Detecting pan-cancer conserved microRNA modules from microRNA expression profiles across multiple cancers

We develop a novel computational workflow to analyze the cooperation of miRNAs across cancers by identifying pan-cancer conserved miRNA modules. Such modules are assumed to contribute to cancer hallmarks.

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Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

Cancer Informatics ◽

10.1177/11769351211002494 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110024

Author(s):

Jason D Wells ◽

Jacqueline R Griffin ◽

Todd W Miller

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Human Tumors ◽

Molecular Characteristics ◽

Survival Times ◽

Chemotherapy Drugs ◽

Testing Dataset ◽

Cancer Models ◽

Cancer Types ◽

Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

BioMed Research International ◽

10.1155/2020/7136049 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Yuntao Shi ◽

Yingying Zhuang ◽

Jialing Zhang ◽

Mengxue Chen ◽

Shangnong Wu

Keyword(s):

Target Genes ◽

Cox Regression ◽

Expression Profiles ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Risk Groups ◽

Normal Mucosa ◽

Microrna Expression ◽

Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

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