An Independent Prognostic Model Based on Ten Autophagy-Related Genes in Pancreatic Cancer

Abstract Purpose Pancreatic cancer(PC) is a common cancer with high lethality and low survival rate. Autophagy is involved in the biological process of PC. Thus, we intended to explore the function of autophagy-related long noncoding RNA signature for survival assessment in PC. Methods Based on 10 autophagy-related lncRNAs, the prognostic model was attained through univariate and multivariate Cox regression analysis. Subsequently, the relationship network of 10 lncRNAs was crystallized in co-expression network and Sankey diagram. Survival analysis and ROC curve were used to evaluate the signature. GSEA was utilized to screen enriched gene sets. Result The OS has significant deference in low-risk group and high-risk group(P < 0.001). The ROC curve further proved the potential utility of the signature(AUC = 0.815). GSEA was significantly enriched in cancer-related gene sets. Conclusion The signature has potential to evaluate clinical prognosis in PC. The 10 autophagy-related lncRNAs may achieve great development for PC in target therapy field.

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Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.796729 ◽

2021 ◽

Vol 9 ◽

Author(s):

Peng Gu ◽

Lei Zhang ◽

Ruitao Wang ◽

Wentao Ding ◽

Wei Wang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

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A new prognostic model for pancreatic cancer based on pyroptosis-related genes

10.1101/2021.11.08.467376 ◽

2021 ◽

Author(s):

Liukai Ma ◽

Ruoling Jia ◽

Mengyao Wang ◽

Cheng Yan

Keyword(s):

Pancreatic Cancer ◽

Prognostic Model ◽

Cox Regression ◽

Malignant Tumors ◽

Risk Group ◽

High Risk Group ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Normal Tissues ◽

New Perspective

Pancreatic cancer is one of the most common malignant tumors of the digestive tract. It is known as the "king of cancer" in the field of cancer, and is one of the worst prognosis malignant tumors. pyroptosis is a kind of programmed cell death, which can promote the inflammatory response of cells. Studies have shown that the effect of pyroptosis-related genes in cancer is significant. However, the role of pyroptosis in pancreatic cancer is not clear. The aim of this study is to establish a prognostic model based on pyroptosis. The gene expression and clinical data of pancreatic cancer patients were obtained from TCGA and verified in GEO. The differential expression of 33 pyroptosis-related genes in pancreatic cancer and normal tissues was analyzed, of which 6 genes were up-regulated and 12 genes were down regulated. Then, it was analyzed that pyroptosis-related genes were mainly enriched in the defense against bacteria and pyroptosis pathways. A concise and reliable model is established by lasso-cox regression analysis. Km curve shows that there are differences between high-risk group and low-risk group. And the nomogram has reliable prediction ability. In conclusion, pyroptosis an important role in pancreatic cancer, which can be used for the prediction of pancreatic cancer and provide a new perspective for the treatment of pancreatic cancer.

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A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

10.21203/rs.3.rs-892534/v1 ◽

2021 ◽

Author(s):

Shaopei Ye ◽

Wenbin Tang ◽

Ke Huang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

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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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A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02046-9 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Shigehisa Kubota ◽

Tetsuya Yoshida ◽

Susumu Kageyama ◽

Takahiro Isono ◽

Takeshi Yuasa ◽

...

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

Treatment Strategies ◽

Immunohistochemical Analysis ◽

Cancer Specific Survival ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Model Based ◽

Novel Biomarkers ◽

Metastatic Rcc

Abstract Background Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated. Results Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

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Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

Frontiers in Genetics ◽

10.3389/fgene.2021.739470 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jiahui Pan ◽

Xinyue Zhang ◽

Xuedong Fang ◽

Zhuoyuan Xin

Keyword(s):

Gastric Cancer ◽

Cancer Patients ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

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Clinical Prognostic Model of Autophagy-Related LncRNA Genes in The Esophageal Adenocarcinoma (EAC) to Predicting Overall Survival (OS) of The Patients：The Evidence From Bioinformatic Analysis

10.21203/rs.3.rs-147326/v1 ◽

2021 ◽

Author(s):

Liusheng Wu ◽

Xiaoqiang Li ◽

Jixian Liu ◽

Da Wu ◽

Dingwang Wu ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Esophageal Adenocarcinoma ◽

Clinical Data ◽

Roc Curve ◽

Prognostic Model ◽

Cox Regression ◽

Enrichment Analysis ◽

Prognostic Models ◽

Cox Regression Analysis

Abstract Objective: Autophagy-related LncRNA genes play a vital role in the development of esophageal adenocarcinoma.Our study try to construct a prognostic model of autophagy-related LncRNA esophageal adenocarcinoma, and use this model to calculate patients with esophageal adenocarcinoma. The survival risk value of esophageal adenocarcinoma can be used to evaluate its survival prognosis. At the same time, to explore the sites of potential targeted therapy genes to provide valuable guidance for the clinical diagnosis and treatment of esophageal adenocarcinoma.Methods: Our study have downloaded 261 samples of LncRNA-related transcription and clinical data of 87 patients with esophageal adenocarcinoma from the TCGA database, and 307 autophagy-related gene data from www.autuphagy.com. We applied R software (Version 4.0.2) for data analysis, merged the transcriptome LncRNA genes, autophagy-related genes and clinical data, and screened autophagy LncRNA genes related to the prognosis of esophageal adenocarcinoma. We also performed KEGG and GO enrichment analysis and GSEA enrichment analysis in these LncRNA genes to analysis the risk characteristics and bioinformatics functions of signal transduction pathways. Univariate and multivariate Cox regression analysis were used to determine the correlation between autophagy-related LncRNA and independent risk factors. The establishment of ROC curve facilitates the evaluation of the feasibility of predicting prognostic models, and further studies the correlation between autophagy-related LncRNA and the clinical characteristics of patients with esophageal adenocarcinoma. Finally, we also used survival analysis, risk analysis and independent prognostic analysis to verify the prognosis model of esophageal adenocarcinoma.Results: We screened and identified 22 autophagic LncRNA genes that are highly correlated with the overall survival (OS) of patients with esophageal adenocarcinoma. The area under the ROC curve（AUC=0.941）and the calibration curve have a good lineup, which has statistical analysis value. In addition, univariate and multivariate Cox regression analysis showed that the autophagy LncRNA feature of this esophageal adenocarcinoma is an independent predictor of esophageal adenocarcinoma.Conclusion: These LncRNA screened and identified may participate in the regulation of cellular autophagy pathways, and at the same time affect the tumor development and prognosis of patients with esophageal adenocarcinoma. These results indicate that risk signature and nomogram are important indicators related to the prognosis of patients with esophageal adenocarcinoma.

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Expression Status and Prognostic Value of m6A RNA Methylation Regulators in Lung Adenocarcinoma

10.21203/rs.3.rs-120253/v1 ◽

2020 ◽

Author(s):

Rui Wang ◽

Zian Feng ◽

Jie Hu ◽

Xiaodong He ◽

Zuojun Shen

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Cox Regression Analysis ◽

Rna Methylation ◽

Kaplan Meier ◽

Prediction Efficiency

Abstract Background: N6-methyladenosine (m6A) RNA modification is the most abundant modification method in mRNA, and it plays an important role in the occurrence and development of many cancers. However, data on the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) are still lacking. This paper mainly discusses the role of m6A RNA methylation regulators in LUAD, to identify novel prognostic biomarkers.Methods: The gene expression data of 19 m6A methylation regulator in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm were performed to construct a risk signature and evaluated its prognostic prediction efficiency by using the receiver operating characteristic (ROC) curve. The risk score of each patient was calculated according to the risk signature, and LUAD patients were divided into high-risk group and low-risk group. Kaplan-Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of risk signature. Finally, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the differential signaling pathways and cellular processes between the two groups.Results: The expression of 15 m6A RNA methylation regulators in LUAD tissues was significantly different than that in normal tissues. YTHDF3, YTHDF2, KIAA1429, HNRNPA2B1, RBM15, METTL3, HNRNPC, YTHDF1, IGF2BP2, IGF2BP3, IGF2BP1 were significantly up-regulated in LUAD, and the expressions of FTO, ZC3H13, WTAP, and METL14 were significantly down-regulated. We selected IGF2BP1, HNRNPC, and HNRNPA2B1 to construct the risk signature. ROC curve indicated the area under the curve (AUC) was 0.659, which means the risk signature had a good prediction efficiency. The results of Kaplan-Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD.Conclusions: The m6A RNA methylation regulators IGF2BP1, HNRNPC, and HNRNPA2B1 have a significant correlation with the clinicopathological characteristics of LUAD, which may be a promising prognostic feature and clinical treatment target.

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Identification of Prognostic Biomarkers of Cutaneous Melanoma Based on Analysis of Tumor Mutation Burden

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/8836493 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Jiaqiong Lin ◽

Yan Lin ◽

Zena Huang ◽

Xiaoyong Li

Keyword(s):

Cutaneous Melanoma ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Predictive Biomarkers ◽

Tumor Mutation Burden ◽

Skin Development ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Mutation Burden

Background. Immunotherapy offers a novel approach for the treatment of cutaneous melanoma, but the clinical efficiency varies for individual patients. In consideration of the high cost and adverse effects of immunotherapy, it is essential to explore the predictive biomarkers of outcomes. Recently, the tumor mutation burden (TMB) has been proposed as a predictive prognosticator of the immune response. Method. RNA-seq and somatic mutation datasets of 472 cutaneous melanoma patients were downloaded from The Cancer Genome Atlas (TCGA) database to analyze mutation type and TMB. Differently expressed genes (DEGs) were identified for functional analysis. TMB-related signatures were identified via LASSO and multivariate Cox regression analysis. The association between mutants of signatures and immune cells was evaluated from the TIMER database. Furthermore, the Wilcox test was applied to assess the difference in immune infiltration calculated by the CIBERSORT algorithm in risk groupings. Results. C>T substitutions and TTN were the most common SNV and mutated gene, respectively. Patients with low TMB presented poor prognosis. DEGs were mainly implicated in skin development, cell cycle, DNA replication, and immune-associated crosstalk pathways. Furthermore, a prognostic model consisting of eight TMB-related genes was developed, which was found to be an independent risk factor for treatment outcome. The mutational status of eight TMB-related genes was associated with a low level of immune infiltration. In addition, the immune infiltrates of CD4+ and CD8+ T cells, NK cells, and M1 macrophages were higher in the low-risk group, while those of M0 and M2 macrophages were higher in the high-risk group. Conclusion. Our study demonstrated that a higher TMB was associated with favorable survival outcome in cutaneous melanoma. Moreover, a close association between prognostic model and immune infiltration was identified, providing a new potential target for immunotherapy.

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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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