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Lv F, Xiong Q, Qi M, Dai C, Zhang X, Cheng S. Unraveling neoantigen-associated genes in bladder cancer: An in-depth analysis employing 101 machine learning algorithms. Environ Toxicol 2024; 39:2528-2544. [PMID: 38189174 DOI: 10.1002/tox.24123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/12/2023] [Accepted: 12/25/2023] [Indexed: 01/09/2024]
Abstract
The therapeutic outcomes for bladder cancer (BLCA) remain suboptimal. Concurrently, there is a growing appreciation for the role of neoantigens in tumors. In this study, we explored the mechanisms underlying the involvement of neoantigen-associated genes in BLCA and their impact on prognosis. Our analysis incorporated both single-cell sequencing and bulk sequencing data sourced from publicly available databases. By employing a comprehensive set of 10 machine learning algorithms, we generated 101 algorithm combinations. The optimal combination, determined based on consistency indices, was utilized to construct a prognostic model comprising nine genes (CAPG, ACTA2, PDIA6, AKNA, PTMS, SNAP23, ID2, CD3G, SP140). Subsequently, we validated this model in an independent cohort, demonstrating its robust testing efficacy. Moreover, we explored the correlations between various clinical traits, model scores, and genes. Leveraging extensive public data resources, we conducted a drug sensitivity analysis to provide insights for targeted drug screening. Additionally, consensus clustering analysis and immune infiltration analysis were performed on bulk sequencing datasets and immunotherapy cohorts. These analyses yield valuable insights into the role of neoantigens in BLCA, guiding future research endeavors.
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Affiliation(s)
- Fang Lv
- Department of General Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Qi Xiong
- Department of Urology, The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Meiying Qi
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Caixia Dai
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiuhong Zhang
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shunhua Cheng
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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Xiao L, He R, Hu K, Song G, Han S, Lin J, Chen Y, Zhang D, Wang W, Peng Y, Zhang J, Yu P. Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning. Apoptosis 2024:10.1007/s10495-024-01960-7. [PMID: 38615305 DOI: 10.1007/s10495-024-01960-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2024] [Indexed: 04/15/2024]
Abstract
The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.
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Affiliation(s)
- Leyang Xiao
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Ruifeng He
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Kaibo Hu
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Gelin Song
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Shengye Han
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Jitao Lin
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Yixuan Chen
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Deju Zhang
- Food and Nutritional Sciences, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, 999077, Hong Kong, Hong Kong
| | - Wuming Wang
- Department of Thoracic Surgery, Jiangxi Provincial Chest Hospital, Nanchang, 330006, People's Republic of China
| | - Yating Peng
- Department of Dermatology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Jing Zhang
- Department of Anesthesiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China.
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, 332000, People's Republic of China.
| | - Peng Yu
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China.
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, 332000, People's Republic of China.
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Xie J, Wu D, Zhang P, Zhao S, Qi M. Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms. Exp Dermatol 2024; 33:e15070. [PMID: 38570935 DOI: 10.1111/exd.15070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024]
Abstract
Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.
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Affiliation(s)
- Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Dan Wu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Pengpeng Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Songyun Zhao
- Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
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Yang R, Sun F, Shi Y, Wang H, Fan Y, Wu Y, Fan R, Wu S, Sun L. Machine Learning-based Development and Validation of a Cell Senescence Predictive and Prognostic Signature in Intrahepatic Cholangiocarcinoma. J Cancer 2024; 15:2810-2828. [PMID: 38577599 PMCID: PMC10988306 DOI: 10.7150/jca.92698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/22/2024] [Indexed: 04/06/2024] Open
Abstract
Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.
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Affiliation(s)
- Ruida Yang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Feidi Sun
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Yu Shi
- Department of Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Huanhuan Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Yangwei Fan
- Department of Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Yinying Wu
- Department of Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Ruihan Fan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Shaobo Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Liankang Sun
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
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Wang X, Chan S, Chen J, Xu Y, Dai L, Han Q, Wang Z, Zuo X, Yang Y, Zhao H, Wang M, Wang C, Li Z, Zhang H, Chen W. Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer. Cancer Cell Int 2024; 24:52. [PMID: 38297270 PMCID: PMC10829178 DOI: 10.1186/s12935-024-03239-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). METHODS 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified. RESULTS The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. CONCLUSION We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Jiajie Chen
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yang Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Hu Zhao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Chen Wang
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Zichen Li
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Huabing Zhang
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, 230032, Anhui, China.
- The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, 239000, Anhui, China.
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China.
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Zou M, Li H, Su D, Xiong Y, Wei H, Wang S, Sun H, Wang T, Xi Q, Zuo Y, Yang L. Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes. Brief Bioinform 2023; 25:bbad430. [PMID: 38040491 PMCID: PMC10783866 DOI: 10.1093/bib/bbad430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/29/2023] [Accepted: 11/05/2023] [Indexed: 12/03/2023] Open
Abstract
Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.
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Affiliation(s)
- Min Zou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hongmei Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qilemuge Xi
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot 010010, China
- Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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