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A miRNA-based classification of renal cell carcinoma subtypes by PCR and in situ hybridization.

ABSTRACT: Renal cell carcinoma (RCC) constitutes an array of morphologically and genetically distinct tumors the most prevalent of which are clear cell, papillary, and chromophobe RCC. Accurate distinction between the typically benign-behaving renal oncocytoma and RCC subtypes is a frequent challenge for pathologists. This is critical for clinical decision making. Subtypes also have different survival outcomes and responses to therapy. We extracted RNA from ninety formalin-fixed paraffin-embedded (FFPE) tissues (27 clear cell, 29 papillary, 19 chromophobe, 4 unclassified RCC and 11 oncocytomas). We quantified the expression of six miRNAs (miR-221, miR-222, miR-126, miR-182, miR-200b and miR-200c) by qRT-PCR, and by in situ hybridization in an independent set of tumors. We developed a two-step classifier. In the first step, it uses expression of either miR-221 or miR-222 to distinguish the clear cell and papillary subtypes from chromophobe RCC and oncocytoma (miR-221 AUC: 0.96, 95% CI: 0.9132-1.014, p < 0.0001 and miR-222 AUC: 0.91, 95% CI: 0.8478-0.9772, p < 0.0001). In the second step, it uses miR-126 to discriminate clear cell from papillary RCC (AUC: 1, p < 0.0001) and miR-200b to discriminate chromophobe RCC from oncocytoma (AUC: 0.95, 95% CI: 0.8933-1.021, p < 0.0001). In situ hybridization showed a nuclear staining pattern. miR-126, miR-222 and miR-200b were significantly differentially expressed between the subtypes by in situ hybridization. miRNA expression could distinguish RCC subtypes and oncocytoma. miRNA expression assessed by either PCR or in situ hybridization can be a clinically useful diagnostic tool to complement morphologic renal tumor classification, improving diagnosis and patient management.

SUBMITTER: Di Meo A

PROVIDER: S-EPMC5788624 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A miRNA-based classification of renal cell carcinoma subtypes by PCR and <i>in situ</i> hybridization.

Di Meo Ashley A Saleeb Rola R Wala Samantha J SJ Khella Heba W HW Ding Qiang Q Zhai Haiyan H Krishan Kalra K Krizova Adriana A Gabril Manal M Evans Andrew A Brimo Fadi F Pasic Maria D MD Finelli Antonio A Diamandis Eleftherios P EP Yousef George M GM

Oncotarget 20171208 2

Renal cell carcinoma (RCC) constitutes an array of morphologically and genetically distinct tumors the most prevalent of which are clear cell, papillary, and chromophobe RCC. Accurate distinction between the typically benign-behaving renal oncocytoma and RCC subtypes is a frequent challenge for pathologists. This is critical for clinical decision making. Subtypes also have different survival outcomes and responses to therapy. We extracted RNA from ninety formalin-fixed paraffin-embedded (FFPE) t ...[more]

PMID: 29416756

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.ResultsA four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e-08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.ConclusionsA novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Project description:Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of malignancy in adults. However, the clinical significance of tumor suppressor genes (TSG) is largely elusive. Herein, the expression profile TSGs and its clinical response in ccRCC were investigated. A total of 603 ccRCC samples from two cohorts (TCGA and ICGC) were retrieved in this study. Three molecular subtypes (C1, C2, and C3) were identified based on the TSGs expression profile in the TCGA dataset. Through Weighted Gene Correlation Network Analysis (WGCNA), six modules associated with three subtypes were identified. Pathway enrichment for the modules revealed that crucial pathways including p53 signaling and immune-related pathways were significantly enriched. We further focused on the relationship between immune infiltration level and subtypes, and found that subtype C1 was associated with higher immune infiltration level, subtype C2 was corresponding with medium immune infiltration level, whereas subtype C3 was correlated with lower immune infiltration level. Interestingly, C2 have a better survival outcome, while C1 and C3 showed a poor prognosis. Considering their survival difference, we then performed a differentially expression analysis between C2 and C1&3, and a total of 99 differentially expressed tumor suppressor genes (DETSGs) were identified. According to these DETSGs, 59 potential compounds with 28 mechanisms of action (MOA) were predicted using the Connectivity Map (CMap) database. Among these compounds, leflunomide, naftopidil, and ribavirin were the most prospective compounds for the treatment of ccRCC. In addition, we found that subtype C2 is more sensitive to sorafenib and sunitinib drugs, and C2 have more likelihood to be responded to immunotherapy. In summary, the three subtypes hinged on the tumor suppressor gene expression for ccRCC might contribute to understanding the underlying molecular mechanisms of ccRCC. Also, its potential compounds might offer guidelines for developing a novel treatment strategy of ccRCC.

Project description:BackgroundRecent research of clear cell renal cell carcinoma (ccRCC) is focused on the tumor immune microenvironment (TIME). Chromatin accessibility is critical for regulation of gene expression. However, its role in different immunological subtypes of ccRCC based on immune cell infiltration has not been systematically studied.MethodsFive hundred thirty patient data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) were adopted to estimate immune cell infiltration. Twenty-four types of immune cells were evaluated with single-sample Gene Set Enrichment Analysis (ssGSEA). Patients were divided into two clusters based on immune cell infiltration. Systematic chromatin accessibility analysis was conducted based on the two clusters.ResultsWe compared the relative expression of the immune gene signatures among 530 patients of TCGA-KIRC using ssGSEA. Overall survival (OS) analysis revealed 10 types of immune cells were significantly associated with prognosis. Patients were divided into two clusters based on 24 types of immune cell infiltration. Immune cell signals as well as PD-1/PD-L1 signal were higher in cluster 1. Among the two clusters, 2,400 differential peaks were found in TCGA-KIRC Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) data. The distribution of differential peaks and prognosis-related immune cells in 23 chromosomes are essentially the same. There is no peak distribution downstream. The proportion of peaks upstream of the 5' transcription start site decreases, and both sides of binding regions of the TSS 0.1-1 kb becomes smaller. Enrichment analysis of GO and KEGG of these differential peaks showed that they are remarkably related to the immune regulation in tumor microenvironment. Known motifs and de novo motifs were found by linking motif annotations to different peaks. Survival analysis of related motif transcription factors were prognostic. The GSEA enrichment analysis showed that high SP1 expression positively correlates with TGF-beta signaling and inflammatory response, while negatively correlates with TNF-alpha signaling via NFKB. High KLF12 expression negatively correlates with interferon gamma response, IL2-STAT5 signaling, TNF-alpha signaling via NFKB, IL6-JAK-STAT3 signaling.ConclusionThe abnormality of chromatin accessibility may play an important regulatory role in ccRCC immunity.

Dataset Information

A miRNA-based classification of renal cell carcinoma subtypes by PCR and in situ hybridization.

Publications

A miRNA-based classification of renal cell carcinoma subtypes by PCR and <i>in situ</i> hybridization.

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