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Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [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] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
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Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
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Dai D, Zhuang H, Shu M, Chen L, Long C, Wu H, Chen B. Identification of N7-methylguanosine-related miRNAs as potential biomarkers for prognosis and drug response in breast cancer. Heliyon 2024; 10:e29326. [PMID: 38628712 PMCID: PMC11017060 DOI: 10.1016/j.heliyon.2024.e29326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 03/29/2024] [Accepted: 03/31/2024] [Indexed: 04/19/2024] Open
Abstract
Objectives The impact of N7-methylguanosine (m7G) on tumor progression and the regulatory role of microRNAs (miRNAs) in immune function significantly influence breast cancer (BC) prognosis. Investigating the interplay between m7G modification and miRNAs provides novel insights for assessing prognostics and drug responses in BC. Materials and methods RNA sequences (miRNA and mRNA profiles) and clinical data for BC were acquired from the Cancer Genome Atlas (TCGA) database. A miRNA signature associated with 15 m7G in this cohort was identified using Cox regression and LASSO. The risk score model was evaluated using Kaplan-Meier and time-dependent ROC analysis, categorizing patients into high-risk and low-risk groups. Functional enrichment analyses were conducted to explore potential pathways. The immune system, including scores, cell infiltration, function, and drug sensitivity, was examined and compared between high-risk and low-risk groups. A nomogram that combines risk scores and clinical factors was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) was employed to explore m7G-related miRNA signatures and immune cell relationships in the tumor microenvironment. Additionally, drug susceptibility was compared between risk groups. Results Fifteen m7G-related miRNAs were independently correlated with overall survival (OS) in BC patients. Time-dependent ROC analysis yielded area under the curve (AUC) values of 0.742, 0.726, and 0.712 for predicting 3-, 5-, and 10-year survival rates, respectively. The Kaplan-Meier analysis revealed a significant disparity in OS between the high-risk and low-risk groups (p = 1.3e-6). Multiple regression identified the risk score as a significant independent prognostic factor. An excellent calibration nomogram with a C-index of 0.785 (95 % CI: 0.728-0.843) was constructed. In immune analysis, low-risk patients exhibited heightened immune function and increased responsiveness to immunotherapy and chemotherapy compared to high-risk patients. Conclusion This study systematically analyzed m7G-related miRNAs and revealed their regulatory mechanisms concerning the tumor microenvironment (TME), pathology, and the prognosis of BC patient. Based on these miRNAs, a prognostic model and nomogram were developed for BC patients, facilitating prognostic assessments. These findings can also assist in predicting treatment responses and guiding medication selection.
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Affiliation(s)
- Danian Dai
- Department of Vascular and Plastic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510080, China
| | - Hongkai Zhuang
- Department of Hepatobiliary Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Mao Shu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Lezi Chen
- Department of Vascular and Plastic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510080, China
| | - Chen Long
- Department of Pathology, Yueyang Maternal Child Health-Care Hospital, Yueyang, 414000, Hunan, China
| | - Hongmei Wu
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Bo Chen
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
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Peng C, Wang Y, Zhang H, Chen P. The platelet-related genes associated with the prognosis of HCC by regulating cycling T cell and prolif-TAMs. Heliyon 2024; 10:e26798. [PMID: 38486758 PMCID: PMC10938119 DOI: 10.1016/j.heliyon.2024.e26798] [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: 05/23/2023] [Revised: 01/15/2024] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Accumulating evidence highlighted the important roles of platelets in the prognosis and progression of various tumors. Nevertheless, the role of platelet-related genes (PRGs) in HCC remains limited. In this work, 92 differentially expressed PRGs were described in HCC using TCGA and ICGC databases. Then, based on the different expressions of PRGs, we explored two subtypes and developed the PRGs prognostic signature in HCC. The PRGs signature was an independent prognosis factor associated with immune cell infiltration in HCC. Furthermore, two external validation sets verified the expression and prognosis of the PRGs signature gene in HCC. Finally, scRNA-seq analysis demonstrated that the signature genes (CENPE and KIF2C) were mainly expressed in cycling T cells and prolif-TAMs. Enrichment analysis showed that CENPE and KIF2C regulated the cell cycle and p53 pathways in these cells. In conclusion, this study builds the PRGs-related risk signature of HCC and reveals the potential mechanism by which these signature genes regulate the immune microenvironment in HCC.
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Affiliation(s)
- Chenjia Peng
- School of Mathematics and Computational Science, Hunan First Normal University, Changsha, 410205, PR China
- The National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, PR China
| | - Ying Wang
- The National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, PR China
| | - Hengbo Zhang
- Physical Education Department, First Hunan Normal University, Changsha, 410081, PR China
| | - Ping Chen
- The National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, PR China
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Chen W, Kang Y, Sheng W, Huang Q, Cheng J, Pei S, Meng Y. A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis. Front Immunol 2024; 15:1331841. [PMID: 38370403 PMCID: PMC10869553 DOI: 10.3389/fimmu.2024.1331841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Background Breast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients. Methods BRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the 'limma' package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function. Results Our study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells. Conclusion Our 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.
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Affiliation(s)
- Wenlong Chen
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Yakun Kang
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wenyi Sheng
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Qiyan Huang
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Jiale Cheng
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Shengbin Pei
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - You Meng
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
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Diao Y, Zhao Y, Li X, Li B, Huo R, Han X. A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis. Front Immunol 2023; 14:1286203. [PMID: 38054005 PMCID: PMC10694245 DOI: 10.3389/fimmu.2023.1286203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/03/2023] [Indexed: 12/07/2023] Open
Abstract
Background Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings. Methods A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score. Results A PLT count <100×109/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model's performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542. Conclusion Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.
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Affiliation(s)
| | | | | | | | | | - Xiaoxu Han
- National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, China
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Zhang F, Zhang R, Zong J, Hou Y, Zhou M, Yan Z, Li T, Gan W, Lv S, Yang L, Zeng Z, Zhao W, Yang M. Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients. Front Immunol 2023; 14:1279789. [PMID: 37928532 PMCID: PMC10620970 DOI: 10.3389/fimmu.2023.1279789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/27/2023] [Indexed: 11/07/2023] Open
Abstract
Background Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism. Methods CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR. Results We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility. Conclusion We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.
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Affiliation(s)
- Fang Zhang
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rixin Zhang
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jinbao Zong
- Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, China
- Qingdao Hospital of Traditional Chinese Medicine, The Affiliated Qingdao Hiser Hospital of Qingdao University, Qingdao, China
| | - Yufang Hou
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingxuan Zhou
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zheng Yan
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tiegang Li
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenqiang Gan
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Silin Lv
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liu Yang
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zifan Zeng
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyi Zhao
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Min Yang
- State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Tan Z, Fu S, Zuo J, Wang J, Wang H. Prognosis analysis and validation of lipid metabolism-associated lncRNAs and tumor immune microenvironment in bladder cancer. Aging (Albany NY) 2023; 15:8384-8407. [PMID: 37632832 PMCID: PMC10496992 DOI: 10.18632/aging.204975] [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: 03/27/2023] [Accepted: 07/25/2023] [Indexed: 08/28/2023]
Abstract
BACKGROUND Numerous types of research revealed that long noncoding RNAs (lncRNAs) played a significant role in immune response and the tumor microenvironment of bladder cancer (BLCA). Dysregulated lipid metabolism is considered to be one of the major risk factors for BLCA, the study aimed to detect the lipid metabolism-related lncRNAs (LMRLs) along with their potential prognostic values and immune correlations in BLCA. METHODS We collected lipid metabolism-related genes, expression profiles, and clinical information on BLCA from the Molecular Signature Database (MSigDB) and the TCGA database, respectively. Differentially expressed lipid metabolism genes (DE-LMRGs) and differentially expressed long non-coding RNAs (DE-lncRNAs) were selected using the limma package. Spearman correlation analysis was employed to explore the correlations between DE-lncRNAs and DE-LMRGs and to further develop protein-protein interaction (PPI) networks and perform mutational analysis. The least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis were then employed to construct a prognostic risk model. The performance of the model was evaluated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and consistency indices. In addition, we downloaded the GSE31684 dataset for external validation of the prognostic signature. Moreover, we explored the association of the risk model with immune cell infiltration and chemotherapy response analysis to reveal the tumor immune microenvironment of BLCA. Finally, RT-qPCR was utilized to validate the expression of prognostic genes. RESULTS A total of 48 DE-LncRNAs and 33 DE-LMRGs were found to be robustly correlated, and were used to construct a lncRNA-mRNA co-expression network, in which ACACB, ACOX2, and BCHE showed high mutation rates. Then, a risk model based on three LMRLs (RP11-465B22.8, MIR100HG, and LINC00865) was constructed. The risk model effectively distinguished between the clinical outcomes of BLCA patients, with high-risk scores indicating a worse prognosis and with substantial prognostic prediction accuracy. The model's results were consistent in the GSE31684 dataset. In addition, a nomogram was constructed based on the risk score, age, pathological T-stage, and pathological N-stage, which showed robust predictive power. Immune landscape analysis indicated that the risk model was significantly associated with T-cell CD4 memory activation, M1 macrophage, M2 macrophage, dendritic cell activation, and T-cell regulatory. We predicted that 49 drugs would perform satisfactorily in the high-risk group. Additionally, we found five m6A regulators associated with the high- and low-risk groups, suggesting that upstream regulation of LncRNA could be a novel target for BLCA treatment. Finally, RT-qPCR showed that RP11-465B22.8 was highly expressed in BLCA, while MIR100HG and LINC00865 were downregulated in BLCA. CONCLUSION Our findings suggest that the three LMRLs may serve as potential prognostic and immunotherapeutic biomarkers in BLCA. In addition, our study provides new ideas for understanding the pathogenic mechanisms and developing therapeutic strategies for BLCA patients.
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Affiliation(s)
- Zhiyong Tan
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Urological Disease Clinical Medical Center of Yunnan, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
| | - Shi Fu
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Urological Disease Clinical Medical Center of Yunnan, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
| | - Jieming Zuo
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Urological Disease Clinical Medical Center of Yunnan, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
| | - Jiansong Wang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Urological Disease Clinical Medical Center of Yunnan, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
| | - Haifeng Wang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Urological Disease Clinical Medical Center of Yunnan, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, People’s Republic of China
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Zhao S, Gong H, Liang W. Characterization of platelet-related genes and constructing signature combined with immune-related genes for predicting outcomes and immunotherapy response in lung squamous cell carcinoma. Aging (Albany NY) 2023; 15:6969-6992. [PMID: 37477536 PMCID: PMC10415560 DOI: 10.18632/aging.204886] [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: 03/15/2023] [Accepted: 06/26/2023] [Indexed: 07/22/2023]
Abstract
Lung squamous cell carcinoma (LUSC) is a highly malignant subtype of non-small cell lung cancer with poor prognosis. Platelets are known to play a critical role in cancer development and progression, and recent studies suggest that they can also regulate immune response in tumors. However, the relationship between platelet-related genes (PRGs) and LUSC prognosis and tumor microenvironments remains unclear. In this study, we used multiple bioinformatics algorithms to identify 25 dysregulated PRGs that were significantly associated with LUSC prognosis. We found that PRGs were involved in multiple biological processes, particularly in the tumor microenvironment, and that platelet-related scores (PRS) were a risk factor. Additionally, we established a 6-gene prognostic signature combining PRGs and immune-related genes that accurately predicted outcomes and immunotherapy efficacy in LUSC patients. Our study provides a comprehensive analysis of the biological functions and potential therapeutic targets of PRGs in LUSC, which may inform the development of new treatments for this disease.
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Affiliation(s)
- Siyi Zhao
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University and Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, Guangzhou, China
- Department of Clinical Medicine, The First Clinical Medical School of Guangzhou Medical University, Guangzhou, China
| | - Han Gong
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University and Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, Guangzhou, China
- Molecular Biology Research Center and Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Wenhua Liang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University and Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, Guangzhou, China
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Liu Z, Ding M, Qiu P, Pan K, Guo Q. Natural killer cell-related prognostic risk model predicts prognosis and treatment outcomes in triple-negative breast cancer. Front Immunol 2023; 14:1200282. [PMID: 37520534 PMCID: PMC10373504 DOI: 10.3389/fimmu.2023.1200282] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Background Natural killer (NK) cells are crucial to the emergence, identification, and prognosis of cancers. The roles of NK cell-related genes in the tumor immune microenvironment (TIME) and immunotherapy treatment are unclear. Triple-negative breast cancer (TNBC) is a highly aggressive malignant tumor. Hence, this study was conducted to develop a reliable risk model related to NK cells and provide a novel system for predicting the prognosis of TNBC. Methods NK cell-related genes were collected from previous studies. Based on TCGA and GEO database, univariate and LASSO cox regression analysis were used to establish the NK cell-related gene signature. The patients with TNBC were separated to high-risk and low-risk groups. After that, survival analysis was conducted and the responses to immunotherapies were evaluated on the basis of the signature. Moreover, the drug sensitivity of some traditional chemotherapeutic drugs was assessed by using the "oncoPredict" R package. In addition, the expression levels of the genes involved in the signature were validated by using qRT-PCR in TNBC cell lines. Results The patients with TNBC were divided into high- and low-risk groups according to the median risk score of the 5-NK cell-related gene signature. The low-risk group was associated with a better clinical outcome. Besides, the differentially expressed genes between the different risk groups were enriched in the biological activities associated with immunity. The tumor immune cells were found to be highly infiltrated in the low-risk groups. In accordance with the TIDE score and immune checkpoint-related gene expression analysis, TNBC patients in the low-risk groups were suggested to have better responses to immunotherapies. Eventually, some classical anti-tumor drugs were shown to be less effective in high-risk groups than in low-risk groups. Conclusion The 5-NK cell-related gene signature exhibit outstanding predictive performance and provide fresh viewpoints for evaluating the success of immunotherapy. It will provide new insights to achieve precision and integrated treatment for TNBC in the future.
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Affiliation(s)
- Zundong Liu
- Stem Cell Laboratory, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Mingji Ding
- Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Pengjun Qiu
- Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Kelun Pan
- Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Qiaonan Guo
- Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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10
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Wang Y, Lv W, Yi Y, Zhang Q, Zhang J, Wu Y. A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer. Aging (Albany NY) 2023; 15:3480-3497. [PMID: 37142271 PMCID: PMC10449298 DOI: 10.18632/aging.204685] [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: 01/17/2023] [Accepted: 04/17/2023] [Indexed: 05/06/2023]
Abstract
Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low- and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.
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Affiliation(s)
- Yichen Wang
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Wenchang Lv
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Yi Yi
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Qi Zhang
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Jun Zhang
- Department of Thyroid and Breast Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen 518067, Guangdong, China
| | - Yiping Wu
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
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11
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Deng D, Li X, Qi T, Dai Y, Liu N, Li H. A novel platelet risk score for stratifing the tumor immunophenotypes, treatment responses and prognosis in bladder carcinoma: results from real-world cohorts. Front Pharmacol 2023; 14:1187700. [PMID: 37214475 PMCID: PMC10192868 DOI: 10.3389/fphar.2023.1187700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/18/2023] [Indexed: 05/24/2023] Open
Abstract
Background: Although the durable efficacy of immune checkpoint inhibitors (ICIs) in BLCA has been confirmed in numerous studies, not all patients benefit from their application in the clinic. Platelets are increasingly being found to be closely associated with cancer progression and metastasis; however, their comprehensive role in BLCA remains unclear. Methods: We comprehensively explored platelet expression patterns in BLCA patients using an integrated set of 244 related genes. Correlations between these platelet patterns with tumor microenvironment (TME) subtypes, immune characteristics and immunotherapy efficacies were explored. In addition, a platelet risk score (PRS) was generated for individual prognosis and verified the ability to predict prognosis, precise TME phenotypes, and immunotherapy efficacies. Results: Genes were clustered into two patterns that represented different TME phenotypes and had the ability to predict immunotherapy efficacy. We constructed a PRS that could predict individual prognosis with satisfactory accuracy using TCGA-BLCA. The results remained consistent when PRS was validated in the GSE32894 and Xiangya cohort. Moreover, we found that our PRS was positively related to tumor-infiltrating lymphocytes (TILs) in the TCGA-BLCA and Xiangya cohort. As expected, patients with higher PRS exhibited more sensitive to immunotherapy than patients with lower PRS. Finally, we discovered that a high PRS indicated a basal subtype of BLCA, whereas a low PRS indicated a luminal subtype. Conclusion: Platelet-related genes could predict TME phenotypes in BLCA. We constructed a PRS that could predict the TME, prognosis, immunotherapy efficacy, and molecular subtypes in BLCA.
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Affiliation(s)
- Dingshan Deng
- Department of Urology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiaowen Li
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Tiezheng Qi
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Yuanqing Dai
- Department of Urology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Neng Liu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Huihuang Li
- Department of Urology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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12
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Xie J, Luo X, Deng X, Tang Y, Tian W, Cheng H, Zhang J, Zou Y, Guo Z, Xie X. Advances in artificial intelligence to predict cancer immunotherapy efficacy. Front Immunol 2023; 13:1076883. [PMID: 36685496 PMCID: PMC9845588 DOI: 10.3389/fimmu.2022.1076883] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023] Open
Abstract
Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.
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Affiliation(s)
- Jindong Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiyuan Luo
- School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinpei Deng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhui Tang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenwen Tian
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hui Cheng
- School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Junsheng Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yutian Zou
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
| | - Zhixing Guo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
| | - Xiaoming Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
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13
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Deng X, Zheng C, Tang F, Rosol TJ, Shao ZM. Editorial: Triple-negative breast cancer: Heterogeneity, tumor microenvironment and targeted therapy. Front Oncol 2022; 12:1026566. [PMID: 36483047 PMCID: PMC9725095 DOI: 10.3389/fonc.2022.1026566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/02/2022] [Indexed: 08/30/2023] Open
Affiliation(s)
- Xiyun Deng
- Key Laboratory of Translational Cancer Stem Cell Research, Hunan Normal University School of Medicine, Changsha, Hunan, China
| | - Chanjuan Zheng
- Key Laboratory of Translational Cancer Stem Cell Research, Hunan Normal University School of Medicine, Changsha, Hunan, China
| | - Faqing Tang
- Clinical Laboratory of Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan Key Laboratory of Oncotarget Gene, Changsha, Hunan, China
| | - Thomas J. Rosol
- Department of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, United States
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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14
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Xie J, Zheng S, Zou Y, Tang Y, Tian W, Wong CW, Wu S, Ou X, Zhao W, Cai M, Xie X. Turning up a new pattern: Identification of cancer-associated fibroblast-related clusters in TNBC. Front Immunol 2022; 13:1022147. [PMID: 36275659 PMCID: PMC9583405 DOI: 10.3389/fimmu.2022.1022147] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 09/16/2022] [Indexed: 11/17/2022] Open
Abstract
Growing evidence indicates a connection between cancer-associated fibroblasts (CAFs) and tumor microenvironment (TME) remodeling and tumor progression. Nevertheless, how patterns of CAFs impact TME and immunotherapy responsiveness in triple-negative breast cancer (TNBC) remains unclear. Here, we systematically investigate the relationship between TNBC progression and patterns of CAFs. By using unsupervised clustering methods in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, we identified two distinct CAF-associated clusters that were related to clinical features, characteristics of TME, and prognosis of patients. Then, we established a CAF-related prognosis index (CPI) by the least absolute shrinkage and selection operator (LASSO)-Cox regression method. CPI showed prognostic accuracy in both training and validation cohorts (METABRIC, GSE96058, and GSE21653). Consequently, we constructed a nomogram with great predictive performance. Moreover, the CPI was verified to be correlated with the responsiveness of immunotherapy in three independent cohorts (GSE91061, GSE165252, and GSE173839). Taken together, the CPI might help us improve our recognition of the TME of TNBC, predict the prognosis of TNBC patients, and offer more immunotherapy strategies in the future.
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Affiliation(s)
- Jindong Xie
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shaoquan Zheng
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yutian Zou
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhui Tang
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenwen Tian
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chau-Wei Wong
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Song Wu
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xueqi Ou
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wanzhen Zhao
- Department of Radiotherapy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Manbo Cai
- Department of Radiotherapy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- *Correspondence: Xiaoming Xie, ; Manbo Cai,
| | - Xiaoming Xie
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- *Correspondence: Xiaoming Xie, ; Manbo Cai,
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15
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Yuan M, Jia Y, Xing Y, Wang Y, Liu Y, Liu X, Liu D. Screening and validation of platelet activation-related lncRNAs as potential biomarkers for prognosis and immunotherapy in gastric cancer patients. Front Genet 2022; 13:965033. [PMID: 36186426 PMCID: PMC9515443 DOI: 10.3389/fgene.2022.965033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Platelets (PLT) have a significant effect in promoting cancer progression and hematogenous metastasis. However, the effect of platelet activation-related lncRNAs (PLT-related lncRNAs) in gastric cancer (GC) is still poorly understood. In this study, we screened and validated PLT-related lncRNAs as potential biomarkers for prognosis and immunotherapy in GC patients.Methods: We obtained relevant datasets from the Cancer Genome Atlas (TCGA) and Gene Ontology (GO) Resource Database. Pearson correlation analysis was used to identify PLT-related lncRNAs. By using the univariate, least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we constructed the PLT-related lncRNAs model. Kaplan-Meier survival analysis, univariate, multivariate Cox regression analysis, and nomogram were used to verify the model. The Gene Set Enrichment Analysis (GSEA), drug screening, tumor immune microenvironment analysis, epithelial-mesenchymal transition (EMT), and DNA methylation regulators correlation analysis were performed in the high- and low-risk groups. Patients were regrouped based on the risk model, and candidate compounds and immunotherapeutic responses aimed at GC subgroups were also identified. The expression of seven PLT-related lncRNAs was validated in clinical medical samples using quantitative reverse transcription-polymerase chain reaction (qRT-PCR).Results: In this study, a risk prediction model was established using seven PLT-related lncRNAs -(AL355574.1, LINC01697, AC002401.4, AC129507.1, AL513123.1, LINC01094, and AL356417.2), whose expression were validated in GC patients. Kaplan-Meier survival analysis, the receiver operating characteristic (ROC) curve analysis, univariate, multivariate Cox regression analysis verified the accuracy of the model. We screened multiple targeted drugs for the high-risk patients. Patients in the high-risk group had a poorer prognosis since low infiltration of immune killer cells, activation of immunosuppressive pathways, and poor response to immunotherapy. In addition, we revealed a close relationship between risk scores and EMT and DNA methylation regulators. The nomogram based on risk score suggested a good ability to predict prognosis and high clinical benefits.Conclusion: Our findings provide new insights into how PLT-related lncRNAs biomarkers affect prognosis and immunotherapy. Also, these lncRNAs may become potential biomarkers and therapeutic targets for GC patients.
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Affiliation(s)
- Mingjie Yuan
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Department of Laboratory, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yanfei Jia
- Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Yuanxin Xing
- Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Yunshan Wang
- Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Yunyun Liu
- Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiangdong Liu
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Xiangdong Liu, ; Duanrui Liu,
| | - Duanrui Liu
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Xiangdong Liu, ; Duanrui Liu,
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16
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Xia Y, Lin X, Cheng Y, Xu H, Zeng J, Xie W, Wang M, Sun Y. Characterization of Platelet Function-Related Gene Predicting Survival and Immunotherapy Efficacy in Gastric Cancer. Front Genet 2022; 13:938796. [PMID: 35836573 PMCID: PMC9274243 DOI: 10.3389/fgene.2022.938796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022] Open
Abstract
Immunotherapy is widely used to treat various cancers, but patients with gastric cancer (GC), which has a high mortality rate, benefit relatively less from this therapy. Platelets are closely related to GC progression and metastasis. This study aimed to find novel potential biomarkers related to platelet function to predict GC and immunotherapy efficacy. First, based on platelet activation, signaling, and aggregation (abbreviation: function)-related genes (PFRGs), we used the least absolute shrinkage and selection operator (Lasso) regression method to construct a platelet-function-related genes prognostic score (PFRGPS). PRFGPS was verified in three independent external datasets (GSE26901, GSE15459, and GSE84437) for its robustness and strong prediction performance. Our results demonstrate that PRFGPS is an independent prognostic indicator for predicting overall survival in patients with GC. In addition, prognosis, potential pathogenesis mechanisms, and the response to immunotherapy were defined via gene set enrichment analysis, tumor mutational burden, tumor microenvironment, tumor immune dysfunction and exclusion (TIDE), microsatellite instability, and immune checkpoint inhibitors. We found that the high-PRFGPS subgroup had a cancer-friendly immune microenvironment, a high TIDE score, a low tumor mutational burden, and relatively low microsatellite instability. In the immunophenoscore model, the therapeutic effect on anti-PD-1 and anti-CTLA-4 in the high-PRFGPS subgroup was relatively low. In conclusion, PRFGPS could be used as a reference index for GC prognosis to develop more successful immunotherapy strategies.
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Affiliation(s)
- Yan Xia
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
- Scientific Research Center, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xin Lin
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yangyang Cheng
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
| | - Huimin Xu
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jingya Zeng
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wanlin Xie
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
| | - Mingzhu Wang
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yihua Sun
- Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China
- *Correspondence: Yihua Sun,
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17
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Zou Y, Xie J, Tian W, Wu L, Xie Y, Huang S, Tang Y, Deng X, Wu H, Xie X. Integrative Analysis of KCNK Genes and Establishment of a Specific Prognostic Signature for Breast Cancer. Front Cell Dev Biol 2022; 10:839986. [PMID: 35656548 PMCID: PMC9152175 DOI: 10.3389/fcell.2022.839986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/29/2022] [Indexed: 12/19/2022] Open
Abstract
Two-pore domains potassium channel subunits, encoded by KCNK genes, play vital roles in breast cancer progression. However, the characteristics of most KCNK genes in breast cancer has yet to be clarified. In this study, we comprehensively analyzed the expression, alteration, prognosis, and biological functions of various KCNKs in breast cancer. The expression of KCNK1/4/6/9/10/13 were significantly upregulated, while KCNK2/3/5/7/17 were downregulated in breast cancer tissues compared to normal mammary tissues. Increased expression of KCNK1/3/4/9 was correlated with poor overall survival, while high expression of KCNK2/7/17 predicted better overall survival in breast cancer. Eight KCNK genes were altered in breast cancer patients with a genomic mutation rate ranged from 1.9% to 21%. KCNK1 and KCNK9 were the two most common mutations in breast cancer, occurred in 21% and 18% patients, respectively. Alteration of KCNK genes was associated with the worse clinical characteristics and higher TMB, MSI, and hypoxia score. Using machine learning method, a specific prognostic signature with seven KCNK genes was established, which manifested accuracy in predicting the prognosis of breast cancer in both training and validation cohorts. A nomogram with great predictive performance was afterwards constructed through incorporating KCNK-based risk score with clinical features. Furthermore, KCNKs were correlated with the activation of several tumor microenvironment cells, including T cells, mast cells, macrophages, and platelets. Presentation of antigen, stimulation of G protein signaling and toll-like receptor cascaded were regulated by KCNKs family. Taken together, KCNKs may regulate breast cancer progression via modulating immune response which can serve as ideal prognostic biomarkers for breast cancer patients. Our study provides novel insight for future studies evaluating their usefulness as therapeutic targets.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Hao Wu
- *Correspondence: Hao Wu, ; Xinhua Xie,
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18
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Xie J, Tian W, Tang Y, Zou Y, Zheng S, Wu L, Zeng Y, Wu S, Xie X, Xie X. Establishment of a Cell Necroptosis Index to Predict Prognosis and Drug Sensitivity for Patients With Triple-Negative Breast Cancer. Front Mol Biosci 2022; 9:834593. [PMID: 35601830 PMCID: PMC9117653 DOI: 10.3389/fmolb.2022.834593] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/04/2022] [Indexed: 12/25/2022] Open
Abstract
Background: Necroptosis has been an alternatively identified mechanism of programmed cancer cell death, which plays a significant role in cancer. However, research about necroptosis-related long noncoding RNAs (lncRNAs) in cancer are still few. Moreover, the potentially prognostic value of necroptosis-related lncRNAs and their correlation with the immune microenvironment remains unclear. The present study aimed to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship to immune microenvironment in triple-negative breast cancer (TNBC). Methods: The RNA expression matrix of patients with TNBC was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Finally, 107 patients of GSE58812, 159 patients of TCGA, and 143 patients of GSE96058 were included. Necroptosis-related lncRNAs were screened by Cox regression and Pearson correlation analysis with necroptosis-related genes. By LASSO regression analysis, nine necroptosis-related lncRNAs were employed, and a cell necroptosis index (CNI) was established; then, we evaluated its prognostic value, clinical significance, pathways, immune infiltration, and chemotherapeutics efficacy. Results: Based on the CNI value, the TNBC patients were divided into high- and low-CNI groups, and the patients with high CNI had worse prognosis, more lymph node metastasis, and larger tumor (p < 0.05). The receiver operating characteristic (ROC) analysis showed that the signature performed well. The result of the infiltration proportion of different immune cell infiltration further explained that TNBC patients with high CNI had low immunogenicity, leading to poor therapeutic outcomes. Moreover, we found significant differences of the IC50 values of various chemotherapeutic drugs in the two CNI groups, which might provide a reference to make a personalized chemotherapy for them. Conclusion: The novel prognostic marker CNI could not only precisely predict the survival probability of patients with TNBC but also demonstrate a potential role in antitumor immunity and drug sensitivity.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Xinhua Xie
- *Correspondence: Xinhua Xie, ; Xiaoming Xie,
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19
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Tang Y, Tian W, Xie J, Zou Y, Wang Z, Li N, Zeng Y, Wu L, Zhang Y, Wu S, Xie X, Yang L. Prognosis and Dissection of Immunosuppressive Microenvironment in Breast Cancer Based on Fatty Acid Metabolism-Related Signature. Front Immunol 2022; 13:843515. [PMID: 35432381 PMCID: PMC9009264 DOI: 10.3389/fimmu.2022.843515] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/28/2022] [Indexed: 12/18/2022] Open
Abstract
Fatty acid metabolism has been deciphered to augment tumorigenesis and disease progression in addition to therapy resistance via strengthened lipid synthesis, storage, and catabolism. Breast cancer is strongly associated with the biological function of fatty acid metabolism owing to the abundant presence of adipocytes in breast tissue. It has been unraveled that tumor cells exhibit considerable plasticity based on fatty acid metabolism, responding to extra-tumoral and a range of metabolic signals, in which tumor microenvironment plays a pivotal role. However, the prognostic significance of fatty acid metabolism in breast cancer remains to be further investigated. Alongside these insights, we retrieved 269 reliable fatty acid metabolism-related genes (FMGs) and identified the landscape of copy number variations and expression level among those genes. Additionally, 11 overall survival-related FMGs were clarified by univariate Cox hazards regression analysis in The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Subsequently, a prognostic signature based on 6 overall survival (OS)-related FMGs was generated using Lasso Cox hazards regression analysis in TCGA dataset and was validated in two external cohorts. The correlation between the signature and several essential clinical parameters, including T, N, and PAM50 subtypes, was unveiled by comparing the accumulating signature value in various degrees. Furthermore, an optimal nomogram incorporating the signature, age, and American Joint Committee on Cancer (AJCC) stage was constructed, and the discrimination was verified by C-index, the calibration curve, and the decision curve analysis. The underlying implications for immune checkpoints inhibitors, the landscape of tumor immune microenvironment, and the predictive significance in therapy resistance to diverse strategies were depicted ultimately. In conclusion, our findings indicate the potential prognostic connotation of fatty acid metabolism in breast cancer, supporting novel insights into breast cancer patients’ prognosis and administrating effective immunotherapy.
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Affiliation(s)
- Yuhui Tang
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenwen Tian
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jindong Xie
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yutian Zou
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zehao Wang
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ning Li
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yan Zeng
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Linyu Wu
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yue Zhang
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Song Wu
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiaoming Xie
- Department of Breast Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lu Yang
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital, Guangzhou, China
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