Authors

  • M.S.Salomov
    Department of Otorhinolaryngology, Ophthalmology, Oncology and Medical Radiology Termez branch of the Tashkent Medical Academy

DOI:

https://doi.org/10.71337/inlibrary.uz.universal-scientific-research.113777

Abstract

 Clinical grading systems that use clinical characteristics, as well as nomograms, do not have the accuracy to guide treatment decisions for prostate cancer (prostate cancer). There is a critical need to identify biomarkers that can more accurately stratify men with primary prostate cancer


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17

Stromal gene expression predictive of metastatic primary prostate cancer

M.S.Salomov

Department of Otorhinolaryngology, Ophthalmology, Oncology and Medical

Radiology Termez branch of the Tashkent Medical Academy

Relevance.

Clinical grading systems that use clinical characteristics, as well as nomograms, do not

have the accuracy to guide treatment decisions for prostate cancer (prostate cancer). There is a critical
need to identify biomarkers that can more accurately stratify men with primary prostate cancer.

The purpose of the study.

To identify an effective prognostic signature that can better distinguish

between indolent and aggressive prostate cancer (prostate cancer).

Design, conditions and participants of the study.

To develop the signature, whole genome and

whole transcriptome were sequenced on five prostate cancer models with xenographs from patients
(PG) obtained from independent foci of a single primary tumor and demonstrating different metastatic
phenotypes. Several independent clinical cohorts, including the intermediate risk cohort, were used
to validate biomarkers.

Determination of results and statistical analysis

Metastasis after radical prostatectomy was the

outcome that determined aggressive prostate cancer. A general linear model with lasso regularization
was used to construct a 93-gene stromal metastasis signature (SDMS). The association between
SDMS and metastasis was assessed using the Wilcoxon rank sum test. Efficiency was evaluated using
the area under the curve (AUC) for receiver performance and Kaplan-Meyer curves. Univariable and
multivariable regression models were used to compare SDMS with clinical histological variables and
other signatures. AUC was evaluated to determine whether SDMS is additive or synergistic with
respect to other previously defined signatures.

Results and limitations

There was a close association

between stromal gene expression and metastatic phenotype. Accordingly, SDMS was modeled and
validated in several independent clinical cohorts. Patients with high SDMS scores, as it turned out,
had a worse prognosis. Moreover, SDMS was an independent predictive factor, was able to stratify
the risk of intermediate-risk prostate cancer, and can improve the effectiveness of other previously
presented signatures.

Conclusion

Profiling of stromal gene expression led to the development of

SDMS, which has been validated as an independent prognostic factor for the metastatic potential of
prostate tumors