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Rosati D, Palmieri M, Brunelli G, Morrione A, Iannelli F, Frullanti E, Giordano A. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Comput Struct Biotechnol J 2024; 23:1154-1168. [PMID: 38510977 PMCID: PMC10951429 DOI: 10.1016/j.csbj.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/22/2024] Open
Abstract
In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.
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Affiliation(s)
- Diletta Rosati
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Cancer Genomics & Systems Biology Lab, Dept. of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Maria Palmieri
- Cancer Genomics & Systems Biology Lab, Dept. of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Giulia Brunelli
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Andrea Morrione
- Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA
| | - Francesco Iannelli
- Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Elisa Frullanti
- Cancer Genomics & Systems Biology Lab, Dept. of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Antonio Giordano
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA
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Liu S, Wang S, Guo J, Wang C, Zhang H, Lin D, Wang Y, Hu X. Crosstalk among disulfidptosis-related lncRNAs in lung adenocarcinoma reveals a correlation with immune profile and clinical prognosis. Noncoding RNA Res 2024; 9:772-781. [PMID: 38590434 PMCID: PMC10999374 DOI: 10.1016/j.ncrna.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/08/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.
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Affiliation(s)
- Shifeng Liu
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Song Wang
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jian Guo
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Congxiao Wang
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hao Zhang
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Dongliang Lin
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuanyong Wang
- Department of Thoracic Surgery, Tangdu Hospital of Air Force Military Medical University, Xi'an, China
| | - Xiaokun Hu
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
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Dong C, Ma H, Mi N, Fu W, Yi J, Gao L, Wang H, Ren Y, Lin Y, Han F, Chen Z, Zhou W. Integrated analysis of scRNA-seq and bulk RNA-seq reveals that GPRC5A is an important prognostic gene in pancreatic cancer and is associated with B-cell Infiltration in pancreatic cancer. Front Oncol 2024; 14:1283164. [PMID: 38634049 PMCID: PMC11021786 DOI: 10.3389/fonc.2024.1283164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/23/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction Pancreatic cancer (PC) is a malignancy with poor prognosis. This investigation aimed to determine the relevant genes that affect the prognosis of PC and investigate their relationship with immune infiltration. Methods : First, we acquired PC single-cell chip data from the GEO database to scrutinize dissimilarities in immune cell infiltration and differential genes between cancerous and adjacent tissues. Subsequently, we combined clinical data from TCGA to identify genes relevant to PC prognosis. Employing Cox and Lasso regression analyses, we constructed a multifactorial Cox prognostic model, which we subsequently confirmed. The prognostic gene expression in PC was authenticated using RT-PCR. Moreover, we employed the TIMER online database to examine the relationship between the expression of prognostic genes and T and B cell infiltration. Additionally, the expression of GPRC5A and its correlation with B cells infiltration and patient prognosis were ascertained in tissue chips using multiple immune fluorescence staining. Results The single-cell analysis unveiled dissimilarities in B-cell infiltration between cancerous and neighboring tissues. We developed a prognostic model utilizing three genes, indicating that patients with high-risk scores experienced a more unfavorable prognosis. Immune infiltration analysis revealed a significant correlation among YWHAZ, GPRC5A, and B cell immune infiltration. In tissue samples, GPRC5A exhibited substantial overexpression and a robust association with an adverse prognosis, demonstrating a positive correlation with B cell infiltration. Conclusion GPRC5A is an independent risk factor in PC and correlated with B cell immune infiltration in PC. These outcomes indicated that GPRC5A is a viable target for treating PC.
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Affiliation(s)
- Chunlu Dong
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Haidong Ma
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
| | - Ningning Mi
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
| | - Wenkang Fu
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
| | - Jianfeng Yi
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- Department of Surgery, The First School of Clinical Medicine of Gansu University of Chinese Medicine, Lanzhou, Gansu, China
| | - Long Gao
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
| | - Haiping Wang
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yanxian Ren
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yanyan Lin
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Fangfang Han
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Zhou Chen
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Wence Zhou
- The First School of Clinical Medicine of Lanzhou University, Lanzhou, Gansu, China
- Lanzhou University Second Hospital, Lanzhou, Gansu, China
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Liu C, Li Z, Zhang Z, Li J, Xu C, Jia Y, Zhang C, Yang W, Wang W, Wang X, Liang K, Peng L, Wang J. Prediction of survival and analysis of prognostic factors for patients with AFP negative hepatocellular carcinoma: a population-based study. BMC Gastroenterol 2024; 24:93. [PMID: 38438972 PMCID: PMC10910698 DOI: 10.1186/s12876-024-03185-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 02/20/2024] [Indexed: 03/06/2024] Open
Abstract
PURPOSE Hepatocellular carcinoma (HCC) has a poor prognosis, and alpha-fetoprotein (AFP) is widely used to evaluate HCC. However, the proportion of AFP-negative individuals cannot be disregarded. This study aimed to establish a nomogram of risk factors affecting the prognosis of patients with AFP-negative HCC and to evaluate its diagnostic efficiency. PATIENTS AND METHODS Data from patients with AFP-negative initial diagnosis of HCC (ANHC) between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and validation. We randomly divided overall cohort into the training or validation cohort (7:3). Univariate and multivariate Cox regression analysis were used to identify the risk factors. We constructed nomograms with overall survival (OS) and cancer-specific survival (CSS) as clinical endpoint events and constructed survival analysis by using Kaplan-Meier curve. Also, we conducted internal validation with Receiver Operating Characteristic (ROC) analysis and Decision curve analysis (DCA) to validate the clinical value of the model. RESULTS This study included 1811 patients (1409 men; 64.7% were Caucasian; the average age was 64 years; 60.7% were married). In the multivariate analysis, the independent risk factors affecting prognosis were age, ethnicity, year of diagnosis, tumor size, tumor grade, surgery, chemotherapy, and radiotherapy. The nomogram-based model related C-indexes were 0.762 (95% confidence interval (CI): 0.752-0.772) and 0.752 (95% CI: 0.740-0.769) for predicting OS, and 0.785 (95% CI: 0.774-0.795) and 0.779 (95% CI: 0.762-0.795) for predicting CSS. The nomogram model showed that the predicted death was consistent with the actual value. The ROC analysis and DCA showed that the nomogram had good clinical value compared with TNM staging. CONCLUSION The age(HR:1.012, 95% CI: 1.006-1.018, P-value < 0.001), ethnicity(African-American: HR:0.946, 95% CI: 0.783-1.212, P-value: 0.66; Others: HR:0.737, 95% CI: 0.613-0.887, P-value: 0.001), tumor diameter(HR:1.006, 95% CI: 1.004-1.008, P-value < 0.001), year of diagnosis (HR:0.852, 95% CI: 0.729-0.997, P-value: 0.046), tumor grade(Grade 2: HR:1.124, 95% CI: 0.953-1.326, P-value: 0.164; Grade 3: HR:1.984, 95% CI: 1.574-2.501, P-value < 0.001; Grade 4: HR:2.119, 95% CI: 1.115-4.027, P-value: 0.022), surgery(Liver Resection: HR:0.193, 95% CI: 0.160-0.234, P-value < 0.001; Liver Transplant: HR:0.102, 95% CI: 0.072-0.145, P-value < 0.001), chemotherapy(HR:0.561, 95% CI: 0.471-0.668, P-value < 0.001), and radiotherapy(HR:0.641, 95% CI: 0.463-0.887, P-value:0.007) were independent prognostic factors for patients with ANHC. We developed a nomogram model for predicting the OS and CSS of patients with ANHC, with a good predictive performance.
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Affiliation(s)
- Chengyu Liu
- Graduate School of Hebei Medical University, Shijiazhuang, Hebei, China
- Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University, 169 Tianshan Street, Shijiazhuang, Hebei, China
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China
| | - Zikang Li
- Graduate School of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhilei Zhang
- Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University, 169 Tianshan Street, Shijiazhuang, Hebei, China
| | - Jinlong Li
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China
| | - Congxi Xu
- Graduate School of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yuming Jia
- Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University, 169 Tianshan Street, Shijiazhuang, Hebei, China
| | - Chong Zhang
- Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University, 169 Tianshan Street, Shijiazhuang, Hebei, China
| | - Wuhan Yang
- Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University, 169 Tianshan Street, Shijiazhuang, Hebei, China
| | - Wenchuan Wang
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China
| | - Xiaojuan Wang
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China
| | - Kuopeng Liang
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China
| | - Li Peng
- Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University, 169 Tianshan Street, Shijiazhuang, Hebei, China.
| | - Jitao Wang
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China.
- Hebei Provincial Key Laboratory of Cirrhosis & Portal Hypertension, 145 Xinhua North Road, Xingtai, Hebei, China.
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Wu X, Li J, Chai S, Li C, Lu S, Bao S, Yu S, Guo H, He J, Peng Y, Sun H, Wang L. Integrated analysis and validation of ferroptosis-related genes and immune infiltration in acute myocardial infarction. BMC Cardiovasc Disord 2024; 24:123. [PMID: 38402377 PMCID: PMC10893752 DOI: 10.1186/s12872-023-03622-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/17/2023] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Acute myocardial infarction (AMI) is indeed a significant cause of mortality and morbidity in individuals with coronary heart disease. Ferroptosis, an iron-dependent cell death, is characterized by the accumulation of intracellular lipid peroxides, which is implicated in cardiomyocyte injury. This study aims to identify biomarkers that are indicative of ferroptosis in the context of AMI, and to examine their potential roles in immune infiltration. METHODS Firstly, the GSE59867 dataset was used to identify differentially expressed ferroptosis-related genes (DE-FRGs) in AMI. We then performed gene ontology (GO) and functional enrichment analysis on these DE-FRGs. Secondly, we analyzed the GSE76591 dataset and used bioinformatic methods to build ceRNA networks. Thirdly, we identified hub genes in protein-protein interaction (PPI) network. After obtaining the key DE-FRGs through the junction of hub genes with ceRNA and least absolute shrinkage and selection operator (LASSO). ImmucellAI was applied to estimate the immune cell infiltration in each sample and examine the relationship between key DE-FRGs and 24 immunocyte subsets. The diagnostic performance of these genes was further evaluated using the receiver operating characteristic (ROC) curve analysis. Ultimately, we identified an immune-related ceRNA regulatory axis linked to ferroptosis in AMI. RESULTS Among 56 DE-FRGs identified in AMI, 41 of them were integrated into the construction of competitive endogenous RNA (ceRNA) networks. TLR4 and PIK3CA were identified as key DE-FRGs and PIK3CA was confirmed as a diagnostic biomarker for AMI. Moreover, CD4_native cells, nTreg cells, Th2 cells, Th17 cells, central-memory cells, effector-memory cells, and CD8_T cells had higher infiltrates in AMI samples compared to control samples. In contrast, exhausted cells, iTreg cells, and Tfh cells had lower infiltrates in AMI samples. Spearman analysis confirmed the correlation between 24 immune cells and PIK3CA/TLR4. Ultimately, we constructed an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA. CONCLUSION Our comprehensive analysis has identified PIK3CA as a robust and promising biomarker for this condition. Moreover, we have also identified an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA, which may play a key role in regulating ferroptosis during AMI progression.
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Affiliation(s)
- Xinyu Wu
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jingru Li
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shengjie Chai
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chaguo Li
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Si Lu
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Suli Bao
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuai Yu
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Hao Guo
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jie He
- Department of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yunzhu Peng
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Huang Sun
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Luqiao Wang
- Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
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Zhong J, Kong Y, Li R, Feng M, Li L, Zhu X, Chen L. Identification and Functional Characterization of PI3K/Akt/mTOR Pathway-Related lncRNAs in Lung Adenocarcinoma: A Retrospective Study. CELL JOURNAL 2024; 26:13-27. [PMID: 38351726 PMCID: PMC10864771 DOI: 10.22074/cellj.2023.2007918.1378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/08/2023] [Accepted: 11/18/2023] [Indexed: 02/18/2024]
Abstract
OBJECTIVE This paper aimed to investigate the PI3K/Akt/mTOR signal-pathway regulator factor-related lncRNA signatures (PAM-SRFLncSigs), associated with regulators of the indicated signaling pathway in patients with lung adenocarcinoma (LUAD) undergoing immunotherapy. MATERIALS AND METHODS In this retrospective study, we employed univariate Cox, multivariate Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses to identify prognostically relevant long non-coding RNAs (lncRNAs), construct prognostic models, and perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Subsequently, immunoassay and chemotherapy drug screening were conducted. Finally, the prognostic model was validated using the Imvigor210 cohort, and tumor stem cells were analyzed. RESULTS We identified seven prognosis-related lncRNAs (AC084757.3, AC010999.2, LINC02802, AC026979.2, AC024896.1, LINC00941 and LINC01312). We also developed prognostic models to predict survival in patients with LUAD. KEGG enrichment analysis confirmed association of LUAD with the PI3K/Akt/mTOR signaling pathway. In the analysis of immune function pathways, we discovered three good prognostic pathways (Cytolytic_activity, Inflammation-promoting, T_cell_co-inhibition) in LUAD. Additionally, we screened 73 oncology chemotherapy drugs using the "pRRophetic" algorithm. CONCLUSION Identification of seven lncRNAs linked to regulators of the PI3K/Akt/mTOR signaling pathway provided valuable insights into predicting the prognosis of LUAD, understanding the immune microenvironment and optimizing immunotherapy strategies.
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Affiliation(s)
- Jiaqi Zhong
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Ying Kong
- Department of Clinical Laboratory, The Third People's Hospital of Hubei Province, Wuhan, China
| | - Ruming Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Minghan Feng
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Liming Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China.
| | - Lianzhou Chen
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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Chhoa H, Chabriat H, Chevret S, Biard L. Comparison of models for stroke-free survival prediction in patients with CADASIL. Sci Rep 2023; 13:22443. [PMID: 38105268 PMCID: PMC10725863 DOI: 10.1038/s41598-023-49552-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 12/09/2023] [Indexed: 12/19/2023] Open
Abstract
Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, which is caused by mutations of the NOTCH3 gene, has a large heterogeneous progression, presenting with declines of various clinical scores and occurrences of various clinical event. To help assess disease progression, this work focused on predicting the composite endpoint of stroke-free survival time by comparing the performance of Cox proportional hazards regression to that of machine learning models using one of four feature selection approaches applied to demographic, clinical and magnetic resonance imaging observational data collected from a study cohort of 482 patients. The quality of the modeling process and the predictive performance were evaluated in a nested cross-validation procedure using the time-dependent Brier Score and AUC at 5 years from baseline, the former measuring the overall performance including calibration and the latter highlighting the discrimination ability, with both metrics taking into account the presence of right-censoring. The best model for each metric was the componentwise gradient boosting model with a mean Brier score of 0.165 and the random survival forest model with a mean AUC of 0.773, both combined with the LASSO feature selection method.
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Affiliation(s)
- Henri Chhoa
- ECSTRRA Team, Université Paris Cité, UMR1153, INSERM, Paris, France
| | - Hugues Chabriat
- Centre NeuroVasculaire Translationnel - Centre de Référence CERVCO, DMU NeuroSciences, Hôpital Lariboisière, GHU APHP-Nord, Université Paris Cité, Paris, France
- INSERM NeuroDiderot UMR 1141, GenMedStroke Team, Paris, France
| | - Sylvie Chevret
- ECSTRRA Team, Université Paris Cité, UMR1153, INSERM, Paris, France
| | - Lucie Biard
- ECSTRRA Team, Université Paris Cité, UMR1153, INSERM, Paris, France.
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Ding T, Li X, Mo J, Alexander G, Li J. Recurrence risk stratification of hepatocellular carcinomas based on immune gene expression and features extracted from pathological images. PLoS Comput Biol 2023; 19:e1011716. [PMID: 38157378 PMCID: PMC10783785 DOI: 10.1371/journal.pcbi.1011716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 01/11/2024] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Immune-based therapy is a promising type of treatment for hepatocellular carcinoma (HCC) but has only been partially successful due to the high heterogeneity in HCC tumor. The differences in the degree of tumor cell progression and in the activity of tumor immune microenvironment could lead to varied clinical outcome. Accurate subgrouping for recurrence risk is an approach to address the issue of such heterogeneity. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome. METHODS We included a total of 231 patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project. For each patient, we extracted 18 statistical metrics corresponding to a global region of interest and 135 features regarding nucleus shape from WSI. A risk score was developed using these image features with high-dimensional survival modeling. We also introduced into the model the expression profile of 66 representative marker genes relevant to currently available immunotherapies. We stratified all patients into higher and lower-risk subgroup based on the final risk score selected from multiple models generated, and further investigated underlying molecular mechanisms associated with the risk stratification. RESULTS One WSI feature and three immune marker genes were selected into the final recurrence-free survival (RFS) prediction model following the best integrated modeling framework. The resultant score showed a significantly improved prediction performance on the test dataset (mean time-dependent AUCs = 0.707) as compared to those of other types (e.g: mean time-dependent AUCs of AJCC tumor stage = 0.525) of input data integration. To assess that the risk score could provide a higher-resolution risk stratification, a lower-risk subgroup (or a higher-risk subgroup) was arbitrarily assigned according to score falling below (or above) the median score. The lower risk subgroup had significantly longer median RFS time than that of the higher-risk patients (median RFS = 903 vs. 265 days, log-rank test p-value< 0.0001). Additionally, the higher-risk subgroup, in contrast to the lower-risk patients were characterized with a significant downregulation of immune checkpoint genes, suppressive signal in tumor immune response pathways, and depletion of CD8 T cells. These observations for the higher-risk subgroup suggest that new targets for adoptive or checkpoint-based combined systemic therapies may be useful. CONCLUSION We developed a novel prognostic model to predict RFS for HCC patients, using one feature that can be automatically extracted from routine histopathological images, as well as the expression profiles of three immune marker genes. The methodology used in this paper demonstrates the feasibility of developing prognostic models that provide both useful risk stratification along with valuable biological insights into the underlying characteristics of the subgroups identified.
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Affiliation(s)
- Tao Ding
- Department of Statistical Science, University College London, London, United Kingdom
| | - Xiao Li
- Product Development Personalized Healthcare, Genentech, San Francisco, California, United States of America
| | - Jiu Mo
- Department of Computer Science, Central South University of Forestry and Technology, Changsha, Hunan, People’s Republic of China
| | - Gregory Alexander
- Mathematical Statistician Consultant, San Francisco, California, United States of America
| | - Jialu Li
- Mathematical Statistician Consultant, San Francisco, California, United States of America
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9
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Zhang S, Yang F, Wang L, Si S, Zhang J, Xue F. Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model. PLoS Comput Biol 2023; 19:e1011396. [PMID: 37733837 PMCID: PMC10569718 DOI: 10.1371/journal.pcbi.1011396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/12/2023] [Accepted: 07/26/2023] [Indexed: 09/23/2023] Open
Abstract
Personalized prediction of chronic diseases is crucial for reducing the disease burden. However, previous studies on chronic diseases have not adequately considered the relationship between chronic diseases. To explore the patient-wise risk of multiple chronic diseases, we developed a multitask learning Cox (MTL-Cox) model for personalized prediction of nine typical chronic diseases on the UK Biobank dataset. MTL-Cox employs a multitask learning framework to train semiparametric multivariable Cox models. To comprehensively estimate the performance of the MTL-Cox model, we measured it via five commonly used survival analysis metrics: concordance index, area under the curve (AUC), specificity, sensitivity, and Youden index. In addition, we verified the validity of the MTL-Cox model framework in the Weihai physical examination dataset, from Shandong province, China. The MTL-Cox model achieved a statistically significant (p<0.05) improvement in results compared with competing methods in the evaluation metrics of the concordance index, AUC, sensitivity, and Youden index using the paired-sample Wilcoxon signed-rank test. In particular, the MTL-Cox model improved prediction accuracy by up to 12% compared to other models. We also applied the MTL-Cox model to rank the absolute risk of nine chronic diseases in patients on the UK Biobank dataset. This was the first known study to use the multitask learning-based Cox model to predict the personalized risk of the nine chronic diseases. The study can contribute to early screening, personalized risk ranking, and diagnosing of chronic diseases.
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Affiliation(s)
- Shuaijie Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China
| | - Fan Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China
| | - Lijie Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China
| | - Shucheng Si
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China
| | - Jianmei Zhang
- Department of Geriatrics, Weihai Municipal Hospital Affiliated Shandong University, 76 Heping Rd, Weihai, Shandong, China
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China
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Liu T, Chen X, Peng B, Liang C, Zhang H, Wang S. A novel prognostic model based on immunogenic cell death-related genes for improved risk stratification in hepatocellular carcinoma patients. J Cancer Res Clin Oncol 2023; 149:10255-10267. [PMID: 37269346 DOI: 10.1007/s00432-023-04950-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/26/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE Hepatocellular carcinoma (HCC) is a prevalent primary malignant tumor with increasing incidence and mortality rates in recent years. The treatment options for advanced HCC are very limited. Immunogenic cell death (ICD) plays an important role in cancer, in particular immunotherapy. However, the specific ICD genes and their prognostic values in HCC remain to be investigated. METHODS The TCGA-LIHC datasets were obtained from TCGA database, LIRI-JP datasets were obtained from ICGC database, and immunogenic cell death (ICD) genes datasets were obtained from previous literature. WGCNA analysis identifies ICD-related genes. Functional analysis was used to investigate the biological characteristics of ICD-related genes. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select prognostic ICD-related genes and construct a prognostic risk score. Prognostic independence of ICD risk scores was determined by univariate and multivariate Cox regression analyses. A nomogram was then constructed and the diagnostic value was assessed using decision curve analysis. Immune infiltration analysis and drug sensitivity analysis were used to investigate immune cell enrichment and drug response in HCC patients classified as low or high risk based on their risk score. RESULTS Most of the ICD genes were differentially expressed in normal and HCC patients, and some ICD genes were differentially expressed in different clinical groups. A total of 185 ICD-related genes were identified by WGCNA. Prognostic ICD-related genes were selected using a univariate Cox analysis. A model comprising nine prognosis ICD-related gene biomarkers was developed. Patients was divided into high-risk and low-risk groups, and patients in high-risk groups had poorer outcomes. Meanwhile, the reliability of the model was verified by external independent data. The Independent prognostic value of the risk score in HCC was investigated by univariate and multivariate Cox analyses. Diagnostic nomogram was constructed to predict prognosis. Through immune infiltration analysis, we found that some innate and adaptive immune cells were significantly different between low- and high-risk groups. CONCLUSION We developed and validated a novel prognostic predictive classification system for HCC based on nine ICD-related genes. In addition, immune-related predictions and model could help predict the outcomes of HCC and could provide a reference for clinical practice.
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Affiliation(s)
- Tianliang Liu
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Advanced Medical Technology Center, the First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Xiaonan Chen
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Advanced Medical Technology Center, the First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Baozhou Peng
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Advanced Medical Technology Center, the First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Chen Liang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Advanced Medical Technology Center, the First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Advanced Medical Technology Center, the First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Shuaiyu Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China.
- Department of Obstetrics, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
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Lu Y, Zhang H, Pan H, Zhang Z, Zeng H, Xie H, Yin J, Tang W, Lin R, Zeng C, Cai D. Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages. J Transl Med 2023; 21:339. [PMID: 37217897 DOI: 10.1186/s12967-023-04173-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/30/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Disruption of N6 methyl adenosine (m6A) modulation hampers gene expression and cellular functions, leading to various illnesses. However, the role of m6A modification in osteoarthritis (OA) synovitis remains unclear. This study aimed to explore the expression patterns of m6A regulators in OA synovial cell clusters and identify key m6A regulators that mediate synovial macrophage phenotypes. METHODS The expression patterns of m6A regulators in the OA synovium were illustrated by analyzing bulk RNA-seq data. Next, we built an OA LASSO-Cox regression prediction model to identify the core m6A regulators. Potential target genes of these m6A regulators were identified by analyzing data from the RM2target database. A molecular functional network based on core m6A regulators and their target genes was constructed using the STRING database. Single-cell RNA-seq data were collected to verify the effects of m6A regulators on synovial cell clusters. Conjoint analyses of bulk and single-cell RNA-seq data were performed to validate the correlation between m6A regulators, synovial clusters, and disease conditions. After IGF2BP3 was screened as a potential modulator in OA macrophages, the IGF2BP3 expression level was tested in OA synovium and macrophages, and its functions were further tested by overexpression and knockdown in vitro. RESULTS OA synovium showed aberrant expression patterns of m6A regulators. Based on these regulators, we constructed a well-fitting OA prediction model comprising six factors (FTO, YTHDC1, METTL5, IGF2BP3, ZC3H13, and HNRNPC). The functional network indicated that these factors were closely associated with OA synovial phenotypic alterations. Among these regulators, the m6A reader IGF2BP3 was identified as a potential macrophage mediator. Finally, IGF2BP3 upregulation was verified in the OA synovium, which promoted macrophage M1 polarization and inflammation. CONCLUSIONS Our findings revealed the functions of m6A regulators in OA synovium and highlighted the association between IGF2BP3 and enhanced M1 polarization and inflammation in OA macrophages, providing novel molecular targets for OA diagnosis and treatment.
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Affiliation(s)
- Yuheng Lu
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Hongbo Zhang
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Haoyan Pan
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Zhicheng Zhang
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Hua Zeng
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Haoyu Xie
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Jianbin Yin
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Wen Tang
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Rengui Lin
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Chun Zeng
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China.
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China.
| | - Daozhang Cai
- Department of Orthopedics, Academy of Orthopedics, Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510515, China.
- Department of Joint Surgery, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China.
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Shi H, Zhong F, Yi X, Shi Z, Ou F, Zuo Y, Xu Z. The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis-Trans Isomerase Gene Signature in Hepatocellular Carcinoma. Front Genet 2021; 12:730141. [PMID: 34887898 PMCID: PMC8650315 DOI: 10.3389/fgene.2021.730141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma. Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score. Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal. Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.
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Affiliation(s)
- Huadi Shi
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Fulan Zhong
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Xiaoqiong Yi
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Zhenyi Shi
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Feiyan Ou
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Yufang Zuo
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Zumin Xu
- Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
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