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Algorithms for Exploring the Molecular Mechanisms of Tumorigenesis and Evolution
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Algorithms for Exploring the Molecular Mechanisms of Tumorigenesis and Evolution

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 7891

Special Issue Editors

Dr. Yuanyan Xiong

E-Mail Website
Guest Editor
Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China
Interests: cancer biology; bioinformatics; genetics; epigenetics; transcriptomics; tumorigenesis
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Zhang

E-Mail Website
Guest Editor
School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
Interests: tumorigenesis; immunotherapy; pan-cancer analysis; drug targets; algorithms

Special Issue Information

Dear Colleagues,

The molecular mechanisms of tumorigenesis and evolution play an important role in the diagnosis and treatment of tumors. Such mechanisms have been described in multi-omics data (e.g., genomics, epigenetics, gene expression, metabolomics and proteomics). Currently, through large tumor sequencing databases such as The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC), many algorithms have been applied for corresponding data analysis, and extensive studies have been performed in tumorigenesis and evolution, including the use of various models to determine the dependencies between relevant factors from multiple perspectives of temporal and spatial dynamics. Various algorithms have been used to compute and identify potential biomarkers and drug targets in tumorigenesis and evolution for novel tumor diagnosis and treatment.

The aim of this Special Issue is to provide new algorithms to explore the molecular mechanisms of tumorigenesis and evolution in order to gain a more comprehensive understanding of the conventional mechanisms of cancer, and to provide several potentially useful targets for further tumor diagnosis and treatment.

Dr. Yuanyan Xiong
Dr. Hui Zhang
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. Genes 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 2600 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

  • tumorigenesis
  • immunotherapy
  • pancancer analysis
  • drug targets
  • algorithms
  • tumor treatments
  • multi-omics

Published Papers (4 papers)

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Research

16 pages, 5435 KiB  
Article
A Comprehensive Pan-Cancer Analysis of the Potential Biological Functions and Prognosis Values of RICTOR
by Ying Sun, Rui Li, Baoting Nong, Zhou Songyang, Xianren Wang, Wenbin Ma and Qin Zhou
Genes 2023, 14(6), 1280; https://doi.org/10.3390/genes14061280 - 16 Jun 2023
Cited by 2 | Viewed by 1732
Abstract
The importance of the network defined by phosphatidylinositol-3-kinase (PI3K), AKT and mammalian target of rapamycin (mTOR) downstream of Receptor Tyrosine Kinase (RTK) has been recognized for many years. However, the central role of RICTOR (rapamycin-insensitive companion of mTOR) in this pathway has only [...] Read more.
The importance of the network defined by phosphatidylinositol-3-kinase (PI3K), AKT and mammalian target of rapamycin (mTOR) downstream of Receptor Tyrosine Kinase (RTK) has been recognized for many years. However, the central role of RICTOR (rapamycin-insensitive companion of mTOR) in this pathway has only recently come to light. The function of RICTOR in pan-cancer still needs to be systematically elucidated. In this study, we examined RICTOR’s molecular characteristics and clinical prognostic value by pan-cancer analysis. Our findings indicate that RICTOR was overexpressed in twelve cancer types, and a high RICTOR expression was linked to poor overall survival. Moreover, the CRISPR Achilles’ knockout analysis revealed that RICTOR was a critical gene for the survival of many tumor cells. Function analysis revealed that RICTOR-related genes were mainly involved in TOR signaling and cell growth. We further demonstrated that the RICTOR expression was significantly influenced by genetic alteration and DNA-methylation in multiple cancer types. Additionally, we found a positive relationship between RICTOR expression and the immune infiltration of macrophages and cancer-associated fibroblasts in Colon adenocarcinoma and Head and Neck squamous cell carcinoma. Finally, we validated the ability of RICTOR in sustaining tumor growth and invasion in the Hela cell line using cell-cycle analysis, the cell proliferation assay, and wound-healing assay. Our pan-cancer analysis highlights the critical role of RICTOR in tumor progression and its potential as a prognostic marker for various cancer types. Full article
(This article belongs to the Special Issue Algorithms for Exploring the Molecular Mechanisms of Tumorigenesis and Evolution)
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14 pages, 1786 KiB  
Article
Comprehensive RNA-Seq Analysis Pipeline for Non-Model Organisms and Its Application in Schmidtea mediterranea
by Yanzhi Wang, Sijun Li, Baoting Nong, Weiping Zhou, Shuhua Xu, Zhou Songyang and Yuanyan Xiong
Genes 2023, 14(5), 989; https://doi.org/10.3390/genes14050989 - 27 Apr 2023
Viewed by 2326
Abstract
RNA sequencing (RNA-seq) is a high-throughput technology that provides in-depth information on transcriptome. The advancement and dropping costs of RNA sequencing, accompanied by more available reference genomes for different species, make transcriptome analysis in non-model organisms possible. Current obstacles in analyzing RNA-seq data [...] Read more.
RNA sequencing (RNA-seq) is a high-throughput technology that provides in-depth information on transcriptome. The advancement and dropping costs of RNA sequencing, accompanied by more available reference genomes for different species, make transcriptome analysis in non-model organisms possible. Current obstacles in analyzing RNA-seq data include a lack of functional annotation, which may complicate the process of linking genes to corresponding functions. Here, we provide a one-stop RNA-seq analysis pipeline, PipeOne-NM, for transcriptome functional annotation, non-coding RNA identification, and transcripts alternative splicing analysis of non-model organisms, intended for use with Illumina platform-based RNA-seq data. We performed PipeOne-NM on 237 Schmidtea mediterranea RNA-seq runs and assembled a transcriptome with 84,827 sequences from 49,320 genes, identifying 64,582 mRNA from 35,485 genes, 20,217 lncRNA from 17,084 genes, and 3481 circRNAs from 1103 genes. In addition, we performed a co-expression analysis of lncRNA and mRNA and identified that 1319 lncRNA co-express with at least one mRNA. Further analysis of samples from S. mediterranea sexual and asexual strains revealed the role of sexual reproduction in gene expression profiles. Samples from different parts of asexual S. mediterranea revealed that differential expression profiles of different body parts correlated with the function of conduction of nerve impulses. In conclusion, PipeOne-NM has the potential to provide comprehensive transcriptome information for non-model organisms on a single platform. Full article
(This article belongs to the Special Issue Algorithms for Exploring the Molecular Mechanisms of Tumorigenesis and Evolution)
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13 pages, 1049 KiB  
Article
An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
by Xin Hui Tay, Shahreen Kasim, Tole Sutikno, Mohd Farhan Md Fudzee, Rohayanti Hassan, Emelia Akashah Patah Akhir, Norshakirah Aziz and Choon Sen Seah
Genes 2023, 14(3), 574; https://doi.org/10.3390/genes14030574 - 24 Feb 2023
Cited by 2 | Viewed by 1479
Abstract
The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the [...] Read more.
The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network’s structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The within-dataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types. Full article
(This article belongs to the Special Issue Algorithms for Exploring the Molecular Mechanisms of Tumorigenesis and Evolution)
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18 pages, 4453 KiB  
Article
A Novel Identified Necroptosis-Related Risk Signature for Prognosis Prediction and Immune Infiltration Indication in Acute Myeloid Leukemia Patients
by Yong Sun, Ruiheng Wang, Shufeng Xie, Yuanli Wang and Han Liu
Genes 2022, 13(10), 1837; https://doi.org/10.3390/genes13101837 - 11 Oct 2022
Cited by 6 | Viewed by 1983
Abstract
AML ranks second in the most common types of leukemia diagnosed in both adults and children. Necroptosis is a programmed inflammatory cell death form reported to be an innate immune effector against microbial and viral pathogens and recently has been found to play [...] Read more.
AML ranks second in the most common types of leukemia diagnosed in both adults and children. Necroptosis is a programmed inflammatory cell death form reported to be an innate immune effector against microbial and viral pathogens and recently has been found to play an eventful role in the oncogenesis, progression, and metastasis of cancer. This study is designed to explore the potential value of necroptosis in predicting prognostic and optimizing the current therapeutic strategies for AML patients. We collected transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases and selected necroptosis-related genes with both differential significance and prognostic value. Six genes (YBX3, ZBP1, CDC37, ALK, BRAF, and BNIP3) were incorporated to generate a risk model with the implementation of multivariate Cox regression. The signature was proven to be an independent prognostic predictor in both training and validation cohorts with hazard ratios (HRs) of 1.51 (95% CI: 1.33–1.72) and 1.57 (95% CI: 1.16–2.12), respectively. Moreover, receiver operating characteristic (ROC) curve was utilized to quantify the predictive performance of the signature and satisfying results were shown with the area under the curve (AUC) up to 0.801 (3-year) and 0.619 (3-year), respectively. In addition, the subtyping of AML patients based on the risk signature demonstrated a significant correlation with the immune cell infiltration and response to immunotherapy. Finally, we incorporated risk signature with the classical clinical features to establish a nomogram which may contribute to the improvement of clinical management. To conclude, this study identified a necroptosis-related signature as a novel biomarker to improve the risk stratification, to inform the immunotherapy efficacy, and to indicate the therapeutic option of targeted therapy. Full article
(This article belongs to the Special Issue Algorithms for Exploring the Molecular Mechanisms of Tumorigenesis and Evolution)
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