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Molecular Research of Multi-omics in Cancer
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Molecular Research of Multi-omics in Cancer

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 7660

Special Issue Editor

Dr. Siddharth Pratap

E-Mail Website
Guest Editor
Department of Microbiology, Immunology, and Physiology: Bioinformatics Core, Meharry Medical College, Nashville, TN 37208, USA
Interests: bioinformatics; genomics; transcriptomics; proteomics; biomedical informatics; health disparities

Special Issue Information

Dear Colleagues,

Bioinformatics and “omics”-level technologies have revolutionized cancer research by utilizing computational methods to analyze large-scale biological data. High-throughput sequencing (HTS), such as DNA-Seq and RNA-Seq, combined with advances in proteomics-scale mass spectrometry have significantly contributed to molecular biology methods in discovering novel pathways in cancer. Advanced algorithms have facilitated the discovery of biomarkers for early detection and personalized treatment strategies. Bioinformatics contributes to precision medicine, allowing researchers to uncover molecular intricacies and develop targeted therapies, significantly advancing our understanding and treatment of cancer.

This Special Issue aims to contribute to the evolving landscape of cancer research by leveraging advanced bioinformatics and molecular biology techniques. By focusing on bioinformatics utilizing high-throughput sequencing techniques (DNA-Seq, RNA-Seq, ChIP-Seq) and/or proteomics, we hope to uncover novel perspectives that will advance our understanding of cancer and its potential implications in research and treatment. Researchers are encouraged to submit review and original research articles that contribute to the collective knowledge in this critical and rapidly evolving field.

Dr. Siddharth Pratap
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • bioinformatics
  • biomedical informatics
  • high-throughput sequencing (HTS)/next-generation sequencing (NGS)
  • DNA-Seq/genomics
  • RNA-Req/transcriptomics
  • mass spectrometry/proteomics

 

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Published Papers (5 papers)

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Research

24 pages, 8106 KiB  
Article
Multi-Omics Analysis of the Epigenetic Effects of Inflammation in Murine Type II Pneumocytes
by Jenna A. Fernandez, Qiyuan Han, Andrew T. Rajczewski, Thomas Kono, Nicholas A. Weirath, Alexander S. Lee, Abdur Rahim and Natalia Y. Tretyakova
Int. J. Mol. Sci. 2025, 26(10), 4692; https://doi.org/10.3390/ijms26104692 - 14 May 2025
Viewed by 224
Abstract
Chronic inflammation plays a central role in the pathogenesis of lung diseases including asthma, long COVID, chronic obstructive pulmonary disease (COPD), and lung cancer. Lipopolysaccharide (LPS) is a potent inflammatory agent produced by Gram-negative bacteria and also found in cigarette smoke. Our earlier [...] Read more.
Chronic inflammation plays a central role in the pathogenesis of lung diseases including asthma, long COVID, chronic obstructive pulmonary disease (COPD), and lung cancer. Lipopolysaccharide (LPS) is a potent inflammatory agent produced by Gram-negative bacteria and also found in cigarette smoke. Our earlier study revealed that the intranasal exposure of A/J mice to LPS for 7 days altered gene expression levels in alveolar Type II epithelial cells (AECIIs), which serve as precursors to lung adenocarcinoma and are also preferentially targeted by SARS-CoV-2. In the present work, we employed a comprehensive multi-omics approach to characterize changes in DNA methylation/hydroxymethylation, gene expression, and global protein abundances in the AECIIs of A/J mice following the sub-chronic exposure to LPS and after a 4-week recovery period. Exposure to LPS led to hypermethylation at regulatory elements within the genome such as enhancer regions and expression changes in genes known to play a role in lung cancer tumorigenesis. Changes in protein abundance were consistent with an inflammatory phenotype and also included tumor suppressor proteins. Integration of the multi-omics data resulted in a model where LPS-driven inflammation in AECIIs triggers epigenetic changes that, along with genetic mutations, may contribute to lung cancer development. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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20 pages, 7894 KiB  
Article
Fibrinogen Alpha Chain as a Potential Serum Biomarker for Predicting Response to Cisplatin and Gemcitabine Doublet Chemotherapy in Lung Adenocarcinoma: Integrative Transcriptome and Proteome Analyses
by Pritsana Raungrut, Jirapon Jirapongsak, Suchanan Tanyapattrapong, Thitaya Bunsong, Thidarat Ruklert, Kannika Kueakool, Paramee Thongsuksai and Narongwit Nakwan
Int. J. Mol. Sci. 2025, 26(3), 1010; https://doi.org/10.3390/ijms26031010 - 24 Jan 2025
Viewed by 929
Abstract
Cisplatin combined with gemcitabine, a doublet regimen, is the first-line treatment for patients with advanced lung adenocarcinoma (ADC); however, the treatment response remains poor. This study aimed to identify potential biomarkers for predicting response to cisplatin and gemcitabine. Tissue transcriptome and blood proteome [...] Read more.
Cisplatin combined with gemcitabine, a doublet regimen, is the first-line treatment for patients with advanced lung adenocarcinoma (ADC); however, the treatment response remains poor. This study aimed to identify potential biomarkers for predicting response to cisplatin and gemcitabine. Tissue transcriptome and blood proteome analyses were conducted on 27 patients with lung ADC. Blood-derived proteins that reflected tissue-specific biomarkers were obtained using Venn diagrams. The candidate proteins were validated by Western blotting. Lentivirus-mediated short hairpin RNA interference was used to verify the functional roles of the candidate proteins in human A549 cells. We identified 417 differentially expressed genes, including 52 upregulated and 365 downregulated genes, and 31 differentially expressed proteins, including 26 upregulated and 5 downregulated proteins. Integrative analysis revealed the presence of alpha-1-acid glycoprotein 1 (A1AG1) and fibrinogen alpha chain (FGA or FIBA) in both the tissue and serum. FGA levels were elevated in responders compared to non-responders, and reduced serum FGA levels were correlated with resistance to this regimen. Moreover, FGA knockdown in A549 cells resulted in resistance to the doublet regimen. Our findings indicate that FGA is a tissue-specific serum protein that may function as a blood-based biomarker to predict the response of patients with lung ADC to cisplatin plus gemcitabine chemotherapy. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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25 pages, 10169 KiB  
Article
Exploring Bioinformatics Tools to Analyze the Role of CDC6 in the Progression of Polycystic Ovary Syndrome to Endometrial Cancer by Promoting Immune Infiltration
by Yuhang Song, Jing Zhang, Yao Li, Lufeng Cheng, Hua Song, Yuhang Zhang, Guoqing Du, Sunyue Yu, Yizhou Zou and Qi Xu
Int. J. Mol. Sci. 2024, 25(23), 12974; https://doi.org/10.3390/ijms252312974 - 3 Dec 2024
Viewed by 1200
Abstract
Cell division cycle 6 (CDC6) is essential for the initiation of DNA replication in eukaryotic cells and contributes to the development of various human tumors. Polycystic ovarian syndrome (PCOS) is a reproductive endocrine disease in women of childbearing age, with a significant risk [...] Read more.
Cell division cycle 6 (CDC6) is essential for the initiation of DNA replication in eukaryotic cells and contributes to the development of various human tumors. Polycystic ovarian syndrome (PCOS) is a reproductive endocrine disease in women of childbearing age, with a significant risk of endometrial cancer (EC). However, the role of CDC6 in the progression of PCOS to EC is unclear. Therefore, we examined CDC6 expression in patients with PCOS and EC. We evaluated the relationship between CDC6 expression and its prognostic value, potential biological functions, and immune infiltrates in patients with EC. In vitro analyses were performed to investigate the effects of CDC6 knockdown on EC proliferation, migration, invasion, and apoptosis. CDC6 expression was significantly upregulated in patients with PCOS and EC. Moreover, this protein caused EC by promoting the aberrant infiltration of macrophages into the immune microenvironment in patients with PCOS. A functional enrichment analysis revealed that CDC6 exerted its pro-cancer and pro-immune cell infiltration functions via the PI3K-AKT pathway. Moreover, it promoted EC proliferation, migration, and invasion but inhibited apoptosis. This protein significantly reduced EC survival when mutated. These findings demonstrate that CDC6 regulates the progression of PCOS to EC and promotes immune infiltration. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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22 pages, 3439 KiB  
Article
A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer
by Rossano Atzeni, Matteo Massidda, Enrico Pieroni, Vincenzo Rallo, Massimo Pisu and Andrea Angius
Int. J. Mol. Sci. 2024, 25(15), 8044; https://doi.org/10.3390/ijms25158044 - 24 Jul 2024
Viewed by 1827
Abstract
Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues [...] Read more.
Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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21 pages, 9299 KiB  
Article
The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome
by Victor Paromov, Vladimir N. Uversky, Ayorinde Cooley, Lincoln E. Liburd II, Shyamali Mukherjee, Insung Na, Guy W. Dayhoff II and Siddharth Pratap
Int. J. Mol. Sci. 2024, 25(3), 1552; https://doi.org/10.3390/ijms25031552 - 26 Jan 2024
Cited by 2 | Viewed by 2754
Abstract
Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human [...] Read more.
Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed “the unfoldome”. Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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