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Biomolecules
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Identification of Important Biomolecules of Chronic Diseases from Multi-Omics Data...
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Identification of Important Biomolecules of Chronic Diseases from Multi-Omics Data by Using Machine Learning and Deep Learning

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (21 May 2023) | Viewed by 5619

Special Issue Editors

Dr. Yafeng Li

E-Mail Website
Guest Editor
Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
Interests: kidney disease; IgA nephropathy; mucosal immune; autoimmune disease; genomics; transcriptomics; metabonomics; chronic renal failure; cardiovascular disease; clinical nephrology; acute kidney injury
Dr. Jianbo Qing

E-Mail Website
Guest Editor
Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
Interests: IgA nephropathy; mucosal immune; immunology; chronic kidney diseases; biomarkers; transcriptomics; single cell sequencing; machine learning; deep learning

Special Issue Information

Dear Colleagues,

The early detection and progression of many diseases remains a challenge, and the identification of important biomolecules is essential for the early diagnosis and effective treatment of diseases. A spurt of progress in biological omics, including genomics, transcriptomics, metabonomics, and proteomics, has caused a dramatic change in biological research, which has provided a broader perspective and new technologies for research of the pathogenesis of diseases and personalized treatment.

However, it is difficult to systematically and comprehensively discuss the regulatory mechanisms of complex physiological processes with single omics data. Yet, multi-omics analysis allows for the establishment of data relationships between different-level molecules through normalization, comparative analysis and correlation analysis of data from different biomolecular levels, from genome to transcriptome, from proteome to metabolome and lipome. As such, the exploration of the potential regulatory network mechanisms in organisms would be made possible with more evidence for the pathogenesis of diseases. Additionally, data with different omics, such as transcriptomics, proteomics, and metabolomics, could be integrated through predictive algorithms of machine learning (ML) and deep learning (DL) to reveal the complex mechanisms of diseases.

This Special Issue will focus on articles involving ML and DL combined with multi-omics techniques to explore the disease-related biomarkers that could provide informative value for disease prevention, occurrence and development. Original research and review articles are welcome.

Dr. Yafeng Li
Dr. Jianbo Qing
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-omics data
  • machine learning
  • deep learning
  • biomarkers and biomolecules
  • precise diagnosis and treatment

Published Papers (2 papers)

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Research

20 pages, 25838 KiB  
Article
Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer
by Zenghong Wu, Weijun Wang, Kun Zhang, Mengke Fan and Rong Lin
Biomolecules 2023, 13(5), 736; https://doi.org/10.3390/biom13050736 - 25 Apr 2023
Cited by 5 | Viewed by 2468
Abstract
Background: Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC. Methods: A complete review of genomic data was performed [...] Read more.
Background: Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC. Methods: A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC. Results: Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan–Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox–LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature’s area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered. Conclusion: The study identified some hub genes that could be useful in predicting prognosis and management in GC. Full article
(This article belongs to the Special Issue Identification of Important Biomolecules of Chronic Diseases from Multi-Omics Data by Using Machine Learning and Deep Learning)
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19 pages, 6685 KiB  
Article
Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning
by Zhixun Bai, Jing Lu, Anjian Chen, Xiang Zheng, Mingsong Wu, Zhouke Tan and Jian Xie
Biomolecules 2022, 12(12), 1890; https://doi.org/10.3390/biom12121890 - 16 Dec 2022
Cited by 7 | Viewed by 2401
Abstract
(1) Objective: We aimed to mine cuproptosis-related LncRNAs with prognostic value and construct a corresponding prognostic model using machine learning. External validation of the model was performed in the ICGC database and in multiple renal cancer cell lines via qPCR. (2) Methods: TCGA [...] Read more.
(1) Objective: We aimed to mine cuproptosis-related LncRNAs with prognostic value and construct a corresponding prognostic model using machine learning. External validation of the model was performed in the ICGC database and in multiple renal cancer cell lines via qPCR. (2) Methods: TCGA and ICGC cohorts related to renal clear cell carcinoma were included. GO and KEGG analyses were conducted to determine the biological significance of differentially expressed cuproptosis-related LncRNAs (CRLRs). Machine learning (LASSO), Kaplan–Meier, and Cox analyses were conducted to determine the prognostic genes. The tumor microenvironment and tumor mutation load were further studied. TIDE and IC50 were used to evaluate the response to immunotherapy, a risk model of LncRNAs related to the cuproptosis genes was established, and the ability of this model was verified in an external independent ICGC cohort. LncRNAs were identified in normal HK-2 cells and verified in four renal cell lines via qPCR. (3) Results: We obtained 280 CRLRs and identified 66 LncRNAs included in the TCGA-KIRC cohort. Then, three hub LncRNAs (AC026401.3, FOXD2−AS1, and LASTR), which were over-expressed in the four ccRCC cell lines compared with the human renal cortex proximal tubule epithelial cell line HK-2, were identified. In the ICGC database, the expression of FOXD2-AS1 and LASTR was consistent with the qPCR and TCGA-KIRC. The results also indicated that patients with low-risk ccRCC—stratified by tumor-node metastasis stage, sex, and tumor grade—had significantly better overall survival than those with high-risk ccRCC. The predictive algorithm showed that, according to the three CRLR models, the low-risk group was more sensitive to nine target drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706, ATRA, AP.24534, axitinib, and AZ628), based on the estimated half-maximal inhibitory concentrations. In contrast, the high-risk group was more sensitive to ABT.263 and AKT inhibitors VIII and AS601245. Using the CRLR models, the correlation between the tumor immune microenvironment and cancer immunotherapy response revealed that high-risk patients are more likely to respond to immunotherapy than low-risk patients. In terms of immune marker levels, there were significant differences between the high- and low-risk groups. A high TMB score in the high-risk CRLR group was associated with worse survival, which could be a prognostic factor for KIRC. (4) Conclusions: This study elucidates the core cuproptosis-related LncRNAs, FOXD2−AS1, AC026401.3, and LASTR, in terms of potential predictive value, immunotherapeutic strategy, and outcome of ccRCC. Full article
(This article belongs to the Special Issue Identification of Important Biomolecules of Chronic Diseases from Multi-Omics Data by Using Machine Learning and Deep Learning)
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