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Genomics and Bioinformatics Based Analysis of Cancer

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 26533

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Special Issue Editor

Dr. Jonathan J. Ellis

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Guest Editor

School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, Australia
Interests: genomics; bioinformatics; cancer

Special Issue Information

Dear Colleagues,

With high-throughput sequencing technologies having matured and innovative long-read technologies appearing, our access to large cohort scale genomics data has never been greater. With this increase in data volume and new technologies comes a need for equally innovative ways to handle the associated informatics and analysis. This development in technology has given us our clearest window into the genomes of cancer yet, allowing us to track the progression of tumours from primary, relapse through to metastatic in fine detail and detect minute amounts of the tumour circulating in the blood. Thanks to these approaches, we have started to see the translation of genomics cancer research into a clinical setting with personalised tumour reports and treatments becoming increasingly used to treat cancer throughout the world. This Special Issue aims to highlight recent advances in the application of genomics to the understanding of cancer and/or its treatment and developments in bioinformatics tools for novel analysis in the context of cancer genomics.

Dr. Jonathan J. Ellis
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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

genomics
bioinformatics
artificial intelligence
translational research
personalised medicine
high-throughput sequencing
long-read sequencing
ctDNA

Published Papers (11 papers)

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Research

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19 pages, 9336 KiB

Open AccessArticle

Differentially Expressed Genes, miRNAs and Network Models: A Strategy to Shed Light on Molecular Interactions Driving HNSCC Tumorigenesis

by Saniya Arfin, Dhruv Kumar, Andrea Lomagno, Pietro Luigi Mauri and Dario Di Silvestre

Cancers 2023, 15(17), 4420; https://doi.org/10.3390/cancers15174420 - 04 Sep 2023

Viewed by 1523

Abstract

Head and neck squamous cell carcinoma (HNSCC) is among the most common cancer worldwide, accounting for hundreds thousands deaths annually. Unfortunately, most patients are diagnosed in an advanced stage and only a percentage respond favorably to therapies. To help fill this gap, we hereby propose a retrospective in silico study to shed light on gene–miRNA interactions driving the development of HNSCC. Moreover, to identify topological biomarkers as a source for designing new drugs. To achieve this, gene and miRNA profiles from patients and controls are holistically reevaluated using protein–protein interaction (PPI) and bipartite miRNA–target networks. Cytoskeletal remodeling, extracellular matrix (ECM), immune system, proteolysis, and energy metabolism have emerged as major functional modules involved in the pathogenesis of HNSCC. Of note, the landscape of our findings depicts a concerted molecular action in activating genes promoting cell cycle and proliferation, and inactivating those suppressive. In this scenario, genes, including VEGFA, EMP1, PPL, KRAS, MET, TP53, MMPs and HOXs, and miRNAs, including mir-6728 and mir-99a, emerge as key players in the molecular interactions driving HNSCC tumorigenesis. Despite the heterogeneity characterizing these HNSCC subtypes, and the limitations of a study pointing to relationships that could be context dependent, the overlap with previously published studies is encouraging. Hence, it supports further investigation for key molecules, both those already and not correlated to HNSCC. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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31 pages, 4878 KiB

Open AccessArticle

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine

by Ohanes Ashekyan, Nerses Shahbazyan, Yeva Bareghamyan, Anna Kudryavzeva, Daria Mandel, Maria Schmidt, Henry Loeffler-Wirth, Mohamed Uduman, Dhan Chand, Dennis Underwood, Garo Armen, Arsen Arakelyan, Lilit Nersisyan and Hans Binder

Cancers 2023, 15(15), 3835; https://doi.org/10.3390/cancers15153835 - 28 Jul 2023

Cited by 2 | Viewed by 2802

Abstract

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial–mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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18 pages, 3228 KiB

Open AccessArticle

Bioinformatics Screen Reveals Gli-Mediated Hedgehog Signaling as an Associated Pathway to Poor Immune Infiltration of Dedifferentiated Liposarcoma

by Erik P. Beadle, Natalie E. Bennett and Julie A. Rhoades

Cancers 2023, 15(13), 3360; https://doi.org/10.3390/cancers15133360 - 27 Jun 2023

Viewed by 2726

Abstract

Liposarcomas are the most diagnosed soft tissue sarcoma, with most cases consisting of well-differentiated (WDLPS) or dedifferentiated (DDLPS) histological subtypes. While both tumor subtypes can have clinical recurrence due to incomplete resections, DDLPS often has worse prognosis due to a higher likelihood of metastasis compared to its well-differentiated counterpart. Unfortunately, targeted therapeutic interventions have lagged in sarcoma oncology, making the need for molecular targeted therapies a promising future area of research for this family of malignancies. In this work, previously published data were analyzed to identify differential pathways that may contribute to the dedifferentiation process in liposarcoma. Interestingly, Gli-mediated Hedgehog signaling appeared to be enriched in dedifferentiated adipose progenitor cells and DDLPS tumors, and coincidentally Gli1 is often co-amplified with MDM2 and CDK4, given its genomic proximity along chromosome 12q13-12q15. However, we find that Gli2, but not Gli1, is differentially expressed between WDLPS and DDLPS, with a noticeable co-expression signature between Gli2 and genes involved in ECM remodeling. Additionally, Gli2 co-expression had a noticeable transcriptional signature that could suggest Gli-mediated Hedgehog signaling as an associated pathway contributing to poor immune infiltration in these tumors. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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30 pages, 4214 KiB

Open AccessArticle

Multi-Omic Analysis of CIC’s Functional Networks Reveals Novel Interaction Partners and a Potential Role in Mitotic Fidelity

by Yuka Takemon, Véronique G. LeBlanc, Jungeun Song, Susanna Y. Chan, Stephen Dongsoo Lee, Diane L. Trinh, Shiekh Tanveer Ahmad, William R. Brothers, Richard D. Corbett, Alessia Gagliardi, Annie Moradian, J. Gregory Cairncross, Stephen Yip, Samuel A. J. R. Aparicio, Jennifer A. Chan, Christopher S. Hughes, Gregg B. Morin, Sharon M. Gorski, Suganthi Chittaranjan and Marco A. Marra

Cancers 2023, 15(10), 2805; https://doi.org/10.3390/cancers15102805 - 17 May 2023

Cited by 1 | Viewed by 2317

Abstract

CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC’s interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3′ and 5′ untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC’s functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC’s potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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16 pages, 1882 KiB

Open AccessArticle

Comparing Genetic Risk and Clinical Risk Classification in Luminal-like Breast Cancer Patients Using a 23-Gene Classifier

by Chi-Cheng Huang, Ting-Hao Chen, Liang-Chih Liu, Chiun-Sheng Huang, Ji-An Liang, Yu-Chen Hsu, Chia-Ming Hsieh, Sean-Lin Huang, Kuan-Hui Shih and Ling-Ming Tseng

Cancers 2022, 14(24), 6263; https://doi.org/10.3390/cancers14246263 - 19 Dec 2022

Viewed by 1526

Abstract

Background: A 23-gene classifier has been developed based on gene expression profiles of Taiwanese luminal-like breast cancer. We aim to stratify risk of relapse and identify patients who may benefit from adjuvant chemotherapy based on genetic model among distinct clinical risk groups. Methods: There were 248 luminal (hormone receptor-positive and human epidermal growth factor receptor II-negative) breast cancer patients with 23-gene classifier results. Using the modified Adjuvant! Online definition, clinical high/low-risk groups were tabulated with the genetic model. The primary endpoint was a recurrence-free interval (RFI) at 5 years. Results: There was a significant difference between the high/low-risk groups defined by the 23-gene classifier for the 5-year prognosis of recurrence (16 recurrences in high-risk and 3 recurrences in low-risk; log-rank test: p < 0.0001). Among the clinically high-risk group, the 5-year RFI of high risk defined by the 23-gene classifier was significantly higher than that of the low-risk group (15 recurrences in high-risk and 2 recurrences in low-risk; log-rank test: p < 0.0001). Conclusion: This study showed that 23-gene classifier can be used to stratify clinically high-risk patients into distinct survival patterns based on genomic risks and displays the potentiality to guide adjuvant chemotherapy. The 23-gene classifier can provide a better estimation of breast cancer prognosis which can help physicians make a better treatment decision. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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21 pages, 3974 KiB

Open AccessArticle

Comprehensive Pan-Cancer Analyses of Immunogenic Cell Death as a Biomarker in Predicting Prognosis and Therapeutic Response

by Yuan Wang, Yongbiao Huang, Mu Yang, Yulong Yu, Xinyi Chen, Li Ma, Lingyan Xiao, Chaofan Liu, Bo Liu and Xianglin Yuan

Cancers 2022, 14(23), 5952; https://doi.org/10.3390/cancers14235952 - 01 Dec 2022

Viewed by 2007

Abstract

Immunogenic cell death (ICD), a form of regulated cell death, is related to anticancer therapy. Due to the absence of widely accepted markers, characterizing ICD-related phenotypes across cancer types remained unexplored. Here, we defined the ICD score to delineate the ICD landscape across 33 cancerous types and 31 normal tissue types based on transcriptomic, proteomic and epigenetics data from multiple databases. We found that ICD score showed cancer type-specific association with genomic and immune features. Importantly, the ICD score had the potential to predict therapy response and patient prognosis in multiple cancer types. We also developed an ICD-related prognostic model by machine learning and cox regression analysis. Single-cell level analysis revealed intra-tumor ICD state heterogeneity and communication between ICD-based clusters of T cells and other immune cells in the tumor microenvironment in colon cancer. For the first time, we identified IGF2BP3 as a potential ICD regulator in colon cancer. In conclusion, our study provides a comprehensive framework for evaluating the relation between ICD and clinical relevance, gaining insights into identification of ICD as a potential cancer-related biomarker and therapeutic target. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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21 pages, 45902 KiB

Open AccessArticle

Oncogenic Role of HMGB1 as An Alarming in Robust Prediction of Immunotherapy Response in Colorectal Cancer

by Huijiao Lu, Mengyi Zhu, Lin Qu, Hongwei Shao, Rongxin Zhang and Yan Li

Cancers 2022, 14(19), 4875; https://doi.org/10.3390/cancers14194875 - 05 Oct 2022

Cited by 7 | Viewed by 2115

Abstract

Objective: To assess the correlation between HMGB1 expression and the patient prognosis in a multicancer context. Methods: The potential oncogenic role of HMGB1 was explored in forty tumors through the TCGA, GEO, and Oncomine datasets. We analyzed the clinical prognostic value and antitumor immunotherapy of HMGB1 in a multicancer context using GEO (GSE111636). Results: High expression of HMGB1 is present in multicancer cases, and its low expression is closely associated with the prognostic survival of patients, in terms of both overall and disease-free survival in ACC and LUAD. Further investigation revealed that the high expression of gastric and lung cancer is closely associated with low risk and better prognosis of patients based on COX and Kaplan–Meier analysis of OS, FP and PPS. HMGB1 expression was found to be significantly correlated with cancer-associated fibroblast and CD8⁺ T cell infiltration in the TME. The analysis of GO functional annotation/KEGG pathways indicates that HMGB1 may regulate tumor immunity-related pathways, such as the tumor immunotherapy response in colorectal cancer. The function of four genes as hubs are confirmed by in vitro HMGB1 knockdown which led to inhibition of cell proliferation and metastasis in SW620 and SW480 cells. Conclusion: HMGB1 is a potential novel biomarker for improving clinical prognosis and antitumor immunotherapy efficacy. CDK1, HMGB2, SSRP1, and H2AFV may serve as key nodes for HMGB1 in colorectal cancer. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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21 pages, 2802 KiB

Open AccessArticle

Organic Anion Transporters (OAT) and Other SLC22 Transporters in Progression of Renal Cell Carcinoma

by Thomas C. Whisenant and Sanjay K. Nigam

Cancers 2022, 14(19), 4772; https://doi.org/10.3390/cancers14194772 - 29 Sep 2022

Cited by 5 | Viewed by 2008

Abstract

(1) Background: Many transporters of the SLC22 family (e.g., OAT1, OAT3, OCT2, URAT1, and OCTN2) are highly expressed in the kidney. They transport drugs, metabolites, signaling molecules, antioxidants, nutrients, and gut microbiome products. According to the Remote Sensing and Signaling Theory, SLC22 transporters play a critical role in small molecule communication between organelles, cells and organs as well as between the body and the gut microbiome. This raises the question about the potential role of SLC22 transporters in cancer biology and treatment. (2) Results: In two renal cell carcinoma RNA-seq datasets found in TCGA, KIRC and KIRP, there were multiple differentially expressed (DE) SLC22 transporter genes compared to normal kidney. These included SLC22A6, SLC22A7, SLC22A8, SLC22A12, and SLC22A13. The patients with disease had an association between overall survival and expression for most of these DE genes. In KIRC, the stratification of patient data by pathological tumor characteristics revealed the importance of SLC22A2, SLC22A6, and SLC22A12 in disease progression. Interaction networks combining the SLC22 with ADME genes supported the centrality of SLC22 transporters and other transporters (ABCG2, SLC47A1) in disease progression. (3) Implications: The fact that many of these genes are uric acid transporters is interesting because altered uric acid levels have been associated with kidney cancer. Moreover, these genes play key roles in processing metabolites and chemotherapeutic compounds, thus making them potential therapeutic targets. Finally, our analyses raise the possibility that current approaches may undertreat certain kidney cancer patients with low SLC22 expression and only localized disease while possibly overtreating more advanced disease in patients with higher SLC22 expression. Clinical studies are needed to investigate these possibilities. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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14 pages, 3366 KiB

Open AccessArticle

Whole-Exome Sequencing Reveals the Genomic Features of the Micropapillary Component in Ground-Glass Opacities

by Fanchen Meng, Yi Zhang, Siwei Wang, Tongyan Liu, Mengting Sun, Hongyu Zhu, Guozhang Dong, Zhijun Xia, Jing You, Xiangru Kong, Jintao Wu, Peng Chen, Fangwei Yuan, Xinyu Yu, Youtao Xu, Lin Xu and Rong Yin

Cancers 2022, 14(17), 4165; https://doi.org/10.3390/cancers14174165 - 27 Aug 2022

Cited by 3 | Viewed by 1817

Abstract

Background: Micropapillary components are observed in a considerable proportion of ground-glass opacities (GGOs) and contribute to the poor prognosis of patients with invasive lung adenocarcinoma (LUAD). However, the underlying mutational processes related to the presence of micropapillary components remain obscure, limiting the development of clinical interventions. Methods: We collected 31 GGOs, which were separated into paired micropapillary and non-micropapillary components using microdissection. Whole-exome sequencing (WES) was performed on the GGO components, and bioinformatics analysis was conducted to reveal the genomic features of the micropapillary component in invasive LUAD. Results: The micropapillary component had more genomic variations, including tumor mutation burden, intratumoral heterogeneity, and copy number variation. We also observed the enrichment of AID/APOBEC mutation signatures and an increased activation of the RTK/Ras, Notch, and Wnt oncogenic pathways within the micropapillary component. A phylogenetic analysis further suggested that ERBB2/3/4, NCOR1/2, TP53, and ZNF469 contributed to the micropapillary component’s progression during the early invasion of LUAD, a finding that was validated in the TCGA cohort. Conclusions: Our results revealed specific mutational characteristics of the micropapillary component of invasive LUAD in an Asian population. These characteristics were associated with the formation of high-grade invasive patterns. These preliminary findings demonstrated the potential of targeting the micropapillary component in patients with early-stage LUAD. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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11 pages, 2858 KiB

Open AccessArticle

Machine Learning Predictor of Immune Checkpoint Blockade Response in Gastric Cancer

by Ji-Yong Sung and Jae-Ho Cheong

Cancers 2022, 14(13), 3191; https://doi.org/10.3390/cancers14133191 - 29 Jun 2022

Cited by 8 | Viewed by 2891

Abstract

Predicting responses to immune checkpoint blockade (ICB) lacks official standards despite the discovery of several markers. Expensive drugs and different reactivities for each patient are the main disadvantages of immunotherapy. Gastric cancer is refractory and stem-like in nature and does not respond to immunotherapy. In this study, we aimed to identify a characteristic gene that predicts ICB response in gastric cancer and discover a drug target for non-responders. We built and evaluated a model using four machine learning algorithms for two cohorts of bulk and single-cell RNA seq to predict ICB response in gastric cancer patients. Through the LASSO feature selection, we discovered a marker gene signature that distinguishes responders from non-responders. VCAN, a candidate characteristic gene selected by all four machine learning algorithms, had a significantly high prevalence in non-responders (p = 0.0019) and showed a poor prognosis (p = 0.0014) at high expression values. This is the first study to discover a signature gene for predicting ICB response in gastric cancer by molecular subtype and provides broad insights into the treatment of stem-like immuno-oncology through precision medicine. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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Review

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23 pages, 2834 KiB

Open AccessReview

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

by Ali A. Rabaan, Muhammed A. Bakhrebah, Hajir AlSaihati, Saad Alhumaid, Roua A. Alsubki, Safaa A. Turkistani, Saleh Al-Abdulhadi, Yahya Aldawood, Abdulmonem A. Alsaleh, Yousef N. Alhashem, Jenan A. Almatouq, Ahlam A. Alqatari, Hejji E. Alahmed, Dalal A. Sharbini, Arwa F. Alahmadi, Fatimah Alsalman, Ahmed Alsayyah and Abbas Al Mutair

Cancers 2022, 14(22), 5595; https://doi.org/10.3390/cancers14225595 - 14 Nov 2022

Cited by 13 | Viewed by 3284

Abstract

As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process. Full article

(This article belongs to the Special Issue Genomics and Bioinformatics Based Analysis of Cancer)

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