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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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Wang W, Zhang J, Wang Y, Xu Y, Zhang S. Identifies microtubule-binding protein CSPP1 as a novel cancer biomarker associated with ferroptosis and tumor microenvironment. Comput Struct Biotechnol J 2022; 20:3322-3335. [PMID: 35832625 PMCID: PMC9253833 DOI: 10.1016/j.csbj.2022.06.046] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/19/2022] [Accepted: 06/21/2022] [Indexed: 12/02/2022] Open
Abstract
Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.
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Key Words
- ACC, adrenocortical carcinoma
- BP, biological pathways
- BRCA, breast invasive carcinoma
- Biomarker
- C-index, concordance index
- CAF, cancer-associated fibroblasts
- CC, cellular component
- CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, cholangiocarcinoma
- CNA, copy number alteration
- COAD, colon adenocarcinoma
- CPTAC, Clinical Proteomic Tumor Analysis Consortium
- CSPP1
- CSPP1, centrosome and spindle pole-associated protein
- CTL, cytotoxic T lymphocyte
- DEGs, differentially expressed genes
- DLBC, diffuse large B-cell lymphoma
- DSS, disease-specific survival
- EMT, epithelial-mesenchymal transition
- ENCORI, Encyclopedia of RNA Interactomes
- ESCA, esophageal carcinoma
- FAG, ferroptosis-associated gene
- FDG, ferroptosis-driver gene
- FSG, ferroptosis-suppressor gene
- Ferroptosis
- GBM, glioblastoma multiforme
- GO, Gene Ontology
- GSEA, Gene Set Enrichment Analysis
- GSVA, gene set variation analysis
- GTEx, Genotype-Tissue Expression
- HNSC, head and neck squamous cell carcinoma
- ICB, immune checkpoint blockade
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- KICH, kidney chromophobe
- KIRC, renal clear cell carcinoma
- KM, Kaplan-Meier
- LAML, acute myeloid leukemia
- LGG, low-grade gliomas
- LIHC, liver hepatocellular carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- MF, molecular functions
- MHC, major histocompatibility complex
- MSI, microsatellite instability
- OS, overall survival
- OV, ovarian serous cystadenocarcinoma
- PAAD, pancreatic adenocarcinoma
- PFI, progression-free interval
- PFS, progression-free survival
- PRAD, prostate cancer
- Pan-cancer
- READ, rectum adenocarcinoma
- ROC, receiver operating characteristics
- SKCM, skin cutaneous melanoma
- TCGA, The Cancer Genome Atlas
- TGCT, testicular germ cell tumors, STAD, stomach adenocarcinoma
- THCA, thyroid cancer
- THYM, thymoma
- TIDE, Tumor Immune Dysfunction and Exclusion
- TIMER, Tumor Immune Estimation Resource
- TISIDB, Tumor-Immune System Interactions DataBase
- TMB, tumor mutation burden
- TME, tumor microenvironment
- Tumor microenvironment
- UCEC, endometrial cancer uterine corpus endometrial carcinoma
- UCS, uterine carcinosarcoma
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Affiliation(s)
- Wenwen Wang
- Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou, China
| | - Jingjing Zhang
- Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou, China
| | - Yuqing Wang
- Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou, China
| | - Yasi Xu
- Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou, China
| | - Shirong Zhang
- Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Cancer Center, Zhejiang University, Hangzhou, China
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Wan R, Bai L, Cai C, Ya W, Jiang J, Hu C, Chen Q, Zhao B, Li Y. Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma. Comput Struct Biotechnol J 2021; 19:6386-6399. [PMID: 34938414 PMCID: PMC8649667 DOI: 10.1016/j.csbj.2021.11.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/15/2021] [Accepted: 11/20/2021] [Indexed: 11/17/2022] Open
Abstract
Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.
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Key Words
- AUC, area under the curve
- CCLE, Cancer Cell Line Encyclopedia
- FPKM, fragments per kilobase of transcript per million
- GEO, Gene Expression Omnibus
- GO, gene ontology
- GSVA, gene set variation analysis
- HIC, immunohistochemistry
- HR, hazard ratio
- ICIs, immune checkpoints inhibitors
- IF, immunofluorescence
- Immune checkpoints
- LUAD, lung adenocarcinoma
- Lung adenocarcinoma
- NK cell, natural killer cell
- PBMC, Peripheral Blood Mononuclear Cell
- ROC, receiver operating characteristic
- RSF, random survival forest
- RT-qPCR, Real-time Quantitative Polymerase Chain Reaction
- Small nucleolar RNAs
- TCGA, The Cancer Genome Atlas
- TIISR signature
- TIISR, tumor-infiltrating immune-related snoRNA
- TIME, tumor immune microenvironment
- TPM, transcripts per kilobase million
- TSI, tissue specificity index
- Tumor cell immune infiltration
- ncRNA, noncoding RNA
- snoRNAs, small nucleolar RNAs
- ssGSEA, single-sample gene set enrichment analysis
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Affiliation(s)
- Rongjun Wan
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Lu Bai
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Changjing Cai
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Wang Ya
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Juan Jiang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Chengping Hu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Qiong Chen
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Bingrong Zhao
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
| | - Yuanyuan Li
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, Hunan, China, 410008
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. 410008
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, Hunan, China. 410008
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China. 410008
- National Clinical Research Center for Geriatric Disorders,Xiangya Hospital, Changsha, Hunan, P.R. China, 410008
- Corresponding author.
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Zhang H, Luo YB, Wu W, Zhang L, Wang Z, Dai Z, Feng S, Cao H, Cheng Q, Liu Z. The molecular feature of macrophages in tumor immune microenvironment of glioma patients. Comput Struct Biotechnol J 2021; 19:4603-18. [PMID: 34471502 DOI: 10.1016/j.csbj.2021.08.019] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022] Open
Abstract
Background Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth. Methods Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore. Results Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate. Conclusion Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.
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Key Words
- ACC, Adrenocortical carcinoma
- BBB, brain blood barrier
- BLCA, Bladder Urothelial Carcinoma
- BRCA, Breast invasive carcinoma
- CDF, cumulative distribution function
- CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma
- CGGA, Chinese Glioma Genome Atlas
- CHOL, Cholangiocarcinoma
- CNA, copy number alternations
- CNV, copy number variation
- COAD, Colon adenocarcinoma
- CSF-1, colony-stimulating factor-1
- DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma
- DMP, differentially methylated position
- ESCA, Esophageal carcinoma
- GBM, glioblastoma
- GEO, Gene Expression Omnibus
- GO, gene ontology
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- Glioma microenvironment
- HNSC, Head and Neck squamous cell carcinoma
- IGR, intergenic region
- IHC, immunohistochemistry
- IL, interleukin
- Immunotherapy
- KEGG, Kyoto Encyclopaedia of Genes and Genomes
- KICH, Kidney Chromophobe
- KIRC, Kidney renal clear cell carcinoma
- KIRP, Kidney renal papillary cell carcinoma
- LGG, low grade glioma
- LIHC, Liver hepatocellular carcinoma
- LUAD, Lung adenocarcinoma
- LUSC, Lung squamous cell carcinoma
- MMP-2, matrix metalloproteinase-2
- MT1, MMP membrane type 1 matrix metalloprotease
- Machine learning
- Macrophage
- OV, Ovarian serous cystadenocarcinoma
- PAAD, Pancreatic adenocarcinoma
- PAM, partition around medoids
- PCA, principal component analysis
- PCPG, Pheochromocytoma and Paraganglioma
- PRAD, Prostate adenocarcinoma
- Prognostic model
- READ, Rectum adenocarcinoma
- SARC, Sarcoma
- SKCM, Skin Cutaneous Melanoma
- SNP, single-nucleotide polymorphism
- SNV, single-nucleotide variant
- STAD, Stomach adenocarcinoma
- SVM, Support Vector Machines
- TAM, tumor associated macrophage
- TCGA, The Cancer Genome Atlas
- TGF-β, tumor growth factor-β
- THCA, Thyroid carcinoma
- THYM, Thymoma
- TIMP-2, tissue inhibitor of metalloproteinase-2
- TLR2, toll-like receptor 2
- TME, tumor microenvironment
- TNFα, tumor necrosis factor α
- TSS, transcription start site
- UCEC, Uterine Corpus Endometrial Carcinoma
- UCS, Uterine Carcinosarcoma
- WGCNA, weighted gene co-expression network analysis
- pamr, prediction analysis for microarrays
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