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Wu X, Liu P, Wang Q, Sun L, Wang Y. A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer. ENVIRONMENTAL TOXICOLOGY 2024. [PMID: 38606991 DOI: 10.1002/tox.24287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/13/2024] [Accepted: 03/31/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI. METHODS Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes. RESULTS This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells. CONCLUSION We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.
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Affiliation(s)
- Xin Wu
- Department of General Surgical Medicine, The First Medicine Center of PLA General Hospital, Beijing, China
| | - Peifa Liu
- Pathology Department, The First Medicine Center of PLA General Hospital, Beijing, China
| | - Qing Wang
- Department of General Surgical Medicine, The First Medicine Center of PLA General Hospital, Beijing, China
| | - Linde Sun
- Department of General Surgical Medicine, The First Medicine Center of PLA General Hospital, Beijing, China
| | - Yu Wang
- Department of General Surgical Medicine, The First Medicine Center of PLA General Hospital, Beijing, China
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Bastedo WE, Scott RW, Arostegui M, Underhill TM. Single-cell analysis of mesenchymal cells in permeable neural vasculature reveals novel diverse subpopulations of fibroblasts. Fluids Barriers CNS 2024; 21:31. [PMID: 38575991 PMCID: PMC10996213 DOI: 10.1186/s12987-024-00535-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 03/25/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND In the choroid plexus and pituitary gland, vasculature is known to have a permeable, fenestrated phenotype which allows for the free passage of molecules in contrast to the blood brain barrier observed in the rest of the CNS. The endothelium of these compartments, along with secretory, neural-lineage cells (choroid epithelium and pituitary endocrine cells) have been studied in detail, but less attention has been given to the perivascular mesenchymal cells of these compartments. METHODS The Hic1CreERT2 Rosa26LSL-TdTomato mouse model was used in conjunction with a PdgfraH2B-EGFP mouse model to examine mesenchymal cells, which can be subdivided into Pdgfra+ fibroblasts and Pdgfra- pericytes within the choroid plexus (CP) and pituitary gland (PG), by histological, immunofluorescence staining and single-cell RNA-sequencing analyses. RESULTS We found that both CP and PG possess substantial populations of distinct Hic1+ mesenchymal cells, including an abundance of Pdgfra+ fibroblasts. Within the pituitary, we identified distinct subpopulations of Hic1+ fibroblasts in the glandular anterior pituitary and the neurosecretory posterior pituitary. We also identified multiple distinct markers of CP, PG, and the meningeal mesenchymal compartment, including alkaline phosphatase, indole-n-methyltransferase and CD34. CONCLUSIONS Novel, distinct subpopulations of mesenchymal cells can be found in permeable vascular interfaces, including the CP, PG, and meninges, and make distinct contributions to both organs through the production of structural proteins, enzymes, transporters, and trophic molecules.
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Affiliation(s)
- William E Bastedo
- Department of Cellular and Physiological Sciences, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
| | - R Wilder Scott
- Department of Cellular and Physiological Sciences, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
- School of Biomedical Engineering and the Biomedical Research Centre, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Martin Arostegui
- Department of Cellular and Physiological Sciences, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
| | - T Michael Underhill
- Department of Cellular and Physiological Sciences, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada.
- School of Biomedical Engineering and the Biomedical Research Centre, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada.
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3
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Zhan W, Hu H, Hao B, Zhu H, Yan T, Zhang J, Wang S, Liu S, Zhang T. Development of machine learning-based malignant pericardial effusion-related model in breast cancer: Implications for clinical significance, tumor immune and drug-therapy. Heliyon 2024; 10:e27507. [PMID: 38463870 PMCID: PMC10923851 DOI: 10.1016/j.heliyon.2024.e27507] [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: 09/25/2023] [Revised: 01/30/2024] [Accepted: 02/29/2024] [Indexed: 03/12/2024] Open
Abstract
Background Malignant pericardial effusion (MPE) is a common complication of advanced breast cancer (BRCA) and plays an important role in BRCA. This study is aims to construct a prognostic model based on MPE-related genes for predicting the prognosis of breast cancer. Methods The BRCA samples are analyzed based on the expression of MPE-related genes by using an unsupervised cluster analysis method. This study processes the data by least absolute shrinkage and selection operator and multivariate Cox analysis, and uses machine learning algorithms to construct BRCA prognostic model and develop web tool. Results BRCA patients are classified into three clusters and a BRCA prognostic model is constructed containing 9 MPE-related genes. There are significant differences in signature pathways, immune infiltration, immunotherapy response and drug sensitivity testing between the high and low-risk groups. Of note, a web-based tool (http://wys.helyly.top/cox.html) is developed to predict overall survival as well as drug-therapy response of BRCA patients quickly and conveniently, which can provide a basis for clinicians to formulate individualized treatment plans. Conclusion The MPE-related prognostic model developed in this study can be used as an effective tool for predicting the prognosis of BRCA and provides new insights for the diagnosis and treatment of BRCA patients.
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Affiliation(s)
- Wendi Zhan
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Haihong Hu
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Bo Hao
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hongxia Zhu
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Ting Yan
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jingdi Zhang
- School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, Hunan, 421001, China
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Siyu Wang
- Department of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Saiyang Liu
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Taolan Zhang
- Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Phase I Clinical Trial Center, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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Liu J, Miao X, Yao J, Wan Z, Yang X, Tian W. Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer. Aging (Albany NY) 2024; 16:2934-2952. [PMID: 38329437 PMCID: PMC10911384 DOI: 10.18632/aging.205524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/27/2023] [Indexed: 02/09/2024]
Abstract
BACKGROUND Thyroid cancer (THCA) is the most common endocrine malignancy having a female predominance. The insulin-like growth factor (IGF) pathway contributed to the unregulated cell proliferation in multiple malignancies. We aimed to explore the IGF-related signature for THCA prognosis. METHOD The TCGA-THCA dataset was collected from the Cancer Genome Atlas (TCGA) for screening of key prognostic genes. The limma R package was applied for differentially expressed genes (DEGs) and the clusterProfiler R package was used for the Gene Ontology (GO) and KEGG analysis of DEGs. Then, the un/multivariate and least absolute shrinkage and selection operator (Lasso) Cox regression analysis was used for the establishment of RiskScore model. Receiver Operating Characteristic (ROC) analysis was used to verify the model's predictive performance. CIBERSORT and MCP-counter algorithms were applied for immune infiltration analysis. Finally, we analyzed the mutation features and the correlation between the RiskScore and cancer hallmark pathway by using the GSEA. RESULT We obtained 5 key RiskScore model genes for patient's risk stratification from the 721 DEGs. ROC analysis indicated that our model is an ideal classifier, the high-risk patients are associated with the poor prognosis, immune infiltration, high tumor mutation burden (TMB), stronger cancer stemness and stronger correlation with the typical cancer-activation pathways. A nomogram combined with multiple clinical features was developed and exhibited excellent performance upon long-term survival quantitative prediction. CONCLUSIONS We constructed an excellent prognostic model RiskScore based on IGF-related signature and concluded that the IGF signal pathway may become a reliable prognostic phenotype in THCA intervention.
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Affiliation(s)
- Junyan Liu
- Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
| | - Xin Miao
- Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
| | - Jing Yao
- Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
| | - Zheng Wan
- Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
| | - Xiaodong Yang
- Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
| | - Wen Tian
- Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
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Kang J, Jiang J, Xiang X, Zhang Y, Tang J, Li L. Identification of a new gene signature for prognostic evaluation in cervical cancer: based on cuproptosis-associated angiogenesis and multi-omics analysis. Cancer Cell Int 2024; 24:23. [PMID: 38200479 PMCID: PMC10782580 DOI: 10.1186/s12935-023-03189-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Patients with recurrent or metastatic cervical cancer are in urgent need of novel prognosis assessment or treatment approaches. In this study, a novel prognostic gene signature was discovered by utilizing cuproptosis-related angiogenesis (CuRA) gene scores obtained through weighted gene co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. To enhance its reliability, the gene signature was refined by integrating supplementary clinical variables and subjected to cross-validation. Meanwhile, the activation of the VEGF pathway was inferred from an analysis of cell-to-cell communication, based on the expression of ligands and receptors in cell transcriptomic datasets. High-CuRA patients had less infiltration of CD8 + T cells and reduced expression of most of immune checkpoint genes, which indicated greater difficulty in immunotherapy. Lower IC50 values of imatinib, pazopanib, and sorafenib in the high-CuRA group revealed the potential value of these drugs. Finally, we verified an independent prognostic gene SFT2D1 was highly expressed in cervical cancer and positively correlated with the microvascular density. Knockdown of SFT2D1 significantly inhibited ability of the proliferation, migration, and invasive in cervical cancer cells. CuRA gene signature provided valuable insights into the prediction of prognosis and immune microenvironment of cervical cancer, which could help develop new strategies for individualized precision therapy for cervical cancer patients.
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Affiliation(s)
- Jiawen Kang
- Department of Gynecologic Oncology, School of Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya, Central South University, Changsha, Hunan, China
- Department of Clinical Medicine, Medical College of Hunan Normal University, Changsha, Hunan, China
| | - Jingwen Jiang
- Department of Clinical Medicine, Medical College of Hunan Normal University, Changsha, Hunan, China
| | - Xiaoqing Xiang
- Department of Clinical Medicine, Medical College of Hunan Normal University, Changsha, Hunan, China
| | - Yong Zhang
- Department of Clinical Medicine, Medical College of Hunan Normal University, Changsha, Hunan, China.
| | - Jie Tang
- Department of Gynecologic Oncology, School of Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya, Central South University, Changsha, Hunan, China.
| | - Lesai Li
- Department of Gynecologic Oncology, School of Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya, Central South University, Changsha, Hunan, China.
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Kitzberger C, Shehzad K, Morath V, Spellerberg R, Ranke J, Steiger K, Kälin RE, Multhoff G, Eiber M, Schilling F, Glass R, Weber WA, Wagner E, Nelson PJ, Spitzweg C. Interleukin-6-controlled, mesenchymal stem cell-based sodium/iodide symporter gene therapy improves survival of glioblastoma-bearing mice. Mol Ther Oncolytics 2023; 30:238-253. [PMID: 37701849 PMCID: PMC10493263 DOI: 10.1016/j.omto.2023.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 08/11/2023] [Indexed: 09/14/2023] Open
Abstract
New treatment strategies are urgently needed for glioblastoma (GBM)-a tumor resistant to standard-of-care treatment with a high risk of recurrence and extremely poor prognosis. Based on their intrinsic tumor tropism, adoptively applied mesenchymal stem cells (MSCs) can be harnessed to deliver the theranostic sodium/iodide symporter (NIS) deep into the tumor microenvironment. Interleukin-6 (IL-6) is a multifunctional, highly expressed cytokine in the GBM microenvironment including recruited MSCs. MSCs engineered to drive NIS expression in response to IL-6 promoter activation offer the possibility of a new tumor-targeted gene therapy approach of GBM. Therefore, MSCs were stably transfected with an NIS-expressing plasmid controlled by the human IL-6 promoter (IL-6-NIS-MSCs) and systemically applied in mice carrying orthotopic GBM. Enhanced radiotracer uptake by 18F-Tetrafluoroborate-PET/magnetic resonance imaging (MRI) was detected in tumors after IL-6-NIS-MSC application as compared with mice that received wild-type MSCs. Ex vivo analysis of tumors and non-target organs showed tumor-specific NIS protein expression. Subsequent 131I therapy after IL-6-NIS-MSC application resulted in significantly delayed tumor growth assessed by MRI and improved median survival up to 60% of GBM-bearing mice as compared with controls. In conclusion, the application of MSC-mediated NIS gene therapy focusing on IL-6 biology-induced NIS transgene expression represents a promising approach for GBM treatment.
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Affiliation(s)
- Carolin Kitzberger
- Department of Internal Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
| | - Khuram Shehzad
- Department of Internal Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
| | - Volker Morath
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rebekka Spellerberg
- Department of Internal Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
| | - Julius Ranke
- Department of Internal Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
| | - Katja Steiger
- Institute of Pathology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Roland E. Kälin
- Neurosurgical Research, Department of Neurosurgery, LMU University Hospital, LMU Munich, Munich, Germany
- Walter Brendel Center of Experimental Medicine, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Gabriele Multhoff
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Radiation Immuno-Oncology Group, Munich, Germany
- Department of Radiation Oncology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Eiber
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Franz Schilling
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rainer Glass
- Neurosurgical Research, Department of Neurosurgery, LMU University Hospital, LMU Munich, Munich, Germany
- Walter Brendel Center of Experimental Medicine, Faculty of Medicine, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang A. Weber
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ernst Wagner
- Pharmaceutical Biotechnology, Department of Pharmacy, Centre for System-Based Drug Research and Centre for Nanoscience, LMU Munich, Munich, Germany
| | - Peter J. Nelson
- Department of Internal Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christine Spitzweg
- Department of Internal Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA
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Schrank TP, Kothari A, Weir WH, Stepp WH, Rehmani H, Liu X, Wang X, Sewell A, Li X, Tasoulas J, Kim S, Yarbrough G, Xie Y, Flamand Y, Marur S, Hayward MC, Wu D, Burtness B, Anderson KS, Baldwin AS, Yarbrough WG, Issaeva N. Noncanonical HPV carcinogenesis drives radiosensitization of head and neck tumors. Proc Natl Acad Sci U S A 2023; 120:e2216532120. [PMID: 37523561 PMCID: PMC10410762 DOI: 10.1073/pnas.2216532120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 07/07/2023] [Indexed: 08/02/2023] Open
Abstract
We analyzed transcriptional data from 104 HPV+ (Human papillomavirus) HNSCC (head and neck squamous cell carcinoma) tumors together with two publicly available sources to identify highly robust transcriptional programs (modules) which could be detected consistently despite heterogeneous sequencing and quantification methodologies. Among 22 modules identified, we found a single module that naturally subclassifies HPV+ HNSCC tumors based on a bimodal pattern of gene expression, clusters all atypical features of HPV+ HNSCC biology into a single subclass, and predicts patient outcome in four independent cohorts. The subclass-defining gene set was strongly correlated with Nuclear factor kappa B (NF-κB) target expression. Tumors with high expression of this NF-κB module were rarely associated with activating PIK3CA alterations or viral integration, and also expressed higher levels of HPHPV E2 and had decreased APOBEC mutagenesis. Alternatively, they harbored inactivating alterations of key regulators of NF-κB, TNF receptor associated factor 3 (TRAF3), and cylindromatosis (CYLD), as well as retinoblastoma protein (RB1). HPV+ HNSCC cells in culture with experimental depletion of TRAF3 or CYLD displayed increased expression of the subclass-defining genes, as well as robust radio-sensitization, thus recapitulating both the tumor transcriptional state and improved treatment response observed in patient data. Across all gene sets investigated, methylation to expression correlations were the strongest for the subclass-defining, NF-κB-related genes. Increased tumor-infiltrating CD4+ T cells and increased Estrogen receptors alpha (ERα) expression were identified in NF-κB active tumors. Based on the relatively high rates of cure in HPV+ HNSCC, deintensification of therapy to reduce treatment-related morbidity is being studied at many institutions. Tumor subclassification based on oncogenic subtypes may help guide the selection of therapeutic intensity or modality for patients with HPV+ HNSCC.
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Affiliation(s)
- Travis P. Schrank
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Aditi Kothari
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - William H. Weir
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Wesley H. Stepp
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Hina Rehmani
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Xinyi Liu
- Department of Pharmacology and Regenerative Medicine, University of Illinois at Chicago, Chicago, IL60612
- University of Illinois Cancer Center, Chicago, IL60612
| | - Xiaowei Wang
- Department of Pharmacology and Regenerative Medicine, University of Illinois at Chicago, Chicago, IL60612
- University of Illinois Cancer Center, Chicago, IL60612
| | - Andrew Sewell
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Xue Li
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Jason Tasoulas
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Sulgi Kim
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Gray Yarbrough
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Yue Xie
- Dana Farber Cancer Institute Eastern Cooperative Oncology Group and the American College of Radiology Imaging Network Biostatistics Center, Boston, MA02109
| | - Yael Flamand
- Dana Farber Cancer Institute Eastern Cooperative Oncology Group and the American College of Radiology Imaging Network Biostatistics Center, Boston, MA02109
| | - Shanthi Marur
- Johns Hopkins Univ/Sidney Kimmel Cancer Center, Baltimore, MD21231
| | - Michele C. Hayward
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Di Wu
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill, NC27599
| | - Barbara Burtness
- Department of Internal Medicine and Yale Cancer Center, New Haven, CT06510
| | - Karen S. Anderson
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT06520
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06520
| | - Albert S. Baldwin
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Pathology and Lab Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Wendell G. Yarbrough
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Pathology and Lab Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Natalia Issaeva
- Department of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Lineberger Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Pathology and Lab Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
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Chi H, Gao X, Xia Z, Yu W, Yin X, Pan Y, Peng G, Mao X, Teichmann AT, Zhang J, Tran LJ, Jiang T, Liu Y, Yang G, Wang Q. FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC. Front Mol Biosci 2023; 10:1200335. [PMID: 37275958 PMCID: PMC10235772 DOI: 10.3389/fmolb.2023.1200335] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/09/2023] [Indexed: 06/07/2023] Open
Abstract
Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.
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Affiliation(s)
- Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Xinrui Gao
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Zhijia Xia
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Wanying Yu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Xisheng Yin
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Yifan Pan
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Gaoge Peng
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Xinrui Mao
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Alexander Tobias Teichmann
- Sichuan Provincial Center for Gynecology and Breast Diseases (Gynecology), Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jing Zhang
- Division of Basic Biomedical Sciences, The University of South Dakota Sanford School of Medicine, Vermillion, SD, United States
| | - Lisa Jia Tran
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Tianxiao Jiang
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Yunfei Liu
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Qin Wang
- Sichuan Provincial Center for Gynecology and Breast Diseases (Gynecology), Affiliated Hospital of Southwest Medical University, Luzhou, China
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9
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Zhao K, Bai X, Wang X, Cao Y, Zhang L, Li W, Wang S. Insight on the hub gene associated signatures and potential therapeutic agents in epilepsy and glioma. Brain Res Bull 2023; 199:110666. [PMID: 37192718 DOI: 10.1016/j.brainresbull.2023.110666] [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: 08/25/2022] [Revised: 04/04/2023] [Accepted: 05/13/2023] [Indexed: 05/18/2023]
Abstract
OBJECTIVE The relationship between epilepsy and glioma has long been widely recognized, but the mechanisms of interaction remain unclear. This study aimed to investigate the shared genetic signature and treatment strategies between epilepsy and glioma. METHODS We subjected hippocampal tissue samples from patients with epilepsy and glioma to transcriptomic analysis to identify differential genes and associated pathways, respectively. Weight gene co-expression network (WGCNA) analysis was performed to identify conserved modules in epilepsy and glioma and to obtain differentially expressed conserved genes. Prognostic and diagnostic models were built using lasso regression. We also focused on building transcription factor-gene interaction networks and assessing the proportion of immune invading cells in epilepsy patients. Finally, drug compounds were inferred using a drug signature database (DSigDB) based on core targets. RESULTS We discovered 88 differently conserved genes, most of which are involved in synaptic signaling and calcium ion pathways. We used lasso regression model to reduce 88 characteristic genes, and finally screened out 14 genes (EIF4A2, CEP170B, SNPH, EPHA4, KLK7, GNG3, MYOP, ANKRD29, RASD2, PRRT3, EFR3A, SGIP1, RAB6B, CNNM1) as the features of glioma prognosis model whose ROC curve is 0.9. Then, we developed a diagnosis model for epilepsy patients using 8 genes (PRRT3, RASD2, MYPOP, CNNM1, ANKRD29, GNG3, SGIP1, KLK7) with area under ROC curve (AUC) values near 1. According to the ssGSEA method, we observed an increase in activated B cells, eosinophils, follicular helper T cells and type 2T helper cells, and a decrease in monocytes in patients with epilepsy. Notably, the great majority of these immune cells showed a negative correlation with hub genes. To reveal the transcriptional-level regulation mechanism, we also built a TF-gene network. In addition, we discovered that patients with glioma-related epilepsy may benefit more from gabapentin and pregabalin. CONCLUSION This study reveals the modular conserved phenotypes of epilepsy and glioma and constructs effective diagnostic and prognostic markers. It provides new biological targets and ideas for the early diagnosis and effective treatment of epilepsy.
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Affiliation(s)
- Kai Zhao
- Institute of Brain Trauma and Neurology, Pingjin Hospital, Characteristic Medical Center of the Chinese People's Armed Police Force, Tianjin, 300000, China
| | - Xuexue Bai
- Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China
| | - Xiao Wang
- Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China
| | - Yiyao Cao
- Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China
| | - Liu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China
| | - Wei Li
- Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China
| | - Shiyong Wang
- Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China.
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10
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Hwang S, Lim J, Kang H, Jeong JY, Joung JG, Heo J, Jung D, Cho K, An HJ. Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy. Cell Biosci 2023; 13:17. [PMID: 36694264 PMCID: PMC9875464 DOI: 10.1186/s13578-023-00961-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/11/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Recurrent glioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor that is resistant to existing treatments. Recently, we reported that activated autologous natural killer (NK) cell therapeutics induced a marked increase in survival of some patients with recurrent GBM. METHODS To identify biomarkers that predict responsiveness to NK cell therapeutics, we examined immune profiles in tumor tissues using NanoString nCounter analysis and compared the profiles between 5 responders and 7 non-responders. Through a three-step data analysis, we identified three candidate biomarkers (TNFRSF18, TNFSF4, and IL12RB2) and performed validation with qRT-PCR. We also performed immunohistochemistry and a NK cell migration assay to assess the function of these genes. RESULTS Responders had higher expression of many immune-signaling genes compared with non-responders, which suggests an immune-active tumor microenvironment in responders. The random forest model that identified TNFRSF18, TNFSF4, and IL12RB2 showed a 100% accuracy (95% CI 73.5-100%) for predicting the response to NK cell therapeutics. The expression levels of these three genes by qRT-PCR were highly correlated with the NanoString levels, with high Pearson's correlation coefficients (0.419 (TNFRSF18), 0.700 (TNFSF4), and 0.502 (IL12RB2)); their prediction performance also showed 100% accuracy (95% CI 73.54-100%) by logistic regression modeling. We also demonstrated that these genes were related to cytotoxic T cell infiltration and NK cell migration in the tumor microenvironment. CONCLUSION We identified TNFRSF18, TNFSF4, and IL12RB2 as biomarkers that predict response to NK cell therapeutics in recurrent GBM, which might provide a new treatment strategy for this highly aggressive tumor.
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Affiliation(s)
- Sohyun Hwang
- grid.410886.30000 0004 0647 3511Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea ,grid.452398.10000 0004 0570 1076CHA Future Medicine Research Institute, CHA Bundang Medical Center, Seongnam, Korea
| | - Jaejoon Lim
- grid.410886.30000 0004 0647 3511Department of Neurosurgery, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea
| | - Haeyoun Kang
- grid.410886.30000 0004 0647 3511Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea
| | - Ju-Yeon Jeong
- grid.452398.10000 0004 0570 1076CHA Future Medicine Research Institute, CHA Bundang Medical Center, Seongnam, Korea
| | - Je-Gun Joung
- grid.452398.10000 0004 0570 1076CHA Future Medicine Research Institute, CHA Bundang Medical Center, Seongnam, Korea ,grid.410886.30000 0004 0647 3511Department of Biomedical Science, CHA University, Seongnam, Korea
| | - Jinhyung Heo
- grid.410886.30000 0004 0647 3511Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea
| | - Daun Jung
- grid.410886.30000 0004 0647 3511Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea
| | - Kyunggi Cho
- grid.410886.30000 0004 0647 3511Department of Neurosurgery, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea
| | - Hee Jung An
- grid.410886.30000 0004 0647 3511Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, 13496 Korea ,grid.452398.10000 0004 0570 1076CHA Future Medicine Research Institute, CHA Bundang Medical Center, Seongnam, Korea
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11
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Zhou Y, Dai X, Lyu J, Li Y, Bao X, Deng F, Liu K, Cui L, Cheng L. Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs. Medicine (Baltimore) 2022; 101:e31075. [PMID: 36281116 PMCID: PMC9592387 DOI: 10.1097/md.0000000000031075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration. METHODS In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out. RESULTS We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores. CONCLUSION Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.
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Affiliation(s)
- Yang Zhou
- Department of Otolaryngology Head and Neck Surgery, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Xuezhong Dai
- Department of Otolaryngology Head and Neck Surgery, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Jianhong Lyu
- Department of Anesthesiology, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Yingyue Li
- Department of Otolaryngology Head and Neck Surgery, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Xueyu Bao
- Department of Otolaryngology Head and Neck Surgery, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Fang Deng
- Department of Otolaryngology Head and Neck Surgery, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Kun Liu
- Department of Otorhinolaryngology, Puren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei, China
| | - Liming Cui
- Department of Otolaryngology Head and Neck Surgery, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Li Cheng
- Department of Endocrinology, The Third People’s Hospital of Yunnan Province, Kunming, Yunnan, China
- * Correspondence: Li Cheng, The Third People’s Hospital of Yunnan Province, 292 Beijing Road, Guandu District, Kunming City, Yunnan Province 650011, China (e-mail: )
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12
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Du Y, Zeng X, Yu W, Xie W. A transmembrane protein family gene signature for overall survival prediction in osteosarcoma. Front Genet 2022; 13:937300. [PMID: 35991561 PMCID: PMC9388755 DOI: 10.3389/fgene.2022.937300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
The transmembrane (TMEM) protein family is constituted by a large number of proteins that span the lipid bilayer. Dysregulation of TMEM protein genes widely occurs and is associated with clinical outcomes of patients with multiple tumors. Nonetheless, the significance of TMEM genes in the prognosis prediction of patients with osteosarcoma remains largely unclear. Here, we comprehensively analyzed TMEM protein family genes in osteosarcoma using public resources and bioinformatics methods. Prognosis-related TMEM protein family genes were identified by the univariate Cox regression analysis and were utilized to construct a signature based on six TMEM protein family genes (TMEM120B, TMEM147, TMEM9B, TMEM8A, TMEM59, and TMEM39B) in osteosarcoma. The prognostic signature stratified patients into high- and low-risk groups, and validation in the internal and external cohorts confirmed the risk stratification ability of the signature. Functional enrichment analyses of differentially expressed genes between high- and low-risk groups connected immunity with the prognostic signature. Moreover, we found that M2 and M0 macrophages were the most abundant infiltrated immune cell types in the immune microenvironment, and samples of the high-risk group showed a decreased proportion of M2 macrophages. Single-sample gene set enrichment analysis revealed that the scores of neutrophils and Treg were markedly lower in the high-risk group than these in the low-risk group in The Cancer Genome Atlas and GSE16091 cohorts. As for the related immune functions, APC co-inhibition and cytolytic activity exhibited fewer active levels in the high-risk group than that in the low-risk group in both cohorts. Of the six TMEM genes, the expression of TMEM9B was lower in the high-risk group than in the low-risk group and was positively associated with the overall survival of osteosarcoma patients. In conclusion, our TMEM protein family gene-based signature is a novel and clinically useful prognostic biomarker for osteosarcoma patients, and TMEM9B might be a potential therapeutic target in osteosarcoma.
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13
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Munquad S, Si T, Mallik S, Li A, Das AB. Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine. Brief Funct Genomics 2022; 21:408-421. [PMID: 35923100 DOI: 10.1093/bfgp/elac025] [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: 03/31/2022] [Revised: 06/23/2022] [Accepted: 07/17/2022] [Indexed: 11/13/2022] Open
Abstract
Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.
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Affiliation(s)
- Sana Munquad
- Department of Biotechnology, National Institute of Technology Warangal, Warangal 506004, Telangana, India
| | - Tapas Si
- Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura 722146, West Bengal, India
| | - Saurav Mallik
- Department of Environmental Epigenetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Aimin Li
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Asim Bikas Das
- Department of Biotechnology, National Institute of Technology Warangal, Warangal 506004, Telangana, India
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14
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Zhang Y, Yang X, Zhou L, Gao X, Wu X, Chen X, Hou J, Wang L. Immune-related lincRNA pairs predict prognosis and therapeutic response in hepatocellular carcinoma. Sci Rep 2022; 12:4259. [PMID: 35277569 PMCID: PMC8917134 DOI: 10.1038/s41598-022-08225-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/03/2022] [Indexed: 12/03/2022] Open
Abstract
Growing evidence has demonstrated the functional relevance of long intergenic noncoding RNAs (lincRNAs) to tumorigenesis and immune response. However, immune-related lincRNAs and their value in predicting the clinical outcomes of patients with liver cancer remain largely unexplored. Herein, we utilized the strategy of iterative gene pairing to construct a tumor-specific immune-related lincRNA pairs signature (IRLPS), which did not require specific expression levels, as an indicator of patient outcomes. The 18-IRLPS we developed was associated with overall survival, tumor progression, and recurrence in liver cancer patients. Multivariate analysis revealed that the risk model was an independent predictive factor. A high IRLPS risk was correlated suppressive immune microenvironment, and IRLPS-high patients might benefit more from CD276 blockade or TMIGD2 agonist. Patients in the high-risk group were associated with elevated tumor mutation, increased sensitivity to dopamine receptor antagonists, cisplatin, doxorubicin, and mitomycin but more resistance to vinblastine. Mechanistically, IRLPS high scores might lead to poor prognosis by promoting cell proliferation and metabolic reprogramming. The prognostic significance of the 18-IRLPS was confirmed in independent cancer datasets. These findings highlighted the robust predictive performances of the 18-IRLPS for prognosis and personalized treatment.
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Affiliation(s)
- Yingna Zhang
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Key Laboratory of Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Department of Anatomy, Shihezi University School of Medicine, Shihezi, Xinjiang, China
| | - Xiaofeng Yang
- Department of Immunology, Shihezi University School of Medicine, Shihezi, Xinjiang, China
| | - Lisha Zhou
- Department of Immunology, Shihezi University School of Medicine, Shihezi, Xinjiang, China
| | - Xiangting Gao
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Key Laboratory of Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Department of Pathology, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China
| | - Xiangwei Wu
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Key Laboratory of Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, China
| | - Xueling Chen
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Key Laboratory of Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, China.,Department of Immunology, Shihezi University School of Medicine, Shihezi, Xinjiang, China
| | - Jun Hou
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China. .,Key Laboratory of Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, China. .,Department of Immunology, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
| | - Lianghai Wang
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China. .,Key Laboratory of Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, China. .,Department of Pathology, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
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