ALDH3A2 , ODF2 , QSOX2 , and MicroRNA-503-5p Expression to Forecast Recurrence in TMPRSS2-ERG -Positive Prostate Cancer.
Anastasiya A KobelyatskayaAlexander A KudryavtsevAnna V KudryavtsevaAnastasiya V SnezhkinaMaria S FedorovaDmitry V KalininVladislav S PavlovZulfiya G GuvatovaPavel A NaberezhnevKirill M NyushkoBoris Y AlekseevGeorge S KrasnovElizaveta V BulavkinaElena A PudovaPublished in: International journal of molecular sciences (2022)
Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model's use of identified differentially expressed genes and miRNAs, miRNA-target pairs were found that correlate with the prognosis and can be presented as an interactome network.
Keyphrases
- prostate cancer
- locally advanced
- end stage renal disease
- squamous cell carcinoma
- ejection fraction
- newly diagnosed
- chronic kidney disease
- neural network
- free survival
- rectal cancer
- prognostic factors
- minimally invasive
- neoadjuvant chemotherapy
- radiation therapy
- poor prognosis
- bone marrow
- gene expression
- dna methylation
- genome wide
- patient reported outcomes
- artificial intelligence
- binding protein
- lymph node
- coronary artery disease
- coronary artery bypass
- data analysis
- network analysis
- bioinformatics analysis
- genome wide identification