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Li J, Dan K, Ai J. Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis. Front Immunol 2024; 15:1281940. [PMID: 38835779 PMCID: PMC11148209 DOI: 10.3389/fimmu.2024.1281940] [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: 08/23/2023] [Accepted: 05/08/2024] [Indexed: 06/06/2024] Open
Abstract
Background The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy. Methods Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0. Results A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively. Conclusion Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.
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Affiliation(s)
- Juan Li
- Department of Dermatology, Chongqing Dangdai Plastic Surgery Hospital, Chongqing, China
| | - Kena Dan
- Department of Dermatology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jun Ai
- Department of Dermatology, Chongqing Huamei Plastic Surgery Hospital, Chongqing, China
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Sha Y, Mao AQ, Liu YJ, Li JP, Gong YT, Xiao D, Huang J, Gao YW, Wu MY, Shen H. Nidogen-2 (NID2) is a Key Factor in Collagen Causing Poor Response to Immunotherapy in Melanoma. Pharmgenomics Pers Med 2023; 16:153-172. [PMID: 36908806 PMCID: PMC9994630 DOI: 10.2147/pgpm.s399886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 02/25/2023] [Indexed: 03/06/2023] Open
Abstract
Background The incidence of cutaneous melanoma continues to rise rapidly and has an extremely poor prognosis. Immunotherapy strategies are the most effective approach for patients who have developed metastases, but not all cases have been successful due to the complex and variable mechanisms of melanoma response to immune checkpoint inhibition. Methods We synthesized collagen-coding gene expression data (second-generation and single-cell sequencing) from public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatics analysis was performed using R software and several database resources such as Metascape database, Gene Set Cancer Analysis (GSCA) database, and Cytoscape software, etc., to investigate the biological mechanisms that may be related with collagens. Immunofluorescence and immunohistochemical staining were used to validate the expression and localization of Nidogen-2 (NID2). Results Melanoma patients can be divided into two collagen clusters. Patients with high collagen levels (C1) had a shorter survival than those with low collagen levels (C2) and were less likely to benefit from immunotherapy. We demonstrated that NID2 is a potential key factor in the collagen phenotype, is involved in fibroblast activation in melanoma, and forms a barrier to limit the proximity of CD8+ T cells to tumor cells. Conclusion We clarified the adverse effects of collagen on melanoma patients and identified NID2 as a potential therapeutic target.
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Affiliation(s)
- Yan Sha
- Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - An-Qi Mao
- Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - Yuan-Jie Liu
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, People's Republic of China
| | - Jie-Pin Li
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, People's Republic of China
| | - Ya-Ting Gong
- Departments of Rehabilitation, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - Dong Xiao
- Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - Jun Huang
- Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - Yan-Wei Gao
- Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - Mu-Yao Wu
- Departments of Rehabilitation, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
| | - Hui Shen
- Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People's Republic of China
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Chang J, Jiang Z, Ma T, Li J, Chen J, Ye P, Feng L. Integrating transcriptomics and network analysis-based multiplexed drug repurposing to screen drug candidates for M2 macrophage-associated castration-resistant prostate cancer bone metastases. Front Immunol 2022; 13:989972. [PMID: 36389722 PMCID: PMC9643318 DOI: 10.3389/fimmu.2022.989972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
Metastatic castration-resistant prostate cancer (CRPC) has long been considered to be associated with patient mortality. Among metastatic organs, bone is the most common metastatic site, with more than 90% of advanced patients developing bone metastases (BMs) before 24 months of death. Although patients were recommended to use bone-targeted drugs represented by bisphosphonates to treat BMs of CRPC, there was no significant improvement in patient survival. In addition, the use of immunotherapy and androgen deprivation therapy is limited due to the immunosuppressed state and resistance to antiandrogen agents in patients with bone metastases. Therefore, it is still essential to develop a safe and effective therapeutic schedule for CRPC patients with BMs. To this end, we propose a multiplex drug repurposing scheme targeting differences in patient immune cell composition. The identified drug candidates were ranked from the perspective of M2 macrophages by integrating transcriptome and network-based analysis. Meanwhile, computational chemistry and clinical trials were used to generate a comprehensive drug candidate list for the BMs of CRPC by drug redundancy structure filtering. In addition to docetaxel, which has been approved for clinical trials, the list includes norethindrone, testosterone, menthol and foretinib. This study provides a new scheme for BMs of CRPC from the perspective of M2 macrophages. It is undeniable that this multiplex drug repurposing scheme specifically for immune cell-related bone metastases can be used for drug screening of any immune-related disease, helping clinicians find promising therapeutic schedules more quickly, and providing reference information for drug R&D and clinical trials.
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The Molecular Mechanism of Traditional Chinese Medicine Prescription: Gu-tong Formula in Relieving Osteolytic Bone Destruction. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4931368. [PMID: 35872837 PMCID: PMC9300326 DOI: 10.1155/2022/4931368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/20/2022] [Indexed: 01/01/2023]
Abstract
Bone metastasis is a common complication in patients with advanced tumors, causing pain and bone destruction and affecting their quality of life. Typically, complementary and alternative medicine (CAM), with unique theoretical guidance, has played key roles in the treatment of tumor-related diseases. Gu-tong formula (GTF), as a representative prescription of traditional Chinese medicine, has been demonstrated to be an effective clinical medication for the relief of cancer pain. However, the molecular mechanism of GTF in the treatment of osteolytic metastasis is still unclear. Herein, we employ network pharmacology and molecular dynamics methods to uncover the potential treatment mechanism, indicating that GTF can reduce the levels of serum IL6 and TGFB1 and thus limit the scope of bone cortical damage. Among the active compounds, sesamin and deltoin can bind stably with IL6 and TGFB1, respectively, and have the potential to become anti-inflammatory and anticancer drugs. Although the reasons for the therapeutic effect of GTF are complex and comprehensive, this work provides biological plausibility in the treatment of osteolytic metastases, which has a guiding significance for the treatment of cancer pain with CAM.
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Tian L, Long F, Hao Y, Li B, Li Y, Tang Y, Li J, Zhao Q, Chen J, Liu M. A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning. Front Med (Lausanne) 2022; 9:880326. [PMID: 35479936 PMCID: PMC9035939 DOI: 10.3389/fmed.2022.880326] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/22/2022] [Indexed: 11/24/2022] Open
Abstract
Background Melanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME. Methods Three gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis. Results The six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively. Conclusion Confirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.
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Affiliation(s)
- Luyao Tian
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Fei Long
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Youjin Hao
- Cell Biology and Bioinformatics, College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Bo Li
- Cell Biology and Bioinformatics, College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Yinghong Li
- Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Ying Tang
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Jing Li
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Qi Zhao
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Juan Chen
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
- *Correspondence: Juan Chen
| | - Mingwei Liu
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
- Mingwei Liu
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Guo X, Jessel S, Qu R, Kluger Y, Chen TM, Hollander L, Safirstein R, Nelson B, Cha C, Bosenberg M, Jilaveanu LB, Rimm D, Rothlin CV, Kluger HM, Desir GV. Inhibition of renalase drives tumour rejection by promoting T cell activation. Eur J Cancer 2022; 165:81-96. [PMID: 35219026 PMCID: PMC8940682 DOI: 10.1016/j.ejca.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/30/2021] [Accepted: 01/10/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Although programmed cell death protein 1 (PD-1) inhibitors have revolutionised treatment for advanced melanoma, not all patients respond. We previously showed that inhibition of the flavoprotein renalase (RNLS) in preclinical melanoma models decreases tumour growth. We hypothesised that RNLS inhibition promotes tumour rejection by effects on the tumour microenvironment (TME). METHODS We used two distinct murine melanoma models, studied in RNLS knockout (KO) or wild-type (WT) mice. WT mice were treated with the anti-RNLS antibody, m28, with or without anti-PD-1. 10X single-cell RNA-sequencing was used to identify transcriptional differences between treatment groups, and tumour cell content was interrogated by flow cytometry. Samples from patients treated with immunotherapy were examined for RNLS expression by quantitative immunofluorescence. RESULTS RNLS KO mice injected with wild-type melanoma cells reject their tumours, supporting the importance of RNLS in cells in the TME. This effect was blunted by anti-cluster of differentiation 3. However, MØ-specific RNLS ablation was insufficient to abrogate tumour formation. Anti-RNLS antibody treatment of melanoma-bearing mice resulted in enhanced T cell infiltration and activation and resulted in immune memory on rechallenging mice with injection of melanoma cells. At the single-cell level, treatment with anti-RNLS antibodies resulted in increased tumour density of MØ, neutrophils and lymphocytes and increased expression of IFNγ and granzyme B in natural killer cells and T cells. Intratumoural Forkhead Box P3 + CD4 cells were decreased. In two distinct murine melanoma models, we showed that melanoma-bearing mice treated with anti-RNLS antibodies plus anti-PD-1 had superior tumour shrinkage and survival than with either treatment alone. Importantly, in pretreatment samples from patients treated with PD-1 inhibitors, high RNLS expression was associated with decreased survival (log-rank P = 0.006), independent of other prognostic variables. CONCLUSIONS RNLS KO results in melanoma tumour regression in a T-cell-dependent fashion. Anti-RNLS antibodies enhance anti-PD-1 activity in two distinct aggressive murine melanoma models resistant to PD-1 inhibitors, supporting the development of anti-RNLS antibodies with PD-1 inhibitors as a novel approach for melanomas poorly responsive to anti-PD-1.
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Affiliation(s)
- Xiaojia Guo
- Department of Medicine Section of Nephrology, Yale University, New Haven, CT, USA
| | - Shlomit Jessel
- Department of Medicine Section of Medical Oncology, Yale University, New Haven, CT, USA
| | - Rihao Qu
- Department of Medicine Pathology, Yale University, New Haven, CT, USA
| | - Yuval Kluger
- Department of Medicine Pathology, Yale University, New Haven, CT, USA
| | - Tian-Min Chen
- Department of Medicine Section of Nephrology, Yale University, New Haven, CT, USA
| | - Lindsay Hollander
- Department of Medicine Section of Nephrology, Yale University, New Haven, CT, USA
| | - Robert Safirstein
- Department of Medicine Section of Nephrology, Yale University, New Haven, CT, USA; Department of Medicine VACHS, Yale University, New Haven, CT, USA
| | - Bryce Nelson
- Department of Medicine Pharmacology, Yale University, New Haven, CT, USA
| | - Charles Cha
- Department of Medicine Surgery, Yale University, New Haven, CT, USA
| | - Marcus Bosenberg
- Department of Medicine Section of Medical Oncology, Yale University, New Haven, CT, USA
| | - Lucia B Jilaveanu
- Department of Medicine Section of Medical Oncology, Yale University, New Haven, CT, USA
| | - David Rimm
- Department of Medicine Pathology, Yale University, New Haven, CT, USA
| | - Carla V Rothlin
- Department of Medicine Immunology, Yale University, New Haven, CT, USA
| | - Harriet M Kluger
- Department of Medicine Section of Medical Oncology, Yale University, New Haven, CT, USA
| | - Gary V Desir
- Department of Medicine Section of Nephrology, Yale University, New Haven, CT, USA; Department of Medicine VACHS, Yale University, New Haven, CT, USA; Department of Medicine Yale School of Medicine, Yale University, New Haven, CT, USA.
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7
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Wan Z, Xiong H, Tan X, Su T, Xia K, Wang D. Integrative Multi-Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma. Front Oncol 2022; 11:794146. [PMID: 35096593 PMCID: PMC8795899 DOI: 10.3389/fonc.2021.794146] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/17/2021] [Indexed: 01/10/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Due to the lack of early detection and treatment, the survival rate of OSCC remains poor and the incidence of OSCC has not decreased during the past decades. To explore potential biomarkers and therapeutic targets for OSCC, we analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation-regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining publicly available methylomic datasets together with our transcriptomic data. Protein-protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. Subsequently, expression and survival analyses of hub genes were performed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Profiling Interactive Analysis (GEPIA) online tool. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the PPI networks constructed by those MeDEGs. Among them, the expression level of four hub genes (CTLA4, CDSN, ACTN2, and MYH11) were found to be significantly changed in the head and neck squamous carcinoma (HNSC) patients. Three hypomethylated hub genes (CTLA4, GPR29, and TNFSF11) and one hypermethylated hub gene (ISL1) were found to be significantly associated with overall survival (OS) of HNSC patients. Therefore, these hub genes may serve as potential DNA methylation biomarkers and therapeutic targets of OSCC.
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Affiliation(s)
- Zhengqing Wan
- Hengyang Medical School, University of South China, Hengyang, China.,The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.,Postdoctoral Station for Basic Medicine, Hengyang Medical School, University of South China, Hengyang, China
| | - Haofeng Xiong
- Xiangya Hospital, Central South University, Changsha, China.,Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xian Tan
- Hengyang Medical School, University of South China, Hengyang, China
| | - Tong Su
- Xiangya Hospital, Central South University, Changsha, China
| | - Kun Xia
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Danling Wang
- Hengyang Medical School, University of South China, Hengyang, China.,The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
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Lin L, Huang K, Tu Z, Zhu X, Li J, Lei K, Luo M, Wang P, Gong C, Long X, Wu L. Integrin Alpha-2 as a Potential Prognostic and Predictive Biomarker for Patients With Lower-Grade Glioma. Front Oncol 2021; 11:738651. [PMID: 34778054 PMCID: PMC8578896 DOI: 10.3389/fonc.2021.738651] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 10/06/2021] [Indexed: 12/21/2022] Open
Abstract
Diffuse gliomas are the most common malignant brain tumors with the highest mortality and recurrence rate in adults. Integrin alpha-2 (ITGA2) is involved in a series of biological processes, including cell adhesion, stemness regulation, angiogenesis, and immune/blood cell functions. The role of ITGA2 in lower-grade gliomas (LGGs) is not well defined. Firstly, we downloaded RNA sequencing and relevant clinical information from The Cancer Genome Atlas cohort, the Chinese Glioma Genome Atlas cohort, and related immune cohorts. Next, prognosis analysis, difference analysis, clinical model construction, enrichment analysis, and immune infiltration analysis are performed for this study. These analyses indicated that ITGA2 may have clinical application value and research value in LGG immunotherapy. We also detected the mRNA and protein expression of ITGA2 in three LGG cell lines and normal glial cells using quantitative real-time polymerase chain reaction assay and western blot assay. Our study not only offers a novel target for LGG immunotherapy but also can better comprehend the mechanism of the development and progression of patients with LGG. This study revealed that ITGA2 may be a potential prognostic and predictive biomarker for LGG, which can bring new insights into targeted immunotherapy.
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Affiliation(s)
- Li Lin
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kai Huang
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Scientific Research, East China Institute of Digital Medical Engineering, Shangrao, China.,Institute of Neuroscience, Nanchang University, Nanchang, China
| | - Zewei Tu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Institute of Neuroscience, Nanchang University, Nanchang, China
| | - Xingen Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Institute of Neuroscience, Nanchang University, Nanchang, China
| | - Jingying Li
- Department of Comprehensive Intensive Care Unit, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kunjian Lei
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Min Luo
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Peng Wang
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chuandong Gong
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoyan Long
- Department of Scientific Research, East China Institute of Digital Medical Engineering, Shangrao, China
| | - Lei Wu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Institute of Neuroscience, Nanchang University, Nanchang, China
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Li Y, Gao Y, Niu X, Tang M, Li J, Song B, Guan X. LncRNA BASP1-AS1 interacts with YBX1 to regulate Notch transcription and drives the malignancy of melanoma. Cancer Sci 2021; 112:4526-4542. [PMID: 34533860 PMCID: PMC8586662 DOI: 10.1111/cas.15140] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/08/2021] [Accepted: 09/12/2021] [Indexed: 12/14/2022] Open
Abstract
Melanoma is a fatal skin malignant tumor with a poor prognosis. We found that long noncoding RNA BASP1-AS1 is essential for the development and prognosis of melanoma. The methylation, RNA sequencing, copy number variation, mutation data, and sample follow-up information of melanoma from The Cancer Genome Atlas (TCGA) were analyzed using weighted gene co-expression network analysis and 366 samples common to the three omics were selected for multigroup clustering analysis. A four-gene prognostic model (BASP1-AS1, LOC100506098, ARHGAP27P1, and LINC01532) was constructed in the TCGA cohort and validated using the GSE65904 series. The expression of BASP1-AS1 was upregulated in melanoma tissues and various melanoma cell lines. Functionally, the ectopic expression of BASP1-AS1 promoted cell proliferation, migration, and invasion in both A375 and SK-MEL-2 cells. Mechanically, BASP1-AS1 interacted with YBX1 and recruited it to the promoter of NOTCH3, initiating its transcription process. The activation of the Notch signaling then resulted in the transcription of multiple oncogenes, including c-MYC, PCNA, and CDK4, which contributed to melanoma progression. Thus, BASP1-AS1 could act as a potential biomarker for cutaneous malignant melanoma.
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Affiliation(s)
- YaLing Li
- Department of DermatologyThe First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin DiseasesThe First Hospital of China Medical University and Key Laboratory of ImmunodermatologyMinistry of Health and Ministry of EducationShenyangChina
| | - YaLi Gao
- Department of DermatologyThe First Afflicated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - XueLi Niu
- Department of DermatologyThe First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin DiseasesThe First Hospital of China Medical University and Key Laboratory of ImmunodermatologyMinistry of Health and Ministry of EducationShenyangChina
| | - MingSui Tang
- Department of DermatologyThe First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin DiseasesThe First Hospital of China Medical University and Key Laboratory of ImmunodermatologyMinistry of Health and Ministry of EducationShenyangChina
| | - JingYi Li
- Department of DermatologyThe First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin DiseasesThe First Hospital of China Medical University and Key Laboratory of ImmunodermatologyMinistry of Health and Ministry of EducationShenyangChina
| | - Bing Song
- Department of DermatologyThe First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin DiseasesThe First Hospital of China Medical University and Key Laboratory of ImmunodermatologyMinistry of Health and Ministry of EducationShenyangChina
- School of DentistryCardiff UniversityCardiffUK
| | - XiuHao Guan
- Department of DermatologyThe First Hospital of China Medical University and National Joint Engineering Research Center for Theranostics of Immunological Skin DiseasesThe First Hospital of China Medical University and Key Laboratory of ImmunodermatologyMinistry of Health and Ministry of EducationShenyangChina
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10
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Lai X, Lin P, Ye J, Liu W, Lin S, Lin Z. Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs. Biochem Genet 2021; 60:433-451. [PMID: 34173117 DOI: 10.1007/s10528-021-10101-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/16/2021] [Indexed: 12/18/2022]
Abstract
Ovarian cancer (OVC) is often diagnosed at the advanced stage resulting in a poor overall outcome for the patient. The disease mechanisms, prognosis, and treatment require imperative elucidation. A rank-based module-centric framework was proposed to analyze the key modules related to the development, prognosis, and treatment of OVC. The ovarian cancer cell line microarray dataset GSE43765 from the Gene Expression Omnibus database was used to construct the reference modules by weighted gene correlation network analysis. Twenty-three reference modules were tested for stability and functionally annotated. Furthermore, to demonstrate the utility of reference modules, two more OVC datasets were collected, and their gene expression profiles were projected to the reference modules to generate a module-level expression. An epithelial-mesenchymal transition module was activated in OVC compared to the normal epithelium, and a pluripotency module was activated in ovarian cancer stroma compared to ovarian cancer epithelium. Seven differentially expressed modules were identified in OVC compared to the normal ovarian epithelium, with five up-regulated, and two down-regulated. One module was identified to be predictive of patient overall survival. Four modules were enriched with SNP signals. Based on differentially expressed modules and hub genes, five candidate drugs were screened. The hub genes of those modules merit further investigation. We firstly propose the reference module-based analysis of OVC. The utility of the analysis framework can be extended to transcriptome data of other kinds of diseases.
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Affiliation(s)
- Xuedan Lai
- Department of Gynaecology and Obstetrics, Fuzhou First Hospital Affiliated to Fujian Medical University, Fuzhou, 350009, People's Republic of China
| | - Peihong Lin
- Department of Gynaecology and Obstetrics, Fuzhou First Hospital Affiliated to Fujian Medical University, Fuzhou, 350009, People's Republic of China
| | - Jianwen Ye
- Department of Gynaecology and Obstetrics, Fuzhou First Hospital Affiliated to Fujian Medical University, Fuzhou, 350009, People's Republic of China
| | - Wei Liu
- Department of Bioinformatics, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Shiqiang Lin
- Department of Bioinformatics, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Zhou Lin
- Department of Gynaecology and Obstetrics, Fuzhou First Hospital Affiliated to Fujian Medical University, Fuzhou, 350009, People's Republic of China.
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Garutti M, Bonin S, Buriolla S, Bertoli E, Pizzichetta MA, Zalaudek I, Puglisi F. Find the Flame: Predictive Biomarkers for Immunotherapy in Melanoma. Cancers (Basel) 2021; 13:cancers13081819. [PMID: 33920288 PMCID: PMC8070445 DOI: 10.3390/cancers13081819] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/24/2021] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Immunotherapy has revolutionized the therapeutic landscape of melanoma. In particular, checkpoint inhibition has shown to increase long-term outcome, and, in some cases, it can be virtually curative. However, the absence of clinically validated predictive biomarkers is one of the major causes of unpredictable efficacy of immunotherapy. Indeed, the availability of predictive biomarkers could allow a better stratification of patients, suggesting which type of drugs should be used in a certain clinical context and guiding clinicians in escalating or de-escalating therapy. However, the difficulty in obtaining clinically useful predictive biomarkers reflects the deep complexity of tumor biology. Biomarkers can be classified as tumor-intrinsic biomarkers, microenvironment biomarkers, and systemic biomarkers. Herein we review the available literature to classify and describe predictive biomarkers for checkpoint inhibition in melanoma with the aim of helping clinicians in the decision-making process. We also performed a meta-analysis on the predictive value of PDL-1.
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Affiliation(s)
- Mattia Garutti
- CRO Aviano National Cancer Institute IRCCS, 33081 Aviano, Italy; (E.B.); (M.A.P.); (F.P.)
- Correspondence:
| | - Serena Bonin
- DSM—Department of Medical Sciences, University of Trieste, 34123 Trieste, Italy;
| | - Silvia Buriolla
- Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
- Dipartimento di Oncologia, Azienda Sanitaria Universitaria Friuli Centrale, 33100 Udine, Italy
| | - Elisa Bertoli
- CRO Aviano National Cancer Institute IRCCS, 33081 Aviano, Italy; (E.B.); (M.A.P.); (F.P.)
- Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
| | - Maria Antonietta Pizzichetta
- CRO Aviano National Cancer Institute IRCCS, 33081 Aviano, Italy; (E.B.); (M.A.P.); (F.P.)
- Department of Dermatology, University of Trieste, 34123 Trieste, Italy;
| | - Iris Zalaudek
- Department of Dermatology, University of Trieste, 34123 Trieste, Italy;
| | - Fabio Puglisi
- CRO Aviano National Cancer Institute IRCCS, 33081 Aviano, Italy; (E.B.); (M.A.P.); (F.P.)
- Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
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12
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Rodin AS, Gogoshin G, Hilliard S, Wang L, Egelston C, Rockne RC, Chao J, Lee PP. Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data. Int J Mol Sci 2021; 22:ijms22052316. [PMID: 33652558 PMCID: PMC7956201 DOI: 10.3390/ijms22052316] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 12/11/2022] Open
Abstract
Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.
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Affiliation(s)
- Andrei S. Rodin
- City of Hope National Medical Center, Department of Computational and Quantitative Medicine, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (G.G.); (S.H.); (R.C.R.)
- Correspondence: (A.S.R.); (P.P.L.)
| | - Grigoriy Gogoshin
- City of Hope National Medical Center, Department of Computational and Quantitative Medicine, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (G.G.); (S.H.); (R.C.R.)
| | - Seth Hilliard
- City of Hope National Medical Center, Department of Computational and Quantitative Medicine, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (G.G.); (S.H.); (R.C.R.)
| | - Lei Wang
- City of Hope National Medical Center, Department of Immuno-Oncology, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (L.W.); (C.E.)
| | - Colt Egelston
- City of Hope National Medical Center, Department of Immuno-Oncology, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (L.W.); (C.E.)
| | - Russell C. Rockne
- City of Hope National Medical Center, Department of Computational and Quantitative Medicine, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (G.G.); (S.H.); (R.C.R.)
| | - Joseph Chao
- City of Hope National Medical Center, Department of Medical Oncology & Therapeutics Research, 1500 East Duarte Road, Duarte, CA 91010, USA;
| | - Peter P. Lee
- City of Hope National Medical Center, Department of Immuno-Oncology, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA; (L.W.); (C.E.)
- Correspondence: (A.S.R.); (P.P.L.)
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