NDLI: Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning

Content Provider	Springer Nature : BioMed Central
Author	Ghobadi, Mohadeseh Zarei Emamzadeh, Rahman Afsaneh, Elaheh
Abstract	Background Adult T-cell Leukemia/Lymphoma (ATLL) is a cancer disease that is developed due to the infection by human T-cell leukemia virus type 1. It can be classified into four main subtypes including, acute, chronic, smoldering, and lymphoma. Despite the clinical manifestations, there are no reliable diagnostic biomarkers for the classification of these subtypes. Methods Herein, we employed a machine learning approach, namely, Support Vector Machine-Recursive Feature Elimination with Cross-Validation (SVM-RFECV) to classify the different ATLL subtypes from Asymptomatic Carriers (ACs). The expression values of multiple mRNAs and miRNAs were used as the features. Afterward, the reliable miRNA-mRNA interactions for each subtype were identified through exploring the experimentally validated-target genes of miRNAs. Results The results revealed that miR-21 and its interactions with DAAM1 and E2F2 in acute, SMAD7 in chronic, MYEF2 and PARP1 in smoldering subtypes could significantly classify the diverse subtypes. Conclusions Considering the high accuracy of the constructed model, the identified mRNAs and miRNA are proposed as the potential therapeutic targets and the prognostic biomarkers for various ATLL subtypes.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-022-09540-1.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-022-09540-1
Journal	BMC Cancer
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2022-04-21
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health HTLV-1 ATLL Asymptomatic carriers Machine learning ATLL subtypes Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer

High-throughput miRNA profiling of human melanoma blood samples

OMCD: OncomiR Cancer Database

Machine learning-based colorectal cancer prediction using global dietary data

Cervical cancer survival prediction by machine learning algorithms: a systematic review

Association of differential miRNA expression with hepatic vs. peritoneal metastatic spread in colorectal cancer

Patterns of breast cancer locoregional relapse, metastasis, and subtypes in Ghana

An integrative transcriptome analysis reveals potential predictive, prognostic biomarkers and therapeutic targets in colorectal cancer

Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning

Similar Documents

Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer

High-throughput miRNA profiling of human melanoma blood samples

OMCD: OncomiR Cancer Database

Machine learning-based colorectal cancer prediction using global dietary data

Cervical cancer survival prediction by machine learning algorithms: a systematic review

Association of differential miRNA expression with hepatic vs. peritoneal metastatic spread in colorectal cancer

Patterns of breast cancer locoregional relapse, metastasis, and subtypes in Ghana

An integrative transcriptome analysis reveals potential predictive, prognostic biomarkers and therapeutic targets in colorectal cancer

Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning