Identification of crucial genes and pathways associated with prostate cancer in multiple databases

Guo, Hanxu; Zhang, Zhichao; Wang, Yuhang; Xue, Sheng

doi:10.1177/03000605211016624

Cited by 8 publications

(7 citation statements)

References 59 publications

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“…Here, several ivermectin biomarkers identified in our work have been reported to play a critical role in PC. For instance, RPLP0 has been identified as a key regulator in PC [68], while RPS18 is linked to PC recurrence and prognosis [69]. RPS2 has been recognized as a highly selective therapeutic target for PC; knocking down RPS2 expression using oligonucleotides in an in vivo PC3-3ML model led to a promising PC eradication [70].…”

Section: Discussionmentioning

confidence: 99%

Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis

Su,

Lee,

Zhang

et al. 2024

Pharmaceuticals

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Metastatic castration-resistant prostate cancer (mCRPC) remains a deadly disease due to a lack of efficacious treatments. The reprogramming of cancer metabolism toward elevated glycolysis is a hallmark of mCRPC. Our goal is to identify therapeutics specifically associated with high glycolysis. Here, we established a computational framework to identify new pharmacological agents for mCRPC with heightened glycolysis activity under a tumor microenvironment, followed by in vitro validation. First, using our established computational tool, OncoPredict, we imputed the likelihood of drug responses to approximately 1900 agents in each mCRPC tumor from two large clinical patient cohorts. We selected drugs with predicted sensitivity highly correlated with glycolysis scores. In total, 77 drugs predicted to be more sensitive in high glycolysis mCRPC tumors were identified. These drugs represent diverse mechanisms of action. Three of the candidates, ivermectin, CNF2024, and P276-00, were selected for subsequent vitro validation based on the highest measured drug responses associated with glycolysis/OXPHOS in pan-cancer cell lines. By decreasing the input glucose level in culture media to mimic the mCRPC tumor microenvironments, we induced a high-glycolysis condition in PC3 cells and validated the projected higher sensitivity of all three drugs under this condition (p < 0.0001 for all drugs). For biomarker discovery, ivermectin and P276-00 were predicted to be more sensitive to mCRPC tumors with low androgen receptor activities and high glycolysis activities (AR(low)Gly(high)). In addition, we integrated a protein–protein interaction network and topological methods to identify biomarkers for these drug candidates. EEF1B2 and CCNA2 were identified as key biomarkers for ivermectin and CNF2024, respectively, through multiple independent biomarker nomination pipelines. In conclusion, this study offers new efficacious therapeutics beyond traditional androgen-deprivation therapies by precisely targeting mCRPC with high glycolysis.

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Section: Discussionmentioning

confidence: 99%

Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis

Su,

Lee,

Zhang

et al. 2024

Pharmaceuticals

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“…Ribosomal protein S18(RPS18), also known as HKE3, is a type of ribosomal protein which has been supposed to an unfavorable predictor in several cancers including breast cancer (BC) 23 and colorectal cancer (CRC) 24 . Besides, RPS18 which was related to the recurrence and prognosis of prostate cancer (PCa) was shown to be a potential prognostic biomarker of PCa 25 . So far, there have been no reports on the relationship between RPS18 and GC, our study revealed RPS18 was positively correlated with the risk of GC for the rst time.…”

Section: Discussionmentioning

confidence: 99%

Single-cell RNA sequencing combined with mendelian randomization analysis identifies putatively causal genes associated with Gastric cancer

Xu,

Jia,

et al. 2024

Preprint

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To examine the potential causal genes for gastric cancer (GC) susceptibility and effective disease prognosis biomarkers. In this research, public single-cell RNA sequencing (scRNA-seq) data were applied to analyze different cell types and to identify differentially expressed genes (DEGs). The summary data-based Mendelian randomization (SMR) was employed to integrate genome-wide association studies (GWAS) with expression quantitative trait loci (eQTL) to investigate potential genes that causally associated with GC. Besides, a systematic SMR analysis with methylation quantitative trait loci (mQTL) was conducted to reveal the methylation regulatory relationship of GC-related pathogenic genes. In addition, bioinformatic tools including GeneMANIA, gene set enrichment analysis (GSEA), KM-plotter and immune infiltration analysis were used to further explore the biological mechanisms and functions of the candidate genes in GC. Seven cell types and 1707 cell type-specific DEGs were identified by scRNA-seq analysis. Using the SMR and HEIDI test, we screened out 9 genes by integrating GWAS with eQTL analysis from gastric tissue and 26 genes from whole blood. Based on the DEGs identified by scRNA-seq and SMR analysis, 4 positively related genes(HLA-DQB1、PSMB9、RPS18 and TAF1C)were prioritized as candidate GC-causal genes.KM-plotter indicated that aberrant expression of the candidate genes was significantly associated with the prognosis of GC patients. Immune infiltration analysis provides a theoretical basis for these candidate genes to become potential immunotherapeutic targets. These findings may give novel insight into the molecular mechanisms of GC and provide potential biomarkers for therapeutic interventions of GC.

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“…Применение ИИ возможно в качестве альтернативы существующим инструментам скрининга [15,16,18] населения и в качестве инструмента, выявляющего предикторы кастрационно-резистентной формы РПЖ [33]; для анализа крови и мочи -процедур, не требующих дополнительных эко- Стоит отметить, что ряд исследований [15,24,28] проведены in silico (на основании анализа молекулярно-генетических баз данных без валидации на клеточных линиях или на реальных пациентах), поэтому полезны скорее как справочная информация. Тем не менее полученные результаты могут послужить надежной базой для дальнейших исследований в области молекулярной диагностики и геномики.…”

Section: Discussionunclassified

Artificial intelligence in molecular and genomic prostate cancer diagnostics

Morozov,

Bazarkin,

Vovdenko

et al. 2024

Vestn. Urol.

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Introduction. Many molecular genetic analyses have been proposed to predict the course of prostate cancer (PCa). They have the potential to develop artificial intelligence (AI) algorithms by processing large amounts of data and define connections between them.Objective. To evaluate the possibilities of using artificial intelligence in early diagnosis and prognosis of prostate cancer.Materials & methods. We conducted a systematic review of the literature on the Medline citation database. We have selected papers that provide data on the use of AI in vitro, in vivo and in silico systems to determine biological and genetic markers and/or their relationship to clinical data of PCa-patients from 2020 to 2023. The quantitative synthesis includes 16 articles.Results. AI can identify metabolic and genetic «signature» of PCa, the key elements of signal pathways, thus fulfilling complex tasks in the field of bioinformatics. AI analyses various biomaterials: prostate tissue, blood, and urine. When evaluating prostate tissue for aberrations, AI can help a pathologist. For example, AI can predict the histological status of genes, eliminating the need for IHC or tissue sequencing, significantly reducing the economic cost of predicting the severity of the disease. In most cases, prostate tissue sequencing provides information to the attending physician, allowing the start of optimal treatment, considering the molecular or genetic «signature» of PCa. AI can be used as an alternative to existing population screening tools and a predictive castration-resistant PCa. The use of AI capabilities is more appropriate for blood and urine analysis, procedures that do not require additional economic costs for biomaterial sampling. In theory, this may be more affordable for the patient and the medical institution. It is worth noting that a few studies were conducted in silico (based on the analysis of molecular genetic databases without validation on cell lines or on real patients) and are useful as background information. However, the results can serve as a robust basis for further research in molecular diagnostics and genomics.Conclusion. It is possible to use AI in the search for key metabolites and genes of the elements of signalling pathways, as well as the determination of metastasis potential, because molecular or genetic «signature» of PCa allows the physician to start optimal treatment.

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Identification of crucial genes and pathways associated with prostate cancer in multiple databases

Cited by 8 publications

References 59 publications

Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis

Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis

Single-cell RNA sequencing combined with mendelian randomization analysis identifies putatively causal genes associated with Gastric cancer

Artificial intelligence in molecular and genomic prostate cancer diagnostics

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