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Meng J, Jiang A, Lu X, Gu D, Ge Q, Bai S, Zhou Y, Zhou J, Hao Z, Yan F, Wang L, Wang H, Du J, Liang C. Multiomics characterization and verification of clear cell renal cell carcinoma molecular subtypes to guide precise chemotherapy and immunotherapy. IMETA 2023; 2:e147. [PMID: 38868222 PMCID: PMC10989995 DOI: 10.1002/imt2.147] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 10/21/2023] [Indexed: 06/14/2024]
Abstract
Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor with different genetic and molecular alterations. Schemes for ccRCC classification system based on multiomics are urgent, to promote further biological insights. Two hundred and fifty-five ccRCC patients with paired data of clinical information, transcriptome expression profiles, copy number alterations, DNA methylation, and somatic mutations were collected for identification. Bioinformatic analyses were performed based on our team's recently developed R package "MOVICS." With 10 state-of-the-art algorithms, we identified the multiomics subtypes (MoSs) for ccRCC patients. MoS1 is an immune exhausted subtype, presented the poorest prognosis, and might be caused by an exhausted immune microenvironment, activated hypoxia features, but can benefit from PI3K/AKT inhibitors. MoS2 is an immune "cold" subtype, which represented more mutation of VHL and PBRM1, favorable prognosis, and is more suitable for sunitinib therapy. MoS3 is the immune "hot" subtype, and can benefit from the anti-PD-1 immunotherapy. We successfully verified the different molecular features of the three MoSs in external cohorts GSE22541, GSE40435, and GSE53573. Patients that received Nivolumab therapy helped us to confirm that MoS3 is suitable for anti-PD-1 therapy. E-MTAB-3267 cohort also supported the fact that MoS2 patients can respond more to sunitinib treatment. We also confirm that SETD2 is a tumor suppressor in ccRCC, along with the decreased SETD2 protein level in advanced tumor stage, and knock-down of SETD2 leads to the promotion of cell proliferation, migration, and invasion. In summary, we provide novel insights into ccRCC molecular subtypes based on robust clustering algorithms via multiomics data, and encourage future precise treatment of ccRCC patients.
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Affiliation(s)
- Jialin Meng
- Department of UrologyThe First Affiliated Hospital of Anhui Medical University, Institute of Urology, Anhui Medical University, Anhui Province Key Laboratory of Genitourinary DiseasesAnhui Medical UniversityHefeiChina
| | - Aimin Jiang
- Department of Urology, Changhai HospitalNaval Medical University (Second Military Medical University)ShanghaiChina
| | - Xiaofan Lu
- Department of Cancer and Functional GenomicsInstitute of Genetics and Molecular and Cellular Biology, CNRS/INSERM/UNISTRAIllkirchFrance
| | - Di Gu
- Department of Urology, Changhai HospitalNaval Medical University (Second Military Medical University)ShanghaiChina
| | - Qintao Ge
- Department of UrologyThe First Affiliated Hospital of Anhui Medical University, Institute of Urology, Anhui Medical University, Anhui Province Key Laboratory of Genitourinary DiseasesAnhui Medical UniversityHefeiChina
| | - Suwen Bai
- The Second Affiliated Hospital, School of MedicineThe Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of ShenzhenShenzhenChina
| | - Yundong Zhou
- Department of Surgery, Ningbo Medical Center Lihuili HospitalNingbo UniversityNingboZhejiangChina
| | - Jun Zhou
- Department of UrologyThe First Affiliated Hospital of Anhui Medical University, Institute of Urology, Anhui Medical University, Anhui Province Key Laboratory of Genitourinary DiseasesAnhui Medical UniversityHefeiChina
| | - Zongyao Hao
- Department of UrologyThe First Affiliated Hospital of Anhui Medical University, Institute of Urology, Anhui Medical University, Anhui Province Key Laboratory of Genitourinary DiseasesAnhui Medical UniversityHefeiChina
| | - Fangrong Yan
- Research Center of Biostatistics and Computational PharmacyChina Pharmaceutical UniversityNanjingChina
| | - Linhui Wang
- Department of Urology, Changhai HospitalNaval Medical University (Second Military Medical University)ShanghaiChina
| | - Haitao Wang
- Cancer Center, Faculty of Health SciencesUniversity of MacauMacau SARChina
- Present address:
Center for Cancer ResearchBethesdaMarylandUSA
| | - Juan Du
- The Second Affiliated Hospital, School of MedicineThe Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of ShenzhenShenzhenChina
| | - Chaozhao Liang
- Department of UrologyThe First Affiliated Hospital of Anhui Medical University, Institute of Urology, Anhui Medical University, Anhui Province Key Laboratory of Genitourinary DiseasesAnhui Medical UniversityHefeiChina
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Liu Y, Guo S, Xie W, Yang H, Li W, Zhou N, Yang J, Zhou G, Mao C, Zheng Y. Identification of microRNA editing sites in clear cell renal cell carcinoma. Sci Rep 2023; 13:15117. [PMID: 37704698 PMCID: PMC10499803 DOI: 10.1038/s41598-023-42302-y] [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: 05/15/2023] [Accepted: 09/07/2023] [Indexed: 09/15/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is a malignant tumor originating from the renal tubular epithelium. Although the microRNAs (miRNAs) transcriptome of ccRCC has been extensively studied, the role of miRNAs editing in ccRCC is largely unknown. By analyzing small RNA sequencing profiles of renal tissues of 154 ccRCC patients and 22 normal controls, we identified 1025 miRNA editing sites from 246 pre-miRNAs. There were 122 editing events with significantly different editing levels in ccRCC compared to normal samples, which include two A-to-I editing events in the seed regions of hsa-mir-376a-3p and hsa-mir-376c-3p, respectively, and one C-to-U editing event in the seed region of hsa-mir-29c-3p. After comparing the targets of the original and edited miRNAs, we found that hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u had many new targets, respectively. Many of these new targets were deregulated in ccRCC, which might be related to the different editing levels of hsa-mir-376a-3p, hsa-mir-376c-3p, hsa-mir-29c-3p in ccRCC compared to normal controls. Our study sheds new light on miRNA editing events and their potential biological functions in ccRCC.
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Affiliation(s)
- Yulong Liu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
| | - Shiyong Guo
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Wenping Xie
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
| | - Huaide Yang
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
| | - Wanran Li
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Nan Zhou
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Jun Yang
- School of Criminal Investigation, Yunnan Police College, Kunming, 650223, Yunnan, China
| | - Guangchen Zhou
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Chunyi Mao
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
| | - Yun Zheng
- College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China.
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Li K, Zhu Y, Cheng J, Li A, Liu Y, Yang X, Huang H, Peng Z, Xu H. A novel lipid metabolism gene signature for clear cell renal cell carcinoma using integrated bioinformatics analysis. Front Cell Dev Biol 2023; 11:1078759. [PMID: 36866272 PMCID: PMC9971983 DOI: 10.3389/fcell.2023.1078759] [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: 10/24/2022] [Accepted: 01/18/2023] [Indexed: 02/16/2023] Open
Abstract
Background: Clear cell renal cell carcinoma (ccRCC), which is the most prevalent type of renal cell carcinoma, has a high mortality rate. Lipid metabolism reprogramming is a hallmark of ccRCC progression, but its specific mechanism remains unclear. Here, the relationship between dysregulated lipid metabolism genes (LMGs) and ccRCC progression was investigated. Methods: The ccRCC transcriptome data and patients' clinical traits were obtained from several databases. A list of LMGs was selected, differentially expressed gene screening performed to detect differential LMGs, survival analysis performed, a prognostic model established, and immune landscape evaluated using the CIBERSORT algorithm. Gene Set Variation Analysis and Gene set enrichment analysis were conducted to explore the mechanism by which LMGs affect ccRCC progression. Single-cell RNA-sequencing data were obtained from relevant datasets. Immunohistochemistry and RT-PCR were used to validate the expression of prognostic LMGs. Results: Seventy-one differential LMGs were identified between ccRCC and control samples, and a novel risk score model established comprising 11 LMGs (ABCB4, DPEP1, IL4I1, ENO2, PLD4, CEL, HSD11B2, ACADSB, ELOVL2, LPA, and PIK3R6); this risk model could predict ccRCC survival. The high-risk group had worse prognoses and higher immune pathway activation and cancer development. Conclusion: Our results showed that this prognostic model can affect ccRCC progression.
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Affiliation(s)
- Ke Li
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China,Department of Urology, Xiangya Hospital, Central South University, Changsha, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yan Zhu
- Foreign Languages Institute, China University of Geosciences Wuhan, Wuhan, China
| | - Jiawei Cheng
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Anlei Li
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China
| | - Yuxing Liu
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China
| | - Xinyi Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Hao Huang
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Zhangzhe Peng
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China,*Correspondence: Zhangzhe Peng, ; Hui Xu,
| | - Hui Xu
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China,*Correspondence: Zhangzhe Peng, ; Hui Xu,
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Zhan B, Dong X, Yuan Y, Gong Z, Li B. hZIP1 Inhibits Progression of Clear Cell Renal Cell Carcinoma by Suppressing NF-kB/HIF-1α Pathway. Front Oncol 2021; 11:759818. [PMID: 34926261 PMCID: PMC8674186 DOI: 10.3389/fonc.2021.759818] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose Accumulating literature has suggested that hZIP1 and HIF-1α play vital roles in the tumor process of clear cell renal cell carcinoma (ccRCC). However, the functional roles of hZIP1 and HIF-1α in ccRCC remain largely unknown. Methods HIF-1α protein level was evaluated by a western blot in ccRCC tissues and cell lines. ccRCC cell lines were transfected with HIF-1α-siRNA to downregulate the expression level of HIF-1α. Then the proliferative, migratory and invasive abilities of ccRCC cells in vitro were detected by real-time cell analysis (RTCA) assay, wound healing assay and transwell assay, respectively. The role of HIF-1α in vivo was explored by tumor implantation in nude mice. Then the effect on glycolysis‐related proteins was performed by western blot after hZIP1 knockdown (overexpression) or HIF-1α knockdown. The effect on NF‐kB pathway was detected after hZIP1 overexpression. Results HIF-1α was markedly downregulated in ccRCC tissues compared with normal areas. But HIF-1α presented almost no expression in HK-2 and ACHN cells. Immunofluorescence indicated HIF-1α and PDK1 expression in both the cytoplasm and nucleus in ccRCC cells. Downregulation of HIF-1α suppressed ccRCC cell proliferation, migration, and invasion and resulted in smaller implanted tumors in nude mice. Furthermore, hZIP1 knockdown elevated HIF-1α protein levels and PDK1 protein levels in ccRCC cells. Interestingly, a sharp downregulated expression of HIF-1α was observed after hZIP1 overexpression in OSRC-2 and 786-O cells, which resulted from a downtrend of NF-kB1 moving into the cell nucleus. Conclusion Our work has vital implications that hZIP1 suppresses ccRCC progression by inhibiting NF-kB/HIF-1α pathway.
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Affiliation(s)
- Bo Zhan
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Xiao Dong
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Yulin Yuan
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Zheng Gong
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Bohan Li
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
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Wang L, Liu XX, Yang YM, Wang Y, Song YY, Gao S, Li LY, Zhang ZS. RHBDF2 gene functions are correlated to facilitated renal clear cell carcinoma progression. Cancer Cell Int 2021; 21:590. [PMID: 34736454 PMCID: PMC8567583 DOI: 10.1186/s12935-021-02277-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 10/18/2021] [Indexed: 01/05/2023] Open
Abstract
Background The rhomboids are a family of multi-transmembrane proteins, many of which have been implicated in facilitating tumor progression. Little is yet known, however, about rhomboid-associated biomarkers in cancers. An analysis of such biomarkers could yield important insights into the role of the rhomboids in cancer pathology. Methods In this study, we carried out the univariate Cox regression analysis and compared gene expression patterns of several rhomboid genes in 30 types of cancers by using The Cancer Genome Atlas (TCGA) database and the methods delineated in Gene Expression Profiling Interactive Analysis (GEPIA). We then used datasets GSE47032, GSE126964, GSE68417 and 75 paired pathological specimens to verify the influences of the rhomboid genes in cancer progression. Moreover, we carried out Weighted Gene Correlation Network Analysis (WGCNA) to investigate gene-related functions and we exploited potential correlations between rhomboid genes expression and immune cell infiltration in cancer tissues. Furthermore, we constructed gene-knockdown cancer cell lines to investigate rhomboid gene functions. Results We find that kidney renal clear cell carcinoma (KIRC) disease progression is affected by fluctuations in the expression of a number of the rhomboid family of genes and, more specifically, high levels of RHBDF2 gene expression are a good indicator of poor prognosis of the disease, as patients with high RHBDF2 expression levels exhibit less favorable survival rates compared to those with low RHBDF2 levels. Silencing of the RHBDF2 gene in KIRC cell lines leads to significantly diminished cell proliferation and migration; this is in good agreement with the identification of an enhanced presence of a number of cell growth and migration promoting signaling molecules in KIRC tumors. We found that, although high level of RHBDF2 correlated with increased infiltration of lymphocytes in cancer tissues, artificially overexpressed RHBDF2 led to an inhibition of the activity of the infiltrated immune cells through sustaining PD-L1 protein level. Furthermore, we show that RHBDF2 related cell migration and PD-L1 regulation were potentially mediated by EGFR signaling pathway. Conclusions RHBDF2 gene functions are correlated to facilitated renal clear cell carcinoma progression and may serve as a critical prognostic biomarker for the disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02277-0.
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Affiliation(s)
- Lei Wang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China
| | - Xiu-Xiu Liu
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China
| | - Yu-Meng Yang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China
| | - Yan Wang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China
| | - Yuan-Yuan Song
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China
| | - Shan Gao
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China
| | - Lu-Yuan Li
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China.
| | - Zhi-Song Zhang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin, 300350, China.
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Huang H, Zhu L, Huang C, Dong Y, Fan L, Tao L, Peng Z, Xiang R. Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis. Front Oncol 2021; 11:726655. [PMID: 34660292 PMCID: PMC8516333 DOI: 10.3389/fonc.2021.726655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/06/2021] [Indexed: 01/09/2023] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches. Methods Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets. Results In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC. Conclusions The present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.
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Affiliation(s)
- Hao Huang
- Department of Nephrology, Xiangya Hospital Central South University, Changsha, China.,Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Ling Zhu
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Chao Huang
- Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China.,Department of Otolaryngology-Head and Neck Surgery, Second Xiangya Hospital Central South University, Changsha, China
| | - Yi Dong
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Liangliang Fan
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Lijian Tao
- Department of Nephrology, Xiangya Hospital Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Zhangzhe Peng
- Department of Nephrology, Xiangya Hospital Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
| | - Rong Xiang
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China
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Liu H, Yang Y. Identification of Mast Cell-Based Molecular Subtypes and a Predictive Signature in Clear Cell Renal Cell Carcinoma. Front Mol Biosci 2021; 8:719982. [PMID: 34646862 PMCID: PMC8503328 DOI: 10.3389/fmolb.2021.719982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/13/2021] [Indexed: 12/31/2022] Open
Abstract
Background: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. Surgery is the preferred treatment option; however, the rate of distant metastasis is high. Mast cells in the tumor microenvironment promote or inhibit tumorigenesis depending on the cancer type; however, their role in KIRC is not well-established. Here, we used a bioinformatics approach to evaluate the roles of mast cells in KIRC. Methods: To quantify mast cell abundance based on gene sets, a single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze three datasets. Weighted correlation network analysis (WGCNA) was used to identify the genes most closely related to mast cells. To identify new molecular subtypes, the nonnegative matrix factorization algorithm was used. GSEA and least absolute shrinkage and selection operator (LASSO) Cox regression were used to identify genes with high prognostic value. A multivariate Cox regression analysis was performed to establish a prognostic model based on mast cell-related genes. Promoter methylation levels of mast cell-related genes and relationships between gene expression and survival were evaluated using the UALCAN and GEPIA databases. Results: A prolonged survival in KIRC was associated with a high mast cell abundance. KIRC was divided into two molecular subtypes (cluster 1 and cluster 2) based on mast cell-related genes. Genes in Cluster 1 were enriched for various functions related to cancer development, such as the TGFβ signaling pathway, renal cell carcinoma, and mTOR signaling pathway. Based on drug sensitivity predictions, sensitivity to doxorubicin was higher for cluster 2 than for cluster 1. By a multivariate Cox analysis, we established a clinical prognostic model based on eight mast cell-related genes. Conclusion: We identified eight mast cell-related genes and constructed a clinical prognostic model. These results improve our understanding of the roles of mast cells in KIRC and may contribute to personalized medicine.
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Affiliation(s)
- Hanxiang Liu
- Pediatric Urology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yi Yang
- Pediatric Urology, Shengjing Hospital of China Medical University, Shenyang, China
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Xiao L, Zou G, Cheng R, Wang P, Ma K, Cao H, Zhou W, Jin X, Xu Z, Huang Y, Lin X, Nie H, Jiang Q. Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma. BMC Cancer 2021; 21:703. [PMID: 34130646 PMCID: PMC8204412 DOI: 10.1186/s12885-021-08470-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 06/03/2021] [Indexed: 12/20/2022] Open
Abstract
Backgroud Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options. Methods The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model. Results Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/. When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients. Conclusions Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08470-8.
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Affiliation(s)
- Lixing Xiao
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Guoying Zou
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Rui Cheng
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Kexin Ma
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Huimin Cao
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Wenyang Zhou
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Xiyun Jin
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Zhaochun Xu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Yan Huang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Xiaoyu Lin
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Huan Nie
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China.
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China. .,Key Laboratory of Biological Big Data (Harbin Institute of Technology), Ministry of Education, Harbin, China.
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