Dilraj Kaur1, Chakit Arora1, Gajendra Pal Singh Raghava2. 1. Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India. 2. Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India. raghava@iiitd.ac.in.
Abstract
INTRODUCTION: Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs. OBJECTIVES: The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature. METHODS: This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes. RESULTS: Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86. CONCLUSION: We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.
INTRODUCTION: Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs. OBJECTIVES: The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature. METHODS: This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes. RESULTS: Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86. CONCLUSION: We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.
Authors: Veronica Wendy Setiawan; Hannah P Yang; Malcolm C Pike; Susan E McCann; Herbert Yu; Yong-Bing Xiang; Alicja Wolk; Nicolas Wentzensen; Noel S Weiss; Penelope M Webb; Piet A van den Brandt; Koen van de Vijver; Pamela J Thompson; Brian L Strom; Amanda B Spurdle; Robert A Soslow; Xiao-ou Shu; Catherine Schairer; Carlotta Sacerdote; Thomas E Rohan; Kim Robien; Harvey A Risch; Fulvio Ricceri; Timothy R Rebbeck; Radhai Rastogi; Jennifer Prescott; Silvia Polidoro; Yikyung Park; Sara H Olson; Kirsten B Moysich; Anthony B Miller; Marjorie L McCullough; Rayna K Matsuno; Anthony M Magliocco; Galina Lurie; Lingeng Lu; Jolanta Lissowska; Xiaolin Liang; James V Lacey; Laurence N Kolonel; Brian E Henderson; Susan E Hankinson; Niclas Håkansson; Marc T Goodman; Mia M Gaudet; Montserrat Garcia-Closas; Christine M Friedenreich; Jo L Freudenheim; Jennifer Doherty; Immaculata De Vivo; Kerry S Courneya; Linda S Cook; Chu Chen; James R Cerhan; Hui Cai; Louise A Brinton; Leslie Bernstein; Kristin E Anderson; Hoda Anton-Culver; Leo J Schouten; Pamela L Horn-Ross Journal: J Clin Oncol Date: 2013-06-03 Impact factor: 44.544
Authors: Leszek Gottwald; Piotr Pluta; Janusz Piekarski; Michał Spych; Katarzyna Hendzel; Katarzyna Topczewska-Tylinska; Dariusz Nejc; Robert Bibik; Jerzy Korczyński; Aleksandra Ciałkowska-Rysz Journal: Arch Med Sci Date: 2010-12-29 Impact factor: 3.318
Authors: Kathleen A Cronin; Andrew J Lake; Susan Scott; Recinda L Sherman; Anne-Michelle Noone; Nadia Howlader; S Jane Henley; Robert N Anderson; Albert U Firth; Jiemin Ma; Betsy A Kohler; Ahmedin Jemal Journal: Cancer Date: 2018-05-22 Impact factor: 6.860