[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.1
4.9
Journals
IJMS
Special Issues
Data Science in Cancer Genomics and Precision Medicine: 2nd Edition
ijms-logo
Submit to Special Issue Submit Abstract to Special Issue Review for IJMS Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Data Science in Cancer Genomics and Precision Medicine: 2nd Edition

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 1284

Special Issue Editor

Dr. Apostolos Zaravinos

E-Mail Website
Guest Editor
Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), 1516 Nicosia, Cyprus
Interests: cancer genomics; precision medicine; data science in genomics; next-generation sequencing; translational oncology; tumor immunology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is a continuation to our series on the hot topic of “Data Science in Cancer Genomics and Precision Medicine”. We have already published a successful Special Issue, receiving interesting contributions and stimulating discussions (https://www.mdpi.com/journal/ijms/special_issues/Genomics_Medicine).

Data science in cancer genomics represents a new interdisciplinary field that applies statistics and next-generation sequencing (NGS) technologies to understand alterations in the genome of cancer cells. The data generated by these technologies are often termed “multi-omics data” and can include information on DNA, RNA, proteins, and epigenetic modifications. The use of data science in cancer genomics allows us to better understand the molecular basis of different cancers and exploit this information to match each patient with the most appropriate molecular targeted therapy, widely known as “precision medicine”. While traditional chemotherapy and radiation treatments target cellular processes that are common to both healthy and cancerous cells, precision medicine specifically directs newly developed treatments to cancer cells based on their underlying molecular profile.

This Special Issue of the International Journal of Molecular Sciences focuses on the research field of cancer genomics and precision medicine and welcomes both original research articles and review papers that deal with the molecular mechanisms underlying modifications in human cancer cells.

Dr. Apostolos Zaravinos
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

  • cancer genomics
  • big data
  • tumor immunology
  • translational oncology
  • precision medicine
  • next-generation sequencing
  • omics
  • cancer genomic datasets

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

27 pages, 12788 KiB  
Article
A Multi-Omics Analysis of a Mitophagy-Related Signature in Pan-Cancer
by Nora Agir, Ilias Georgakopoulos-Soares and Apostolos Zaravinos
Int. J. Mol. Sci. 2025, 26(2), 448; https://doi.org/10.3390/ijms26020448 - 7 Jan 2025
Viewed by 494
Abstract
Mitophagy, an essential process within cellular autophagy, has a critical role in regulating key cellular functions such as reproduction, metabolism, and apoptosis. Its involvement in tumor development is complex and influenced by the cellular environment. Here, we conduct a comprehensive analysis of a [...] Read more.
Mitophagy, an essential process within cellular autophagy, has a critical role in regulating key cellular functions such as reproduction, metabolism, and apoptosis. Its involvement in tumor development is complex and influenced by the cellular environment. Here, we conduct a comprehensive analysis of a mitophagy-related gene signature, composed of PRKN, PINK1, MAP1LC3A, SRC, BNIP3L, BECN1, and OPTN, across various cancer types, revealing significant differential expression patterns associated with molecular subtypes, stages, and patient outcomes. Pathway analysis revealed a complex interplay between the expression of the signature and potential effects on the activity of various cancer-related pathways in pan-cancer. Immune infiltration analysis linked the mitophagy signature with certain immune cell types, particularly OPTN with immune infiltration in melanoma. Methylation patterns correlated with gene expression and immune infiltration. Mutation analysis also showed frequent alterations in PRKN (34%), OPTN (21%), PINK1 (28%), and SRC (15%), with implications for the tumor microenvironment. We also found various correlations between the expression of the mitophagy-related genes and sensitivity in different drugs, suggesting that targeting this signature could improve therapy efficacy. Overall, our findings underscore the importance of mitophagy in cancer biology and drug resistance, as well as its potential for informing treatment strategies. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine: 2nd Edition)
►▼ Show Figures

Figure 1

Figure 1
<p><b>Mechanisms of mitophagy and associated mitochondrial pathways.</b> (<b>A</b>) PINK1/Parkin-mediated mitophagy: Under mitochondrial stress, PINK1 accumulates on the outer mitochondrial membrane and recruits the E3 ubiquitin ligase Parkin. Parkin ubiquitinates mitochondrial outer membrane proteins such as Mfn1/2, VDAC1, and Miro1. These ubiquitinated proteins are subsequently recognized by autophagy receptors (NDP52, p62, and OPTN), which facilitate the recruitment of the autophagy machinery, leading to mitochondrial degradation. This pathway is regulated by phosphorylation events mediated by TPK1. (<b>B</b>) BNIP3/NIX-mediated mitophagy and cell death pathways: BNIP3 and its homolog NIX, both regulated through phosphorylation, can promote mitophagy by directly interacting with autophagy machinery. Alternatively, BNIP3 induces mitochondrial permeabilization through Bax/Bak activation, leading to the opening of the mitochondrial permeability transition pore (mPTP) and dissociation of the COX1-UCP3 complex. This can trigger apoptosis, necrosis, or pyroptosis. In pyroptosis, BNIP3 activation is linked to Caspase-3/GSDME activity. (<b>C</b>) FUNDC1-mediated mitophagy: FUNDC1, a mitochondrial outer membrane protein, undergoes phosphorylation-dependent regulation to mediate mitophagy by interacting with autophagic components, enabling the selective degradation of damaged mitochondria.</p> Full article ">Figure 2
<p><b>Differential expression of the mitophagy-related signature in pan-cancer.</b> (<b>a</b>) The bubble plot illustrates the log<sub>2</sub> fold change (FC) in the expression of the mitophagy-related genes across a spectrum of cancer types, with significance denoted by the false discovery rate (FDR) values, being represented by the color and size of the bubbles. Blue indicates downregulation, while red indicates upregulation of each gene in the tumor versus the normal tissues. The size of the circles is associated with FDR significance. Notably, <span class="html-italic">PRKN</span> and <span class="html-italic">PINK1</span> show substantial upregulation in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), while <span class="html-italic">SRC</span> and <span class="html-italic">OPTN</span> are significantly elevated in breast cancer (BRCA) and colon adenocarcinoma (COAD), respectively. <span class="html-italic">BECN1</span> was significantly downregulated in KIRC, in contrast to <span class="html-italic">BNIP3L</span>, which was upregulated in the tumor. (<b>b</b>) Boxplots for selected cancer types, showcasing significant differences in gene expression between normal and tumor tissues. (<b>c</b>) Subtype-specific expression differences, emphasizing that certain genes exhibit distinct expression patterns within cancer subtypes, such as <span class="html-italic">SRC</span> in kidney renal clear cell carcinoma (KIRC) and <span class="html-italic">OPTN</span> in BRCA. Red large circles represent deregulation in mRNA expression that is statistically significant. (<b>d</b>) The boxplots depict expression differences (log<sub>2</sub> RSEM) across molecular subtypes in different cancer types (<span class="html-italic">PINK1</span> in LUSC, <span class="html-italic">OPTN</span> in BRCA, <span class="html-italic">SRC</span> in KIRC, <span class="html-italic">MAP1LC3A</span> in GBM, <span class="html-italic">BNIP3L</span> and <span class="html-italic">BECN1</span> in BRCA, and <span class="html-italic">PRKN</span> in LUAD). (<b>e</b>) The heatmap shows a general trend of stable or decreased (in some cases like <span class="html-italic">SRC</span> in BLCA or <span class="html-italic">PINK1</span> and <span class="html-italic">OPTN</span> in ACC) expression for the mitophagy-related signature with advancing tumor stages. (<b>f</b>) The Kaplan–Meier curves show the survival rates in different types of cancer, according to gene expression. Higher expression of <span class="html-italic">PRKN</span> in LUAD and of <span class="html-italic">PINK1</span> in LUSC is associated with poorer prognosis, thereby underscoring the clinical relevance of these mitophagy-related genes in cancer progression and patient survival.</p> Full article ">Figure 3
<p><b>Pathway activity associated with differential expression of mitophagy-related genes.</b> (<b>a</b>) Heatmap depicting the potential activation (A) or inhibitory (I) effects of the mRNA levels of the mitophagy-related gene signature on the activity of 10 cancer-related pathways in pan-cancer. The color scale indicates the percentage of pathway activation (red) or inhibition (blue). The percentages represent the frequency of gene association with pathway regulation in various types of cancer. (<b>b</b>) The boxplots compare the pathway activity scores (PAS) between high and low expression groups of <span class="html-italic">PRKN</span> and <span class="html-italic">OPTN</span> in BRCA. The FDR values indicate the significance of the differences in PAS found between the high and low expression groups.</p> Full article ">Figure 4
<p><b>Correlation between mitophagy-related gene expression and immune cell infiltration in pan-cancer.</b> (<b>a</b>) The heatmap represents the Spearman’s correlation between the infiltration of 24 immune cell types evaluated through ImmuCellAI, and the expression of the mitophagy-related gene signature across different cancer types. The color scale indicates the strength and direction of Spearman’s correlation (blue for negative, red for positive). #, Spearman’s rho &lt;−0.4 or &gt;0.4; *, <span class="html-italic">p</span> &lt; 0.01. (<b>b</b>) Correlation between the expression of the mitophagy-related signature and immune cell infiltrates in skin melanoma (SKCM). The size of the dots represents the significance (−log<sub>10</sub>FDR values), while the color indicates the correlation coefficient (blue for negative, red for positive). (<b>c</b>) Scatter plots depicting the Spearman’s correlation between <span class="html-italic">OPTN</span> expression and specific immune cell infiltrates (Th1, neutrophils, monocytes, and central memory infiltrates) in SKCM, with trend lines and correlation coefficients. <span class="html-italic">OPTN</span> expression is positively correlated with the infiltration of Th1 cells and the central memory infiltrate score, and negatively correlated with the infiltration of neutrophils and monocytes in SKCM.</p> Full article ">Figure 5
<p><b>DNA methylation analysis of mitophagy-related genes in pan-cancer and its correlation with their mRNA expression.</b> (<b>a</b>) The dot plot shows differential DNA methylation levels (tumor vs. normal) for mitophagy-related genes (<span class="html-italic">SRC</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">PRKN</span>, <span class="html-italic">MAP1LC3A</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">BNIP3L</span>) across various cancer types. The color scale represents the methylation difference (tumor–normal), and the size of the dots indicates the significance (FDR values). (<b>b</b>) The dot plot illustrates the correlation between DNA methylation levels and mRNA expression for the same set of genes across multiple cancer types. The color scale represents Spearman’s correlation coefficient, with the size of the dots indicating the significance (FDR values). (<b>c</b>) Integrative genomic analysis displaying the association between <span class="html-italic">PINK1</span> and <span class="html-italic">MAP1LC3A</span> methylation, expression and CNV, and various clinical and demographic features in KIRC. The top panel summarizes patient data [age at diagnosis, hemoglobin levels, histological type, tumor recurrence, smoking history, gender, tumor stage, sample type, and overall survival (OS)]. The middle panel shows the distribution of <span class="html-italic">PINK1</span> expression levels across different copy number alterations. Sections marked with ‘−2’ indicate homozygous deletions, while ‘+1’ or ‘+2’ would indicate low-level or high-level amplifications, respectively. Higher or lower expression levels of <span class="html-italic">PINK1</span> and <span class="html-italic">MAP1LC3A</span> are indicated, showing how these levels align with methylation patterns and CNVs. The bottom panel presents a detailed view of the <span class="html-italic">PINK1</span> (left) and <span class="html-italic">MAP1LC3A</span> (right) regions, showing the relationship between DNA methylation sites, CpG islands, gene structure, and expression levels. The color coding of the CpG sites (vertical lines) likely indicates their methylation status, with different shades representing varying levels of methylation.</p> Full article ">Figure 6
<p><b>Mutation landscape of <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">MAP1LC3A</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">BNIP3L</span> across various cancer types.</b> (<b>a</b>) The waterfall plot presents the tumor mutation burden (TMB) per cancer sample and the mutation distribution of the mitophagy-related gene signature across 400 cancer samples in the TCGA. Each row represents a gene, and each column represents a cancer sample. Different colors indicate different types of mutations, including missense mutations, nonsense mutations, frame-shift deletions, frame-shift insertions, in-frame deletions, splice-site mutations, and multi-hit mutations. The bar plot on the right shows the percentage of samples with alterations in each gene. The bottom annotation panel indicates the cancer type for each sample. (<b>b</b>) Summary of variant classifications and types. Bar plots show the number of different mutation types (missense, nonsense, etc.) and variant types (SNP, insertion, deletion) for the five genes. The SNV class distribution is displayed, highlighting the most frequent base substitutions (184 C&gt;T and 138 C&gt;A). The bottom left plot shows the distribution of variants per sample, with a median of 1 variant per sample. The bottom right plot shows the variant classification summary for each gene, with the percentage of samples altered. (<b>c</b>) The heatmap shows the mutation frequency of the mitophagy-related genes across different cancer types. The numbers in each cell represent the percentage of samples with mutations in the respective gene and cancer type. The intensity of the color corresponds to the mutation frequency, with a darker red indicating a higher frequency.</p> Full article ">Figure 7
<p><b>Copy number variations in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across different cancer types</b>. (<b>a</b>) The pie charts show the frequency and types of CNVs in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across various cancer types. Each pie chart represents a cancer type, with different colors indicating the type of CNV. (<b>b</b>) The dot plots illustrate the distribution of heterozygous amplifications (left) and heterozygous deletions (right) in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across different cancer types. The size of the dots corresponds to the percentage of samples with the respective CNV type. (<b>c</b>) The dot plots show the distribution of homozygous amplifications (left) and homozygous deletions (right) in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across various cancer types. The size of the dots represents the percentage of samples with the respective CNV type.</p> Full article ">Figure 8
<p>Correlation of <span class="html-italic">MAP1LC3A</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">SRC</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">PRKN</span> expression with drug sensitivity (IC50) in pan-cancer, using the GDSC (<b>a</b>) and CTRP (<b>b</b>) drug databases. The color, from red to blue, depicts the correlation between each gene’s mRNA expression and IC50. Also, the bubble size represents the false discovery rate (FDR), with larger circles indicating stronger statistical significance. The color gradient indicates the direction and magnitude of correlation. Blue color, negative correlation; red color, positive correlation. Significant correlations (FDR &lt; 0.05) are emphasized with bold outlines, highlighting the most critical interactions. (<b>c</b>) Regulator prioritization of the mitophagy-related gene signature. Each column is a data cohort. Genes are ranked based on their average score with multiple cohorts. The colors correspond to different score values, ranging from −3 (blue) to +3 (red).</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop