Metagenomics studies for the diagnosis and treatment of prostate cancer

Zarei, Alí; Javid, Hossein; Sanjarian, Sara; Senemar, Sara; Zarei, Hanieh

doi:10.1002/pros.24276

Cited by 6 publications

(3 citation statements)

References 129 publications

(232 reference statements)

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“…The PCa level is closely related to age ( 22 ), levels of androgens ( 23 ), genetics ( 24 ) and obesity ( 25 ). Increasing evidence suggests obesity to be associated with an increased incidence of aggressive PCa ( 9 , 10 , 26 - 30 ) as well as an increased risk of biochemical failure following radical prostatectomy ( 31 ) and external-beam radiotherapy ( 32 ), Such results may be associated with a higher prevalence of complications following androgen-deprivation therapy, increased PCa-specific mortality, and difficulties in the treatment of obese men.…”

Section: Discussionmentioning

confidence: 99%

Multivariate logistic regression analysis of the clinical factors influencing locally advanced prostate cancer

Ke,

Xu,

Shu

et al. 2024

Transl Cancer Res

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Background Locally advanced prostate cancer (PCa) carries a high risk of recurrence and metastasis after surgery, and the prognosis is poor. We explored the risk factors for locally advanced PCa among clinical factors (neutrophil: lymphocyte ratio, lymphocyte: monocyte ratio) and indicators of systemic inflammation [prostate-specific antigen (PSA) level, Gleason score, body mass index (BMI)] through retrospective evaluation of patients with PCa diagnosed at our center. The pathologic T stage was a key indicator of locally advanced PCa. Methods Data from patients with pathologically confirmed PCa at our center from 1 January 2015 to 1 May 2020 were collected in strict accordance with inclusion and exclusion criteria. Clinical data were collected and the relationship between the indicators and the pathologic T stage was explored. First, Spearman rank correlation analysis was used to find the correlates of the pathologic T stage. Then, logistic ordered multiple regression analysis was used to identify independent risk factors. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracy for the T stage of PCa. Results After rigorous screening, the data of 177 patients were obtained. Spearman correlation analysis showed that BMI, the PSA level, Gleason score, hypertension, N stage, and M stage were significantly correlated with the T stage (P<0.05), suggesting that these factors may be involved in locally advanced PCa. Analyses of ROC curves showed that the PSA level [area under the ROC curve (AUC) =0.802] had greater value than BMI (0.675) for the diagnosis of the pathologic T stage PCa, and that a combination of BMI and PSA (combined AUC =0.822) could improve locally advanced PCa diagnosis. Conclusions BMI and PSA are independent risk factors for locally advanced PCa. They may play a key part in locally advanced PCa.

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Section: Discussionmentioning

confidence: 99%

Multivariate logistic regression analysis of the clinical factors influencing locally advanced prostate cancer

Ke,

Xu,

Shu

et al. 2024

Transl Cancer Res

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“…Currently, the most widely employed sequencing techniques include three next-generation sequencing (NGS) systems (i.e., Illumina, Ion Torrent, BGI) and two third-generation sequencing techniques (i.e., PacBio and Nanopore) [41]. These sequencing approaches are the basis for the development of a variety of genomic and transcriptomic research methodologies, including DNA sequencing [15], RNA sequencing [42], 16S rRNA sequencing [43], epigenetic techniques (e.g., ChIP-seq and DNA/RNA methylation sequencing) [44][45][46], and three-dimensional genomic technologies [47]. Moreover, recent research advances of gene editing tools have enabled multi-gene manipulation on a genome-wide level [48].…”

Section: Genomics and Transcriptomics For Tumor Microbiome Studiesmentioning

confidence: 99%

Applying multi‐omics toward tumor microbiome research

Zhang

Kandalai

Zhou

et al. 2023

iMeta

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Rather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies.However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.

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“…Prostate cancer (PCa) is the most common malignancy in men, and the rate of occurrence is significantly associated with age ( 1 ). In recent years, the number of new cases and the number of deaths from PCa have remained high, and the age of the population affected has been getting progressively younger ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

An anoikis-related gene signature for prediction of the prognosis in prostate cancer

Zhao,

Wang,

Tang

et al. 2023

Front. Oncol.

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PurposeThis study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs).MethodsIn this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa.ResultsBased on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer.ConclusionThis signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.

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Metagenomics studies for the diagnosis and treatment of prostate cancer

Cited by 6 publications

References 129 publications

Multivariate logistic regression analysis of the clinical factors influencing locally advanced prostate cancer

Multivariate logistic regression analysis of the clinical factors influencing locally advanced prostate cancer

Applying multi‐omics toward tumor microbiome research

An anoikis-related gene signature for prediction of the prognosis in prostate cancer

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