Biology subjects

Koca, D.

Publications and source records attributed to Koca, D..

2 recordsLinked to original sources

bioRxiv2023

COL7A1 expression improves prognosis prediction for patients with clear cell renal cell carcinoma atop of stage

Clear cell renal cell carcinoma (ccRCC) accounts for 75% of kidney cancers. Due to the high recurrence rate, and treatment options that come with high costs and potential side effects correct prognosis of patient survival is essential for the successful and effective treatment of patients. Novel biomarkers could play an important role in the assessment of the overall survival of patients. COL7A1 encodes for collagen type VII, a constituent of the basal membrane. COL7A1 is associated with survival in many cancers; however, the prognostic value of COL7A1 expression as a standalone biomarker in ccRCC has not been investigated. We used Kaplan-Meier curves and Cox proportional hazards model to investigate the prognostic value of COL7A1, as well as Gene Set Enrichment Analysis to investigate genes that are co-expressed with COL7A1. COL7A1 expression was used to stratify patients into four groups of expression, where the 5-year survival probability of each group was 72.4%, 59.1%, 34.15%, and 8.6% in order of increasing expression. Additionally, COL7A1 expression was successfully used to further divide patients of each stage and histological grade into groups of high and low risk. Similar results were obtained in independent cohorts. In-vitro knockdown of COL7A1 expression significantly impacted ccRCC cells ability to migrate and proliferate. To conclude, we identified COL7A1 as a new prognosis marker that can stratify ccRCC patients.

cancer biology↗

bioRxiv2022

Optimal microRNA sequencing depth to predict cancer patient survival with random forest and Cox models

(1) Backgroundtumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Resultswe first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups. (3) AvailabilityR script is available at https://github.com/remyJardillier/Survival_seq_depth

bioinformatics↗