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Identification of topological features in renal tumor microenvironment associated with patient survival.

ABSTRACT: Motivation:As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist's visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results:We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers. Availability and implementation:https://github.com/chengjun583/KIRP-topological-features. Contact:1271992826@qq.com or kunhuang@iu.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Cheng J

PROVIDER: S-EPMC7263397 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Identification of topological features in renal tumor microenvironment associated with patient survival.

Cheng Jun J Mo Xiaokui X Wang Xusheng X Parwani Anil A Feng Qianjin Q Huang Kun K

Bioinformatics (Oxford, England) 20180301 6

<h4>Motivation</h4>As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist's visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may ...[more]

PMID: 29136101

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Project description:ObjectiveGastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related deaths globally. The tumor microenvironment plays a significant role in the pathogenesis, prognosis, and response to immunotherapy. However, the immune-related molecular mechanisms underlying GC remain elusive. Bioinformatics analysis of the gene expression of GC and paracancerous healthy tissues from the same patient was performed to identify the key genes and signaling pathways, as well as their correlation to the infiltration of the tumor microenvironment (TME) by various immune cells related to GC development.MethodsWe employed GSE19826, a gene expression profile from the Gene Expression Omnibus (GEO), for our analysis. Functional enrichment analysis of Differentially Expressed Genes (DEGs) was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database.ResultsCytoscape software facilitated the identification of nine hub DEGs, namely, FN1, COL1A1, COL1A2, THBS2, COL3A1, COL5A1, APOE, SPP1, and BGN. Various network analysis algorithms were applied to determine their high connectivity. Among these hub genes, FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN were found to be associated with a poor prognosis for GC patients. Subsequent analysis using the TIMER database revealed the infiltration status of the TME concerning the overexpression of these six genes. Specifically, the abovementioned genes demonstrated direct correlations with cancer-associated fibroblasts, M1 and M2 macrophages, myeloid-derived suppressor cells, and activated dendritic cells.ConclusionOur findings suggest that the identified hub genes, particularly BGN, FN1, COL1A2, THBS2, COL3A1, and COL5A1, play crucial roles in GC prognosis and TME cell infiltration. This comprehensive analysis enhances our understanding of the molecular mechanisms underlying GC development and may contribute to the identification of potential therapeutic targets and prognostic markers for GC patients.

Project description:The last decade has witnessed revolutionary advances taken in immunotherapy for various malignant tumors. However, immune-related molecules and their characteristics in the prediction of clinical outcomes and immunotherapy response in clear cell renal cell carcinoma (ccRCC) remain largely unclear. C-C Motif Chemokine Ligand 4 (CCL4) was extracted from the intersection analysis of common differentially expressed genes (DEGs) of four microarray datasets from the Gene Expression Omnibus database and immune-related gene lists in the ImmPort database using Cytoscape plug-ins and univariate Cox regression analysis. Subsequential analysis revealed that CCL4 was highly expressed in ccRCC patients, and positively correlated with multiple clinicopathological characteristics, such as grade, stage and metastasis, while negatively with overall survival (OS). We performed gene set enrichment analysis (GSEA) and gene set variant analysis (GSVA) with gene sets coexpressed with CCL4, and observed that gene sets positively related to CCL4 were enriched in tumor proliferation and immune-related pathways while metabolic activities in the negatively one. To further explore the correlation between CCL4 and immune-related biological process, the CIBERSORT algorithm, ESTIMATE method, and tumor mutational burden (TMB) score were employed to evaluate the tumor microenvironment (TME) characteristics of each sample and confirmed that high CCL4 expression might give rise to high immune cell infiltration. Moreover, correlation analysis revealed that CCL4 was positively correlated with common immune checkpoint genes, such as programmed cell death protein 1 (PD-1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3). Overall, this study demonstrated that CCL4 might serve as a potential immune-related prognostic biomarker to predict clinical outcomes and immunotherapy response in ccRCC. Moreover, CCL4 might contribute to TME modulation, indicating the mechanism CCL4 involved in tumor proliferation and metastasis, which could provide novel therapeutic perceptions for ccRCC patients.

Project description:BackgroundThe tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC.MethodsPRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan-Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA).ResultsWe found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors.ConclusionsFour tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

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Identification of topological features in renal tumor microenvironment associated with patient survival.

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Identification of topological features in renal tumor microenvironment associated with patient survival.

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