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Hua T, Liu DX, Zhang XC, Li ST, Wu JL, Zhao Q, Chen SB. Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq. Eur J Med Res 2024; 29:358. [PMID: 38970067 PMCID: PMC11225302 DOI: 10.1186/s40001-024-01948-8] [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: 07/23/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.
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Affiliation(s)
- Tian Hua
- Department of Gynecology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Deng-Xiang Liu
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China
| | - Xiao-Chong Zhang
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China
| | - Shao-Teng Li
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China
| | - Jian-Lei Wu
- Department of Gynecological Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250021, People's Republic of China
| | - Qun Zhao
- The Third Department of Surgery , Hebei Medical University, Fourth Hospital, Road Jiankang No. 12, Hebei, 050001, People's Republic of China.
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China.
| | - Shu-Bo Chen
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China.
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Ba Y, Liu S, Wei Z, Zhao N, Qiao T, Ren Y, Li L, Zhang Y, Weng S, Xu H, Li C, Ge X, Han X. Pyroptosis-Derived Long Noncoding RNA Profiles Reveal a Novel Signature for Evaluating the Prognosis of Patients With Lung Adenocarcinoma. JCO Precis Oncol 2024; 8:e2300405. [PMID: 38547420 PMCID: PMC10994429 DOI: 10.1200/po.23.00405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/11/2023] [Accepted: 02/07/2024] [Indexed: 04/02/2024] Open
Abstract
PURPOSE Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD). MATERIALS AND METHODS A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms. RESULTS In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts. CONCLUSION We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets.
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Affiliation(s)
- Yuhao Ba
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shutong Liu
- The Medical School of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Zhengpan Wei
- The Medical School of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Nannan Zhao
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Tong Qiao
- Department of Thoracic Surgery, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yuqing Ren
- Department of Respiratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lifeng Li
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chunwei Li
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoyong Ge
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Interventional Institute of Zhengzhou University, Zhengzhou, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, China
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Ye L, Jiang Z, Zheng M, Pan K, Lian J, Ju B, Liu X, Tang S, Guo G, Zhang S, Hong X, Lu W. Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma. Epigenomics 2024; 16:309-329. [PMID: 38356435 DOI: 10.2217/epi-2023-0388] [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] [Indexed: 02/16/2024] Open
Abstract
Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.
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Affiliation(s)
- Lele Ye
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Zhuofeng Jiang
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Mengxia Zheng
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Kan Pan
- First Clinical College, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jingru Lian
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Bing Ju
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Xuefei Liu
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Sangsang Tang
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Gangqiang Guo
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research & Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens & Immunity, Department of Microbiology & Immunology, Institute of Molecular Virology & Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Songfa Zhang
- Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Xin Hong
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
- Key University Laboratory of Metabolism & Health of Guangdong, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
- Guangdong Provincial Key Laboratory of Cell Microenvironment & Disease Research, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Weiguo Lu
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
- Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
- Center of Uterine Cancer Diagnosis & Therapy of Zhejiang Province, Hangzhou, 310006, Zhejiang, China
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Zheng J, Jiang S, Lin X, Wang H, Liu L, Cai X, Sun Y. Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer. BMC Womens Health 2024; 24:37. [PMID: 38218807 PMCID: PMC10788026 DOI: 10.1186/s12905-023-02864-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC. METHODS The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on. RESULTS We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs. CONCLUSIONS Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.
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Affiliation(s)
- Jianfeng Zheng
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Shan Jiang
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Xuefen Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Huihui Wang
- Department of Anesthesiology, The Central hospital of Wenzhou City, 32 Dajian Lane, Wenzhou, 325000, China
| | - Li Liu
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Xintong Cai
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Yang Sun
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
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Li X, Liu H, Wang F, Yuan J, Guan W, Xu G. Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs. Curr Med Chem 2024; 31:4213-4231. [PMID: 38357948 PMCID: PMC11340295 DOI: 10.2174/0109298673281438231217151129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/09/2023] [Accepted: 11/16/2023] [Indexed: 02/16/2024]
Abstract
BACKGROUND Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs. OBJECTIVE The objective of this study was to launch a prediction model for therapeutic responses in OC patients. METHODS The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells. RESULTS The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis. CONCLUSION The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.
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Affiliation(s)
- Xin Li
- Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Huiqiang Liu
- Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Fanchen Wang
- Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Jia Yuan
- Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Wencai Guan
- Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China
| | - Guoxiong Xu
- Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508, China
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Liu W, Peng J, Xiao M, Cai Y, Peng B, Zhang W, Li J, Kang F, Hong Q, Liang Q, Yan Y, Xu Z. The implication of pyroptosis in cancer immunology: Current advances and prospects. Genes Dis 2023; 10:2339-2350. [PMID: 37554215 PMCID: PMC10404888 DOI: 10.1016/j.gendis.2022.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/18/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
Pyroptosis is a regulated cell death pathway involved in numerous human diseases, especially malignant tumors. Recent studies have identified multiple pyroptosis-associated signaling molecules, like caspases, gasdermin family and inflammasomes. In addition, increasing in vitro and in vivo studies have shown the significant linkage between pyroptosis and immune regulation of cancers. Pyroptosis-associated biomarkers regulate the infiltration of tumor immune cells, such as CD4+ and CD8+ T cells, thus strengthening the sensitivity to therapeutic strategies. In this review, we explained the relationship between pyroptosis and cancer immunology and focused on the significance of pyroptosis in immune regulation. We also proposed the future application of pyroptosis-associated biomarkers in basic research and clinical practices to address malignant behaviors. Exploration of the underlying mechanisms and biological functions of pyroptosis is critical for immune response and cancer immunotherapy.
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Affiliation(s)
- Wei Liu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Orthopedic Surgery, The Second Hospital University of South China, Hengyang, Hunan 421001, China
| | - Jinwu Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan 415000, China
| | - Muzhang Xiao
- Department of Burn and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Yuan Cai
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Bi Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Wenqin Zhang
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan 415000, China
| | - Jianbo Li
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan 415000, China
| | - Fanhua Kang
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan 415000, China
| | - Qianhui Hong
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan 415000, China
| | - Qiuju Liang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Yuanliang Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhijie Xu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan 415000, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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Li W, Xiong Y, Zhu J, Jin X, Meng J, He W. Establishing a prognostic model with ferroptosis-related long non-coding RNAs in bladder cancer. Transl Cancer Res 2023; 12:2023-2032. [PMID: 37701097 PMCID: PMC10493782 DOI: 10.21037/tcr-23-194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 07/21/2023] [Indexed: 09/14/2023]
Abstract
Background Ferroptosis is a distinct form of cell death that has the potential to supersede the drug resistance that is commonly observed with current chemotherapeutic agents. As a result, ferroptosis presents a new and innovative therapeutic pathway for cancer treatment. The current understanding regarding the expression of genes associated with ferroptosis in bladder cancer (BLCA) and their prognostic implications remains unclear. Consequently, this study aimed to examine the potential prognostic value of ferroptosis-associated long non-coding RNAs (lncRNAs) in BLCA. Methods The Cancer Genome Atlas (TCGA) was accessed to download RNA sequencing data and clinicopathological features of BLCA while accessing the FerrDb database to download ferroptosis-associated genes. The study calculated risk scores for ferroptosis-associated lncRNAs, and subsequently divided patients with BLCA into two groups, namely high- and low-risk, on the basis of the median risk score. Moreover, Kaplan-Meier (K-M) curves, Cox regression analysis, and column plots were utilized for evaluating the risk score prognostic value. Subsequently, the involvement of ferroptosis-associated mRNA, N6-methyladenosine (m6A) mRNA status, and immune responses was investigated for BLCA prognosis. Results Thirty-six lncRNAs were identified to be differently expressed and linked to the prognosis of BLCA. The findings from the K-M curve analysis indicated a significant association between a high-risk lncRNA profile and poor BLCA prognosis. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.810. The risk assessment model exhibited superior performance in predicting prognosis for BLCA compared to conventional clinicopathological features. Conclusions Thirty-six lncRNAs were found to be linked to ferroptosis for the prognosis of patients with BLCA, and these results may provide new insights for treating BLCA.
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Affiliation(s)
- Weisheng Li
- Henan University of Chinese Medicine, Zhengzhou, China
- Department of Urology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Yang Xiong
- Henan University of Chinese Medicine, Zhengzhou, China
- Department of Urology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Junlei Zhu
- Henan University of Chinese Medicine, Zhengzhou, China
- Department of Urology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaoxiao Jin
- Department of Urology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Jin Meng
- Department of Urology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Wenqiang He
- Department of Urology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
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Salamini-Montemurri M, Lamas-Maceiras M, Lorenzo-Catoira L, Vizoso-Vázquez Á, Barreiro-Alonso A, Rodríguez-Belmonte E, Quindós-Varela M, Cerdán ME. Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis. Int J Mol Sci 2023; 24:10798. [PMID: 37445988 PMCID: PMC10341812 DOI: 10.3390/ijms241310798] [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: 05/15/2023] [Revised: 06/19/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.
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Affiliation(s)
- Martín Salamini-Montemurri
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Mónica Lamas-Maceiras
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Lidia Lorenzo-Catoira
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Ángel Vizoso-Vázquez
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Aida Barreiro-Alonso
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Esther Rodríguez-Belmonte
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - María Quindós-Varela
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
- Complexo Hospitalario Universitario de A Coruña (CHUAC), Servizo Galego de Saúde (SERGAS), 15006 A Coruña, Spain
| | - M Esperanza Cerdán
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
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Wu Y, Wen X, Xia Y, Yu X, Lou Y. LncRNAs and regulated cell death in tumor cells. Front Oncol 2023; 13:1170336. [PMID: 37313458 PMCID: PMC10258353 DOI: 10.3389/fonc.2023.1170336] [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: 02/21/2023] [Accepted: 05/17/2023] [Indexed: 06/15/2023] Open
Abstract
Regulated Cell Death (RCD) is a mode of cell death that occurs through drug or genetic intervention. The regulation of RCDs is one of the significant reasons for the long survival time of tumor cells and poor prognosis of patients. Long non-coding RNAs (lncRNAs) which are involved in the regulation of tumor biological processes, including RCDs occurring on tumor cells, are closely related to tumor progression. In this review, we describe the mechanisms of eight different RCDs which contain apoptosis, necroptosis, pyroptosis, NETosis, entosis, ferroptosis, autosis and cuproptosis. Meanwhile, their respective roles in the tumor are aggregated. In addition, we outline the literature that is related to the regulatory relationships between lncRNAs and RCDs in tumor cells, which is expected to provide new ideas for tumor diagnosis and treatment.
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Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA. Biomolecules 2023; 13:biom13020306. [PMID: 36830675 PMCID: PMC9953467 DOI: 10.3390/biom13020306] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/23/2022] [Accepted: 01/04/2023] [Indexed: 02/10/2023] Open
Abstract
(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.
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Hua T, Liu DX, Zhang XC, Li ST, Yan P, Zhao Q, Chen SB. CD4+ conventional T cells-related genes signature is a prognostic indicator for ovarian cancer. Front Immunol 2023; 14:1151109. [PMID: 37063862 PMCID: PMC10104164 DOI: 10.3389/fimmu.2023.1151109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/16/2023] [Indexed: 04/18/2023] Open
Abstract
Introduction It is believed that ovarian cancer (OC) is the most deadly form of gynecological cancer despite its infrequent occurrence, which makes it one of the most salient public health concerns. Clinical and preclinical studies have revealed that intratumoral CD4+ T cells possess cytotoxic capabilities and were capable of directly killing cancer cells. This study aimed to identify the CD4+ conventional T cells-related genes (CD4TGs) with respect to the prognosis in OC. Methods We obtained the transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD4TGs were first identified from single-cell datasets, then univariate Cox regression was used to screen prognosis-related genes, LASSO was conducted to remove genes with coefficient zero, and multivariate Cox regression was used to calculate riskscore and to construct the CD4TGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. The role of riskscore has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. A risk signature with 11 CD4TGs in OC was finally established in the TCGA database and furtherly validated in several GEO cohorts. Results High riskscore was significantly associated with a poorer prognosis and proven to be an independent prognostic biomarker by multivariate Cox regression. The 1-, 3-, and 5-year ROC values, DCA curve, nomogram, and calibration results confirmed the excellent prediction power of this model. Compared with the reported risk models, our model showed better performance. The patients were grouped into high-risk and low-risk subgroups according to the riskscore by the median value. The low-risk group patients tended to exhibit a higher immune infiltration, immune-related gene expression and were more sensitive to immunotherapy and chemotherapy. Discussion Collectively, our findings of the prognostic value of CD4TGs in prognosis and immune response, provided valuable insights into the molecular mechanisms and clinical management of OC.
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Affiliation(s)
- Tian Hua
- Department of Gynecology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Deng-xiang Liu
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Xiao-chong Zhang
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Shao-teng Li
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Peng Yan
- Department of Oncology, The Second Affiliated Hospital Of Xingtai Medical College, Xingtai, China
| | - Qun Zhao
- Department of Oncology, Hebei Medical University, Fourth Hospital, Shijiazhuang, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
- *Correspondence: Shu-bo Chen, ; Qun Zhao,
| | - Shu-bo Chen
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
- *Correspondence: Shu-bo Chen, ; Qun Zhao,
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Su D, Zhang Z, Xu Z, Xia F, Yan Y. A prognostic exosome-related LncRNA risk model correlates with the immune microenvironment in liver cancer. Front Genet 2022; 13:965329. [PMID: 36081999 PMCID: PMC9445491 DOI: 10.3389/fgene.2022.965329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Emerging studies have shown the important roles of long noncoding RNAs (lncRNAs) in the occurrence and development of liver cancer. However, the exosome-related lncRNA signature in liver cancer remains to be clarified. Methods: We obtained 371 tumor specimens and 50 normal tissues from the TCGA database. These samples were randomly divided into the training queue and verification queue. The exosome-related lncRNA risk model was verified by correlation analysis, Lasso regression analysis, and Cox regression analysis. The differences in the immune microenvironment in the two risk groups were obtained by analyzing the infiltration of different immune cells. Results: Five exosome-related lncRNAs associated (MKLN1-AS, TMCC1-AS1, AL031985.3, LINC01138, AC099850.3) with a poor prognosis were identified and used to construct the signature. Receiver operating curve (ROC) and survival curves were used to confirm the predictive ability of this signature. Based on multivariate regression analysis in the training cohort (HR: 3.033, 95% CI: 1.762–5.220) and validation cohort (HR: 1.998, 95% CI: 1.065–3.751), the risk score was found to be an independent risk factor for patient prognosis. Subsequently, a nomogram was constructed to predict the 1-, 3-, 5-years survival rates of liver cancer patients. Moreover, this signature was also related to overexpressed immune checkpoints (PD-1, B7-H3, VSIR, PD-L1, LAG3, TIGIT and CTLA4). Conclusion: Our study showed that exosome-related lncRNAs and the corresponding nomogram could be used as a better index to predict the outcome and immune regulation of liver cancer patients. This signature might provide a new idea for the immunotherapy of liver cancer in the future.
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Affiliation(s)
- Duntao Su
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zeyu Zhang
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhijie Xu
- Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, China
- Department of Pathology, Xiangya Changde Hospital, Changde, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- *Correspondence: Zhijie Xu, ; Fada Xia,
| | - Fada Xia
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- *Correspondence: Zhijie Xu, ; Fada Xia,
| | - Yuanliang Yan
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Zhang C, Liu N. Ferroptosis, necroptosis, and pyroptosis in the occurrence and development of ovarian cancer. Front Immunol 2022; 13:920059. [PMID: 35958626 PMCID: PMC9361070 DOI: 10.3389/fimmu.2022.920059] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022] Open
Abstract
Ovarian cancer (OC) is one of the most common malignancies that causes death in women and is a heterogeneous disease with complex molecular and genetic changes. Because of the relatively high recurrence rate of OC, it is crucial to understand the associated mechanisms of drug resistance and to discover potential target for rational targeted therapy. Cell death is a genetically determined process. Active and orderly cell death is prevalent during the development of living organisms and plays a critical role in regulating life homeostasis. Ferroptosis, a novel type of cell death discovered in recent years, is distinct from apoptosis and necrosis and is mainly caused by the imbalance between the production and degradation of intracellular lipid reactive oxygen species triggered by increased iron content. Necroptosis is a regulated non-cysteine protease–dependent programmed cell necrosis, morphologically exhibiting the same features as necrosis and occurring via a unique mechanism of programmed cell death different from the apoptotic signaling pathway. Pyroptosis is a form of programmed cell death that is characterized by the formation of membrane pores and subsequent cell lysis as well as release of pro-inflammatory cell contents mediated by the abscisin family. Studies have shown that ferroptosis, necroptosis, and pyroptosis are involved in the development and progression of a variety of diseases, including tumors. In this review, we summarized the recent advances in ferroptosis, necroptosis, and pyroptosis in the occurrence, development, and therapeutic potential of OC.
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Liu J, Liu L, Antwi PA, Luo Y, Liang F. Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning. Front Genet 2022; 13:858466. [PMID: 35719392 PMCID: PMC9198487 DOI: 10.3389/fgene.2022.858466] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning. Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level. Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis. Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.
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Affiliation(s)
- Jinya Liu
- Department of Plastic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Leping Liu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Paul Akwasi Antwi
- Department of Plastic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yanwei Luo
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Fang Liang
- Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
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