were associated with the OS of CRC.5. CDH8, SYNPO2, CCND3, and hsa-mir-26b were closely related to the prognostic of CRC staging.

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Development of a prognostic model based on an immunogenomic landscape analysis of colorectal cancer

Future Oncology ◽

10.2217/fon-2020-0022 ◽

2020 ◽

Author(s):

HJ Li ◽

YL Wang ◽

L Ming ◽

XQ Guo ◽

YL Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Regression Analysis ◽

Prognostic Model ◽

Operating Characteristic ◽

Cox Regression ◽

Area Under The Curve ◽

Patient Needs ◽

Cox Regression Analysis ◽

Immune Related Genes

Screening and therapeutic programs for colorectal cancer (CRC) are invasive or not effective and unable to meet patient needs. Major advances in immunogenomics may change this status but need more exploration. Differentially expressed genes and immune-related genes (IRGs) were identified by computational methods. A prognostic model was established and validated based on survival-related IRGs via stepwise multivariate Cox regression analysis. Nine IRGs were selected and identified as survival-related genes. A 7-gene prognostic model could offer a preliminary and valid determination of risk in CRC patients. The area under the curve of the receiver operating characteristic was 0.672. The 7-gene prognostic model might be used as a novel prognostic tool in CRC patients.

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A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

BioMed Research International ◽

10.1155/2021/6669570 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xiao-Yan Huang ◽

Wen-Tao Qin ◽

Qi-Sheng Su ◽

Cheng-Cheng Qiu ◽

Ruo-Chuan Liu ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Key Genes

Objective. This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). Methods. The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index ( C -index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. Results. A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C -index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. Conclusion. The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

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Identification of a stemness-related prognostic model in Pancreatic ductal adenocarcinoma by Weighted gene co-expression network analysis

10.21203/rs.3.rs-245344/v1 ◽

2021 ◽

Author(s):

Xiao-Yan Huang ◽

Wen-Tao Qin ◽

Qi-Sheng Su ◽

Cheng-Cheng Qiu ◽

Ruo-Chuan Liu ◽

...

Keyword(s):

Network Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Cox Proportional Hazards Model ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis ◽

Key Genes

Abstract Objective: Aim of this study was to identify the stemness-related genes in Pancreatic ductal adenocarcinoma (PDAC). Methods: The RNA-seq data of PADC were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. mRNA expression base-index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC were evaluated. The mRNAsi-based gene sets in PADC were identified by Weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analyses were performed with key genes. Kaplan–Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognosis-associated hub genes were applied to establish nomograms. The receiver operating characteristic curves (ROC) and concordance index (C-index) were utilized to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Results: 36 key genes associate with mRNAsi were screened via WGCNA. Next a five-gene signature compromised TPX2, NCAPH, UBE2C, CCNB2, CEP55. Based on the expression of the signature, the PADC patients were classify patients into high- and low-risk groups. Cox regression analysis revealed that the high-risk group was significantly positive with overall survival (OS). Moreover, the nomogram has better sensitivity and specificity for predicting the OS. And the ROC, C-index indicated good performance of the prognostic signature in the TCGA and GEO dataset. Conclusion: Prognostic model associated with cancer stem cells (CSCs) reliably predict OS in PADC. this might be beneficial for the treatment of PADC patients.

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Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

PeerJ ◽

10.7717/peerj.11219 ◽

2021 ◽

Vol 9 ◽

pp. e11219

Author(s):

Yandong Miao ◽

Hongling Zhang ◽

Bin Su ◽

Jiangtao Wang ◽

Wuxia Quan ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Diagnosis And Prognosis

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-97188/v1 ◽

2020 ◽

Author(s):

Xiaohong - Liu ◽

Qian - Xu ◽

Zi-Jing - Li ◽

Bin - Xiong

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Prognostic Significance ◽

Metabolic Reprogramming ◽

Cox Regression Analysis ◽

Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

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Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

10.21203/rs.3.rs-714025/v1 ◽

2021 ◽

Author(s):

Rui Feng ◽

Jian Li ◽

Weiling Xuan ◽

Hanbo Liu ◽

Dexin Cheng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differential Expression ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Univariate Analysis ◽

Differential Expression Analysis ◽

Cox Regression Analysis ◽

Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

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