Browsing by Author "Atanu Bhattacharjee"

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Bayesian competing risk analysis: An application to nasopharyngeal carcinoma patients data
(John Wiley and Sons Inc, 2021) Rakesh Kumar Saroj; K. Narasimha Murthy; Mukesh Kumar; Atanu Bhattacharjee; Kamalesh Kumar Patel
Background: The Cox proportional hazard (CPH) model is normally used to study the death event data. The presence of competing risk (CR) is often encountered in health data, hence it becomes difficult to manage time to event data in clinical study. Bayesian approach is considered to manage the CR events in clinical data. Objectives: The objective of study is to find the predictors associated with overall survival of nasopharyngeal carcinoma (NPC) patients. Further, our purpose is to use a Bayesian model that can analyze time to event data in the presence of CR. Methods: Total 245 patients with NPC were taken (https://www.ncbi.nlm.nih.gov/geo/). The sociodemographic and clinical variables were considered for analysis purposes. R software and openBUGS were used to overcome the computational problems of CPH and Bayesian models. The Markov chain Monte Carlo (MCMC) method was used to compute the regression coefficients of Bayesian model. Results: The study shows that among NPC patients, the covariates chemotherapy, smoking, N-stage, and tumor site are associated with the higher risk for the deaths occurring in the cancer patients. The posterior mean estimates of proposed Bayesian model for significant factors have been obtained. The posterior mean and standard deviation estimates help to improve the survival of patients in the presence of CR. Conclusions: It is very difficult to use the CR model with Bayesian approach in health research for nonstatistical researcher due to lack of information. This paper is dedicated to the application of Bayesian approach for CR analysis on NPC data. © 2020 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.
Human papillomavirus elevated genetic biomarker signature by statistical algorithm
(Wiley-Liss Inc., 2020) Nimisha Tripathi; Sneha Keshari; Pallavi Shahi; Poonam Maurya; Atanu Bhattacharjee; Kushal Gupta; Sanjay Talole; Mukesh Kumar
Head and neck squamous cell carcinoma (HNSCC) is the one of the most frequently found cancers in the world. The aim of the study was to find the genes responsible and enriched pathways associated with HNSCC using bioinformatics and survival analysis methods. A total of 646 patients with HNSCC based on clinical information were considered for the study. HNSCC samples were grouped according to the parameters (RFS, DFS, PFS, or OS). The probe ID of these 11 genes was retrieved by Affymetrix using the NetAffx Query algorithm. The protein–protein interaction (PPI) network and Kaplan–Meier curve were used to find associations among the genes' expression data. We found that among these 11 genes, nine genes, CCNA1, MMP3, FLRT3, GJB6, ZFR2, PITX2, SYCP2, MEI1, and UGT8 were significant (p <.05). A survival plot was drawn between the p value and gene expression. This study helped us find the nine significant genes which play vital roles in HNSCC along with their key pathways and their interaction with other genes in the PPI network. Finally, we found the biomarker index for relapse time and risk factors for HNSCC in cancer patients. © 2020 Wiley Periodicals LLC
Identification of key genes and construction of regulatory network for the progression of cervical cancer
(Elsevier Inc., 2020) Monika Rajput; Mukesh Kumar; Mayuri Kumari; Atanu Bhattacharjee; Aanchal Anant Awasthi
Across the globe, cervical cancer is the fourth main cause of cancer-associated deaths in women. The present study aimed to identify the differentially expressed genes (DEGs) and enriched pathways involved in cervical cancer. From the database, Gene Expression Omnibus (GEO) microarray data of 300 patients with cervical cancer (GSE44001) was used for analysis purposes. Statistical analysis was performed to identify DEGs between different stages of cervical cancer and progression, and a total of 36 common DEGs were screened. The Gene ontology analysis (GO) and protein-protein interaction (PPI) network were used to find relationship among the DEGs. The gene-miRNA interaction networks were constructed by NetworkAnalyst software. The study revealed that various DEGs are involved in the process of oncogeneis. Genes like TP63, IGF1, AIM2, ABCB7, ARHGAP6, RAP2B, HIST1H3C, THOC2, TRIM66, PKN3, CNBP & ATG3 may play a crucial role in cervical cancer. DEGs like THCO2, TAF5L, GPS1, PKN3 & VPSI3A are upregulated. The down regulated DEGs are KHL13, ST8IA4, RAP2B, FRMD8, EGL6, KLHDC10, TRIM66 & CNBP. Based on the investigation, miRNAs and associated DEGs were analyzed, which in turn helped us in a better understanding of the prognosis of cervical cancer. Comprehensively our results revealed the potential use of biomarkers in the diagnosis of cervical cancer and may uplift the development of advanced cervical cancer therapy and treatment of cancer. © 2020
Parametric survival analysis using R: Illustration with lung cancer data
(Blackwell Publishing Ltd, 2020) Mukesh Kumar; Prashant Kr. Sonker; Agni Saroj; Aanchal Jain; Atanu Bhattacharjee; Rakesh Kr. Saroj
Background: Cox regression is the most widely used survival model in oncology. Parametric survival models are an alternative of Cox regression model. In this study, we have illustrated the application of semiparametric model and various parametric (Weibull, exponential, log-normal, and log-logistic) models in lung cancer data by using R software. Aims: The aim of the study is to illustrate responsible factors in lung cancer and compared with Cox regression and parametric models. Methods: A total of 66 lung cancer patients of African Americans (AAs) (data available online at http://clincancerres.aacrjournals.org) was used. To identify predictors of overall survival, stage of patient, sex, age, smoking, and tumor grade were taken into account. Both parametric and semiparametric models were fitted. Performance of parametric models was compared by Akaike information criterion (AIC). “Survival” package in R software was used to perform the analysis. Posterior density was obtained for different parameters through Bayesian approach using WinBUGS. Results: The illustration about model fitting problem was documented. Parametric models were fitted only for stage after controlling for age. AIC value was minimum (462.4087) for log-logistic model as compared with other parametric models. Log-logistic model was the best fit for AAs lung cancer data under study. Conclusion: Exploring parametric survival models in daily practice of cancer research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using freely available R software with illustration. It is expected that this present work can be useful to apply parametric survival models. © 2019 Wiley Periodicals, Inc.
When COVID-19 will decline in India? Prediction by combination of recovery and case load rate
(Elsevier B.V., 2021) Atanu Bhattacharjee; Mukesh Kumar; Kamalesh Kumar Patel
Background: The World Health Organization (WHO) declared COVID-19 as a pandemic on March 11, 2020. There is sudden need of statistical modeling due to onset of COVID-19 pandemic across the world. But health planning and policy requirements need the estimates of disease problem from clinical data. Objective: The present study aimed to predict the declination of COVID-19 using recovery rate and case load rate on basis of available data from India. Methods: The reported COVID-19 cases in the country were obtained from website (https://datahub.io/core/covid-19#resource-covid-19_zip/). The confirmed cases, recovered cases and deaths were used for estimating recovery rate, case load rate and death rate till June 04, 2020. Results: A total of 216919 confirmed cases were reported nationwide in India on June 04, 2020. It is found that the recovery rate increased to 47.99% and case load rate decreased to 49.21%. Death rate is found to be very low 2.80%. Accordingly, coincidence of the difference of case load rate and recovery rate (delta) will reveal a declination in expected COVID-19 cases. Conclusion: The epidemic in the country was mainly caused by the movement of people from various foreign countries to India. Lockdown as restricting the migration of population and decision taken by the government to quarantine the population may greatly reduce the risk of continued spread of the epidemic in India. This study predicts that when the case load rate gets lesser than recovery rate, there after COVID-19 patients would be started to decline. © 2020