I’m looking for a research paper for my thesis on prostate gene expression. R is

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Feb 26, 2023


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I’m looking for a research paper for my thesis on prostate gene expression. R is used for all statistical analyses.
Title: Biological age genes for prostate cancer
The specific objective of predictive modeling in therapeutic response for prostate cancer is to identify age-related gene biomarkers that are associated with the Gleason score and predict the time to biochemical recurrence (BCR).
Sources used:
Two data sources were used for training and testing. The first data source, used for training, was obtained from the Memorial Sloan Kettering Cancer Center (MSKCC) cBioPortal for Cancer Genomics and can be found at https://www.cbioportal.org/study/clinicalData?id=prad_mskcc. This dataset included clinical and molecular patient characteristics, as well as information on the Gleason score and time to biochemical recurrence (BCR).
The second data source, used for testing, was obtained from The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) dataset, which can be found at https://www.cbioportal.org/study/summary?id=prad_tcga. This dataset provided additional patient information, including gene expression data, and was used to validate the results obtained from the training dataset.
Please use this paper as a reference. I need to align my abstract similar to this paper:
Methods used:
Machine learning can be used to predict the therapeutic response of prostate cancer patients by identifying gene biomarkers that can affect the aggressiveness of the tumor. The aim is to train the machine learning model using a dataset that includes clinical and molecular patient characteristics and therapeutic response data, including Gleason score and time to BCR.
In this study, the MSKCC Taylor et al. dataset was used as the training dataset, and the TCGA dataset was used for testing. The dataset includes clinical data such as age, gender, race, and initial disease aggressiveness, as well as molecular data such as gene expression. Using individual Cox analysis, significant genes were identified and non-significant genes were eliminated.
To further validate the results and assess the interactions between the genes, a heat map was generated. Additionally, Lasso was run on both test and train data, as well as on the stratified data based on Gleason scores. The stratified analysis allowed for a more comprehensive understanding of the impact of the gene biomarkers on disease severity and treatment response in prostate cancer patients.
I’ll share the R code used for the analysis. The data is open source and can be downloaded from the links mentioned above.
Comments from Customer
I need expert in R and gene expression data. They should have statistical knowledge in survival analysis.
Another paper for reference.
https://wetransfer.com/downloads/e7b39125d4f846ac22f2d762a0e804ce20230225013106/12639ec06911b1ac98b9cc572ae40c8820230225013106/2a95ed – link to the needed data


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