International Journal of Molecular Sciences

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Cancer Bioinformatics

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Dr. Gianluca Santamaria

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1. Department of Internal Medicine I, University Hospital rechts der Isar, Technical University of Munich, 81675 Munich, Germany
2. Department of Experimental and Clinical Medicine, University “Magna Graecia” of Catanzaro Campus “S. Venuta”, Germaneto, 88100 Catanzaro, Italy
Interests: bioinformatics; cancer-related genes

Special Issue Information

Dear Colleagues,

Cancer is a leading cause of death worldwide. Cancer biology is an essential research field in order to reveal how cancer develops, evolves, and responds to therapy. By taking advantage of the “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational approaches in bioinformatics can help scientists in deciphering the complexity of cancer heterogeneity, tumorigenesis, tumor microenvironment and drug discovery. Particularly, bioinformatics enables the analysis of cancer from a broad perspective, including genetics, transcriptomics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the unprecedented next-generation sequencing (NGS) data (now at single-cell resolution), and multiple landmark cancer-focused projects (e.g., The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC)), bioinformatics has an advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer. Despite these tremendous advancements, multiple obstacles remain before successful clinical translation. Thus, this Special Issue seeks research papers and review articles that focus on novel approaches and applications of bioinformatics and machine learning that can help in overcoming barriers to further shed light on the driving forces behind the tumorigenesis and development of anticancer drugs and treatments. Therefore, I would like to invite you to submit articles on your research, given your competence in in this field. Moreover, you are encouraged to share this Special Issue call among your colleagues, collaborators and connections.

Dr. Gianluca Santamaria
Guest Editor

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20 pages, 9026 KiB

Open AccessArticle

Establishing a Proteomics-Based Signature of AKR1C3-Related Genes for Predicting the Prognosis of Prostate Cancer

by Xiaoli Cui, Changcheng Li, Jipeng Ding, Zhou Yao, Tianyu Zhao, Jiahui Guo, Yaru Wang and Jing Li

Int. J. Mol. Sci. 2023, 24(5), 4513; https://doi.org/10.3390/ijms24054513 - 24 Feb 2023

Cited by 6 | Viewed by 2986

Abstract

Aldo-keto reductase family 1 member C3 (AKR1C3) plays an important role in prostate cancer (PCa) progression, particularly in castration-resistant prostate cancer (CRPC). It is necessary to establish a genetic signature associated with AKR1C3 that can be used to predict the prognosis of PCa patients and provide important information for clinical treatment decisions. AKR1C3-related genes were identified via label-free quantitative proteomics of the AKR1C3-overexpressing LNCaP cell line. A risk model was constructed through the analysis of clinical data, PPI, and Cox-selected risk genes. Cox regression analysis, Kaplan–Meier (K–M) curves, and receiver operating characteristic (ROC) curves were used to verify the accuracy of the model, and two external datasets were used to verify the reliability of the results. Subsequently, the tumor microenvironment and drug sensitivity were explored. Moreover, the roles of AKR1C3 in the progression of PCa were verified in LNCaP cells. MTT, colony formation, and EdU assays were conducted to explore cell proliferation and drug sensitivity to enzalutamide. Migration and invasion abilities were measured using wound-healing and transwell assays, and qPCR was used to assess the expression levels of AR target genes and EMT genes. CDC20, SRSF3, UQCRH, INCENP, TIMM10, TIMM13, POLR2L, and NDUFAB1 were identified as AKR1C3-associated risk genes. These risk genes, established using the prognostic model, can effectively predict the recurrence status, immune microenvironment, and drug sensitivity of PCa. Tumor-infiltrating lymphocytes and several immune checkpoints that promote cancer progression were higher in high-risk groups. Furthermore, there was a close correlation between the sensitivity of PCa patients to bicalutamide and docetaxel and the expression levels of the eight risk genes. Moreover, through in vitro experiments, Western blotting confirmed that AKR1C3 enhanced SRSF3, CDC20, and INCENP expression. We found that PCa cells with a high expression of AKR1C3 have high proliferation ability and high migration ability and were insensitive to enzalutamide. AKR1C3-associated genes had a significant role in the process of PCa, immune responses, and drug sensitivity and offer the potential for a novel model for prognostic prediction in PCa. Full article

(This article belongs to the Special Issue Cancer Bioinformatics)

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