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Li S, Zhao J, Wang G, Yao Q, Leng Z, Liu Q, Jiang J, Wang W. Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response. Arch Dermatol Res 2024; 316:262. [PMID: 38795156 DOI: 10.1007/s00403-024-03080-3] [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: 10/28/2023] [Revised: 10/28/2023] [Accepted: 04/26/2024] [Indexed: 05/27/2024]
Abstract
Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.
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Affiliation(s)
- Shanshan Li
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Junjie Zhao
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Guangyu Wang
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Qingping Yao
- Institute of Mechanobiology & Medical Engineering, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhe Leng
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Qinglei Liu
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Jun Jiang
- Department of Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, 646000, China
| | - Wei Wang
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
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2
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Ren X, Cui H, Dai L, Chang L, Liu D, Yan W, Zhao X, Kang H, Ma X. PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer. J Cancer Res Clin Oncol 2024; 150:119. [PMID: 38466449 PMCID: PMC10927816 DOI: 10.1007/s00432-024-05626-4] [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: 08/02/2023] [Accepted: 01/16/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity. METHODS PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups. RESULTS PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion. CONCLUSION The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.
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Affiliation(s)
- Xueting Ren
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hanxiao Cui
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Luyao Dai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Lidan Chang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Dandan Liu
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wenyu Yan
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xuyan Zhao
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Xiaobin Ma
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
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Zhang Q, Deng Z, Yang Y. Metastasis-Related Signature for Clinically Predicting Prognosis and Tumor Immune Microenvironment of Osteosarcoma Patients. Mol Biotechnol 2023; 65:1836-1845. [PMID: 36807122 PMCID: PMC10518285 DOI: 10.1007/s12033-023-00681-7] [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: 09/22/2022] [Accepted: 01/18/2023] [Indexed: 02/23/2023]
Abstract
Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.
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Affiliation(s)
- Qing Zhang
- Department of Orthopaedic Oncology Surgery, Beijing Jishuitan Hospital, Peking University, No 31, Xinjiekou Dongjie, Beijing, China.
| | - Zhiping Deng
- Department of Orthopaedic Oncology Surgery, Beijing Jishuitan Hospital, Peking University, No 31, Xinjiekou Dongjie, Beijing, China
| | - Yongkun Yang
- Department of Orthopaedic Oncology Surgery, Beijing Jishuitan Hospital, Peking University, No 31, Xinjiekou Dongjie, Beijing, China
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Zhang J, Liu X, Huang Z, Wu C, Zhang F, Han A, Stalin A, Lu S, Guo S, Huang J, Liu P, Shi R, Zhai Y, Chen M, Zhou W, Bai M, Wu J. T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing. Comput Biol Med 2023; 152:106460. [PMID: 36565482 DOI: 10.1016/j.compbiomed.2022.106460] [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: 10/20/2022] [Revised: 12/06/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND T cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. We aimed to explore the expression profile of T cell marker genes, constructed a prognostic risk model based on these genes in Lung adenocarcinoma (LUAD), and investigated the link between this risk model and the immunotherapy response. METHODS We obtained the single-cell sequencing data of LUAD from the literature, and screened out 6 tissue biopsy samples, including 32,108 cells from patients with non-small cell lung cancer, to identify T cell marker genes in LUAD. Combined with TCGA database, a prognostic risk model based on T-cell marker gene was constructed, and the data from GEO database was used for verification. We also investigated the association between this risk model and immunotherapy response. RESULTS Based on scRNA-seq data 1839 T-cell marker genes were identified, after which a risk model consisting of 9 gene signatures for prognosis was constructed in combination with the TCGA dataset. This risk model divided patients into high-risk and low-risk groups based on overall survival. The multivariate analysis demonstrated that the risk model was an independent prognostic factor. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. Risk scores of the model were closely correlated with Linoleic acid metabolism, intestinal immune network for IgA production and drug metabolism cytochrome P450. CONCLUSION Our study proposed a novel prognostic risk model based on T cell marker genes for LUAD patients. The survival of LUAD patients as well as treatment outcomes may be accurately predicted by the prognostic risk model, and make the high-risk population present different immune cell infiltration and immunosuppression state.
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Affiliation(s)
- Jingyuan Zhang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Xinkui Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zhihong Huang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Chao Wu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Fanqin Zhang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Aiqing Han
- School of Management, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Antony Stalin
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Shan Lu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Siyu Guo
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Jiaqi Huang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Pengyun Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Rui Shi
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Yiyan Zhai
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Meilin Chen
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Wei Zhou
- Pharmacy Department, China-Japan Friendship Hospital, Beijing, 100029, China.
| | - Meirong Bai
- Key Laboratory of Mongolian Medicine Research and Development Engineering, Ministry of Education, Tongliao, 028000, China.
| | - Jiarui Wu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
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Liu C, Pu M, Ma Y, Wang C, Kong L, Zhang S, Zhao X, Lian X. Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis. Exp Biol Med (Maywood) 2022; 247:1741-1751. [PMID: 36330895 PMCID: PMC9638959 DOI: 10.1177/15353702221110659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.
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Affiliation(s)
- Chengli Liu
- Department of Hepatobiliary Surgery, Air Force Medical Center, Air Force Clinical College (Air Force Medical Center) of Anhui Medical University, Beijing 100142, China,Chengli Liu.
| | - Meng Pu
- Department of Hepatobiliary Surgery, Air Force Medical Center, Beijing 100142, China
| | - Yingbo Ma
- Department of Hepatobiliary Surgery, Standardized Residency Training Base, Air Force Medical Center, Beijing 100142, China
| | - Cheng Wang
- Department of Hepatobiliary Surgery, Air Force Medical Center, Beijing 100142, China
| | - Linghong Kong
- Department of Hepatobiliary Surgery, Air Force Medical Center, Beijing 100142, China
| | - Shuhan Zhang
- Department of Hepatobiliary Surgery, Air Force Medical Center, Beijing 100142, China
| | - Xuying Zhao
- Department of Hepatobiliary Surgery, Standardized General Surgery Specialists Training Base, Air Force Medical Center, Beijing 100142, China
| | - Xiaopeng Lian
- Department of Hepatobiliary Surgery, Standardized General Surgery Specialists Training Base, Air Force Medical Center, Beijing 100142, China
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Identification of Prognostic and Tumor Microenvironment by Shelterin Complex-Related Signatures in Oral Squamous Cell Carcinoma. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:6849304. [PMID: 35757510 PMCID: PMC9217620 DOI: 10.1155/2022/6849304] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/24/2022] [Indexed: 12/02/2022]
Abstract
Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the oral cavity. Shelterin complex gene (SG) has an important role in regulating telomere structure and length. SG is considered promising as a novel prognostic marker for cancer and a potential target for tumor therapy. However, SGs have not been systematically studied in OSCC. We analyzed SGs based on public data from OSCC patients and showed that SGs are closely associated with the prognosis of OSCC patients. Two different subtypes of SGs were identified in the TCGA and GEO cohorts, and LASSO regression analysis was used to further construct an SGs-related prognostic model. Randomized cohorts and different clinical subgroups validated the model's accuracy. The assessment of clinical characteristics, tumor mutational burden (TMB), and tumor microenvironment (TME) between high- and low-risk scores groups showed lower TMB, more abundant immune cell infiltration, and better prognosis in the low-risk group. According to the IPS analysis, patients in the low-risk group were more responsive to immunotherapy. This study establishes a foundation for research on SG and confirms that risk scores can predict prognosis and guide clinical treatment in OSCC patients.
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7
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Identification of gene signatures for COAD using feature selection and Bayesian network approaches. Sci Rep 2022; 12:8761. [PMID: 35610288 PMCID: PMC9130243 DOI: 10.1038/s41598-022-12780-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
The combination of TCGA and GTEx databases will provide more comprehensive information for characterizing the human genome in health and disease, especially for underlying the cancer genetic alterations. Here we analyzed the gene expression profile of COAD in both tumor samples from TCGA and normal colon tissues from GTEx. Using the SNR-PPFS feature selection algorithms, we discovered a 38 gene signatures that performed well in distinguishing COAD tumors from normal samples. Bayesian network of the 38 genes revealed that DEGs with similar expression patterns or functions interacted more closely. We identified 14 up-DEGs that were significantly correlated with tumor stages. Cox regression analysis demonstrated that tumor stage, STMN4 and FAM135B dysregulation were independent prognostic factors for COAD survival outcomes. Overall, this study indicates that using feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.
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Niu Z, Wang X, Xu Y, Li Y, Gong X, Zeng Q, Zhang B, Xi J, Pei X, Yue W, Han Y. Development and Validation of a Novel Survival Model for Cutaneous Melanoma Based on Necroptosis-Related Genes. Front Oncol 2022; 12:852803. [PMID: 35387121 PMCID: PMC8979066 DOI: 10.3389/fonc.2022.852803] [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: 01/24/2022] [Accepted: 02/25/2022] [Indexed: 12/16/2022] Open
Abstract
Background Necroptosis is crucial for organismal development and pathogenesis. To date, the role of necroptosis in skin cutaneous melanoma (SKCM) is yet unveiled. In addition, the part of melanin pigmentation was largely neglected in the bioinformatic analysis. In this study, we aimed to construct a novel prognostic model based on necroptosis-related genes and analysis the pigmentation phenotype of patients to provide clinically actionable information for SKCM patients. Methods We downloaded the SKCM data from the TCGA and GEO databases in this study and identified the differently expressed and prognostic necroptosis-related genes. Patients’ pigmentation phenotype was evaluated by the GSVA method. Then, using Lasso and Cox regression analysis, a novel prognostic model was constructed based on the intersected genes. The risk score was calculated and the patients were divided into two groups. The survival differences between the two groups were compared using Kaplan-Meier analysis. The ROC analysis was performed and the area under curves was calculated to evaluate the prediction performances of the model. Then, the GO, KEGG and GSEA analyses were performed to elucidate the underlying mechanisms. Differences in the tumor microenvironment, patients’ response to immune checkpoint inhibitors (ICIs) and pigmentation phenotype were analyzed. In order to validate the mRNA expression levels of the selected genes, quantitative real-time PCR (qRT-PCR) was performed. Results Altogether, a novel prognostic model based on four genes (BOK, CD14, CYLD and FASLG) was constructed, and patients were classified into high and low-risk groups based on the median risk score. Low-risk group patients showed better survival status. The model showed high accuracy in the training and the validation cohort. Pathway and functional enrichment analysis indicated that immune-related pathways were differently activated in the two groups. In addition, immune cells infiltration patterns and sensitivity of ICIs showed a significant difference between patients from two risk groups. The pigmentation score was positively related to the risk score in pigmentation phenotype analysis. Conclusion In conclusion, this study established a novel prognostic model based on necroptosis-related genes and revealed the possible connections between necroptosis and melanin pigmentation. It is expected to provide a reference for clinical treatment.
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Affiliation(s)
- Zehao Niu
- Medical School of Chinese PLA, Beijing, China.,Department of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xin Wang
- Department of Ophthalmology, PLA Strategic Support Force Characteristic Medical Center, Beijing, China
| | - Yujian Xu
- Department of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yan Li
- Medical School of Chinese PLA, Beijing, China.,Department of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xiaojing Gong
- Medical School of Chinese PLA, Beijing, China.,Department of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Quan Zeng
- Stem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.,South China Research Center for Stem Cell and Regenerative Medicine, SCIB, Guangzhou, China
| | - Biao Zhang
- Stem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.,South China Research Center for Stem Cell and Regenerative Medicine, SCIB, Guangzhou, China
| | - Jiafei Xi
- Stem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.,South China Research Center for Stem Cell and Regenerative Medicine, SCIB, Guangzhou, China.,Academy of Military Medical Sciences (AMMS), Academy of Military Sciences, Beijing, China
| | - Xuetao Pei
- Stem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.,South China Research Center for Stem Cell and Regenerative Medicine, SCIB, Guangzhou, China.,Academy of Military Medical Sciences (AMMS), Academy of Military Sciences, Beijing, China
| | - Wen Yue
- Stem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.,South China Research Center for Stem Cell and Regenerative Medicine, SCIB, Guangzhou, China.,Academy of Military Medical Sciences (AMMS), Academy of Military Sciences, Beijing, China
| | - Yan Han
- Department of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
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Zhou Y, Yu Z, Liu L, Wei L, Zhao L, Huang L, Wang L, Sun S. Construction and evaluation of an integrated predictive model for chronic kidney disease based on the random forest and artificial neural network approaches. Biochem Biophys Res Commun 2022; 603:21-28. [PMID: 35276459 DOI: 10.1016/j.bbrc.2022.02.099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/23/2022] [Indexed: 12/18/2022]
Abstract
Chronic kidney disease (CKD) is recognized as a serious global health problem due to its high prevalence and all-cause mortality. The aim of this research was to identify critical biomarkers and construct an integrated model for the early prediction of CKD. By using existing RNA-seq data and clinical information from CKD patients from the Gene Expression Omnibus (GEO) database, we applied a computational technique that combined the random forest (RF) and artificial neural network (ANN) approaches to identify gene biomarkers and construct an early diagnostic model. We generated ROC curves to compare the model with other markers and evaluated the associations of selected genes with various clinical properties of CKD. Moreover, we highlighted two biomarkers involved in energy metabolism pathways: pyruvate dehydrogenase kinase 4 (PDK4) and zinc finger protein 36 (ZFP36). The downregulation of the identified key genes was subsequently confirmed in both unilateral ureteral obstruction (UUO) and ischemia reperfusion injury (IRI) mouse models, accompanied by decreased energy metabolism. In vitro experiments and single-cell sequencing analysis proved that these key genes were related to the energy metabolism of proximal tubule cells and were involved in the development of CKD. Overall, we constructed a composite prediction model and discovered key genes that might be used as biomarkers and therapeutic targets for CKD.
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Affiliation(s)
- Ying Zhou
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China; Department of Geriatrics, General Hospital of Central Theater Command, Wuhan, Hubei, 430070, China
| | - Zhixiang Yu
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Limin Liu
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Lei Wei
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Lijuan Zhao
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Liuyifei Huang
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Liya Wang
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China; State Key Laboratory of Cancer Biology, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China.
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10
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Korfiati A, Grafanaki K, Kyriakopoulos GC, Skeparnias I, Georgiou S, Sakellaropoulos G, Stathopoulos C. Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View. Int J Mol Sci 2022; 23:1299. [PMID: 35163222 PMCID: PMC8836065 DOI: 10.3390/ijms23031299] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/20/2022] [Accepted: 01/20/2022] [Indexed: 02/07/2023] Open
Abstract
The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.
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Affiliation(s)
- Aigli Korfiati
- Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece; (A.K.); (G.S.)
| | - Katerina Grafanaki
- Department of Dermatology, School of Medicine, University of Patras, 26504 Patras, Greece;
| | | | - Ilias Skeparnias
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA;
| | - Sophia Georgiou
- Department of Dermatology, School of Medicine, University of Patras, 26504 Patras, Greece;
| | - George Sakellaropoulos
- Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece; (A.K.); (G.S.)
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Lee KJ, Betz-Stablein B, Stark MS, Janda M, McInerney-Leo AM, Caffery LJ, Gillespie N, Yanes T, Soyer HP. The Future of Precision Prevention for Advanced Melanoma. Front Med (Lausanne) 2022; 8:818096. [PMID: 35111789 PMCID: PMC8801740 DOI: 10.3389/fmed.2021.818096] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/22/2021] [Indexed: 12/16/2022] Open
Abstract
Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.
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Affiliation(s)
- Katie J. Lee
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Brigid Betz-Stablein
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Mitchell S. Stark
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Monika Janda
- Centre for Health Services Research, School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Aideen M. McInerney-Leo
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Liam J. Caffery
- Centre for Health Services Research, School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicole Gillespie
- The University of Queensland Business School, Faculty of Business, Economics and Law, The University of Queensland, Brisbane, QLD, Australia
| | - Tatiane Yanes
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - H. Peter Soyer
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
- Department of Dermatology, Princess Alexandra Hospital, Brisbane, QLD, Australia
- *Correspondence: H. Peter Soyer
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12
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Niu Z, Xu Y, Li Y, Chen Y, Han Y. Construction and validation of a novel pyroptosis-related signature to predict prognosis in patients with cutaneous melanoma. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:688-706. [PMID: 34903008 DOI: 10.3934/mbe.2022031] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Skin cutaneous melanoma (SKCM) is one of the most malignant skin cancers and remains a health concern worldwide. Pyroptosis is a newly recognized form of programmed cell death and plays a vital role in cancer progression. We aim to construct a prognostic model for SKCM patients based on pyroptosis-related genes (PRGs). SKCM patients from The Cancer Genome Atlas (TCGA) were divided into training and validation cohorts. We used GSE65904 downloaded from GEO database as an external validation cohort. We performed Cox regression and the least absolute shrinkage and selection operator (LASSO) regression to identify prognostic genes and built a risk score. Patients were divided into high- and low-risk groups based on the risk score. Differently expressed genes (DEGs), immune cell infiltration and immune-related pathways activation were compared between the two groups. We established a model containing 4 PRGs, i.e., GSDMA, GSDMC, AIM2 and NOD2. The overall survival (OS) time was significantly different between the 2 groups. The risk score was an independent predictor for prognosis in both the uni- and multi-variable Cox regressions. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that DEGs were enriched in immune-related pathways. Most types of immune cells were highly expressed in the low risk group. All immune pathways were significantly up-regulated in the low-risk group. In addition, low-risk patients had a better response to immune checkpoint inhibitors. Our novel pyroptosis-related gene signature could predict the prognosis of SKCM patients and their response to immune checkpoint inhibitors.
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Affiliation(s)
- Zehao Niu
- Medical School of Chinese PLA, Beijing 100853, China
- Department of Plastic and Reconstructive Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Yujian Xu
- Department of Plastic and Reconstructive Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Yan Li
- Medical School of Chinese PLA, Beijing 100853, China
- Department of Plastic and Reconstructive Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Youbai Chen
- Department of Plastic and Reconstructive Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Yan Han
- Department of Plastic and Reconstructive Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
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13
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Hu J, Liu X, Tang Y. HMGB1/Foxp1 regulates hypoxia-induced inflammatory response in macrophages. Cell Biol Int 2021; 46:265-277. [PMID: 34816539 DOI: 10.1002/cbin.11728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 10/14/2021] [Accepted: 11/13/2021] [Indexed: 01/04/2023]
Abstract
Forkhead box protein P1 (Foxp1) is a kind of tumor suppressor gene, and the role of Foxp1 in the macrophages of myocardial infarction (MI) has not been studied yet. Here, we verified the role of the transcription factor high mobility group box 1 (HMGB1) and its target gene Foxp1 in the inflammatory response. In this study, the key genes HMGB1 and Foxp1 in the macrophages of mouse MI model were screened out through single-cell transcriptome analysis of GSE136088 (GEO database). In vitro experiment indicated that hypoxia induced the inflammatory response in RAW264.7 macrophages, promoted the secretion of inflammatory factors (tumor necrosis factor α [TNF-α], interleukin 6 [IL-6], and IL-1β) and the activation of NLRP3 inflammasome (NLRP3, ASC, and pro-caspase-1). Meanwhile, HMGB1 increased while Foxp1 decreased in hypoxia-treated RAW264.7 macrophages. HMGB1 bound to the upstream promoter region of Foxp1 as demonstrated by the dual-luciferase reporter assay, chromatin immunoprecipitation (ChIP)-quantitative polymerase chain reaction (qPCR) and agarose gel electrophoresis. As a transcription factor, HMGB1 regulated Foxp1 expression. The secretion of inflammatory factors and the expression of NLRP3 inflammasome protein were changed when the expression of HMGB1 and Foxp1 was regulated in the hypoxia-treated RAW264.7 macrophages. This study verified that HMGB1 could aggravate the hypoxia-treated inflammatory response of macrophages through downregulating Foxp1, which not only provides evidence to support the role of HMGB1/Foxp1 in macrophages but also offers another angle for the treatment of MI.
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Affiliation(s)
- Jing Hu
- Department of Cardiovascular, The First Hospital of Nanchang, Nanchang, Jiangxi, China
| | - Xiaojun Liu
- Department of Cardiovascular, The First Hospital of Nanchang, Nanchang, Jiangxi, China
| | - Yu Tang
- Department of Cardiovascular, Jiangxi Provincial People's Hospital, Nanchang, Jiangxi, China
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14
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Lei Y, Tang R, Xu J, Wang W, Zhang B, Liu J, Yu X, Shi S. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol 2021; 14:91. [PMID: 34108022 PMCID: PMC8190846 DOI: 10.1186/s13045-021-01105-2] [Citation(s) in RCA: 182] [Impact Index Per Article: 60.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 06/03/2021] [Indexed: 02/06/2023] Open
Abstract
Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.
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Affiliation(s)
- Yalan Lei
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Bo Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jiang Liu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. .,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China. .,Pancreatic Cancer Institute, Fudan University, Shanghai, China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. .,Shanghai Pancreatic Cancer Institute, No. 270 Dong'An Road, Shanghai, 200032, China. .,Pancreatic Cancer Institute, Fudan University, Shanghai, China.
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15
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Yuan Y, Zhu Z, Lan Y, Duan S, Zhu Z, Zhang X, Li G, Qu H, Feng Y, Cai H, Song Z. Development and Validation of a CD8+ T Cell Infiltration-Related Signature for Melanoma Patients. Front Immunol 2021; 12:659444. [PMID: 34040608 PMCID: PMC8141567 DOI: 10.3389/fimmu.2021.659444] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/19/2021] [Indexed: 12/19/2022] Open
Abstract
Aim Immunotherapy shows efficacy in only a subset of melanoma patients. Here, we intended to construct a risk score model to predict melanoma patients’ sensitivity to immunotherapy. Methods Integration analyses were performed on melanoma patients from high-dimensional public datasets. The CD8+ T cell infiltration related genes (TIRGs) were selected via TIMER and CIBERSORT algorithm. LASSO Cox regression was performed to screen for the crucial TIRGs. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to evaluate the immune activity. The prognostic value of the risk score was determined by univariate and multivariate Cox regression analysis. Results 184 candidate TIRGs were identified in melanoma patients. Based on the candidate TIRGs, melanoma patients were classified into three clusters which were characterized by different immune activity. Six signature genes were further screened out of 184 TIRGs and a representative risk score for patient survival was constructed based on these six signature genes. The risk score served as an indicator for the level of CD8+ T cell infiltration and acted as an independent prognostic factor for the survival of melanoma patients. By using the risk score, we achieved a good predicting result for the response of cancer patients to immunotherapy. Moreover, pan-cancer analysis revealed the risk score could be used in a wide range of non-hematologic tumors. Conclusions Our results showed the potential of using signature gene-based risk score as an indicator to predict melanoma patients’ sensitivity to immunotherapy.
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Affiliation(s)
- Yuan Yuan
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Zheng Zhu
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Ying Lan
- School of Nursing, Yueyang Vocational and Technical College, Yueyang, China
| | - Saili Duan
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China.,Xiangya School of Medicine of Central South University, Changsha, China
| | - Ziqing Zhu
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China.,Xiangya School of Medicine of Central South University, Changsha, China
| | - Xi Zhang
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Guoyin Li
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Hui Qu
- Department of Plastic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Yanhui Feng
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Hui Cai
- Department of Orthopaedics, Loudi Central Hospital, Loudi, China
| | - Zewen Song
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
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