Identifying indispensable proteins of the type III secretion systems of Salmonella enterica serovar Typhimurium strain LT2

Lahiri, Chandrajit; Shrikant, Pawar; Sabarinathan, Radhakrishnan; Ashraf, Md. Izhar; Chakravortty, Dipshikha

doi:10.1186/1471-2105-13-s12-a10

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…In this study, we contributed an RNA combination prognostic biomarker model for head and neck cancer patient risk stratification using the RNAs picked out from training dataset by a significant correlation with survival time. The evaluation by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve [15][16][17][18][19][20][21][22][23] showed that our prognostic model presented very good property in most of the situations. Armed with this model, we can recognize the high-risk patients immediately after the operative treatment accurately and combination of this model with current treatment measures is expected to greatly improve the patients' prognosis.…”

Section: Discussionmentioning

confidence: 98%

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

Pawar

Stanam

2019

J. Maxillofac. Oral Surg.

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Background and Objective Head and neck cancer is a malignant tumor that begins in the head and neck region, and has the sixth highest incidence worldwide. Previous studies have indicated several prognostic markers for head and neck squamous cell carcinoma (HNSCC), but due to poor accuracy and sensitivity of these clinical characteristic markers attention has been gradually switched to molecular biomarkers. This study aimed to sort out the mRNAs correlated with patient survival time to establish an mRNA combination prognostic biomarker model for HNSCC patient risk stratification, providing optimal therapeutic regimens and improving patient prognosis. Methods Clinical data and transcriptome sequencing data of HNSCC were retrieved from TCGA database and were allocated into training and validation datasets. The prognostic model was established using the mRNAs, which were sorted out from training dataset by a significant correlation with survival time. Eventually, the prediction property of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve. Results An optimal prognostic model by the combination of six mRNAs was established. Kaplan-Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was [ 0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated. Conclusion Our model provided effective prognostication in terms of death risk stratification and evaluation in HNSCC patients. Combination of this prognostic model with current treatment measures is expected to greatly improve the patients' prognosis. Keywords Biomarkers Á Survival time Á Cox regression Á Kaplan-Meier survival analysis Shrikant Pawar and Aditya Stanam have contributed equally to this work.

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Section: Discussionmentioning

confidence: 98%

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

Pawar

Stanam

2019

J. Maxillofac. Oral Surg.

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“…MA plots [8] for showing differentially expressed genes were calculated as follows: M = Logarithm to base 2 (Treatment/Control), A = 1/2 × Logarithm to base2 (Treatment × Control). MA plots were made on R platform with ‘plotMA’ limma Bioconductor package [9] , [10] , [11] , [12] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Transcriptomic data for analyzing global gene expression patterns in Methicillin-resistance Staphylococcus aureus in response to spermine and oxacillin stress

Pawar

Yao

2018

Data in Brief

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Methicillin-resistant Staphylococcus aureus (MRSA) is a rapidly emerging bacteria causing infection, which has developed resistance to most of the beta-lactam antibiotics because of newly acquired low-affinity penicillin binding protein (PBP2a), which can continue to build the cell wall when other PBPs are blocked by beta-lactams. Exogenous spermine exerts a dose dependent inhibition effect on the growth of E. coli, Salmonella enterica serovar and Staphylococcus aureus. We have selected an MRSA Mu50 derivative which harbors mutation on PBP2 gene (named as MuM) showing spermine resistance and which confers a complete abolishment of spermine-beta-lactam synergy. A transcriptomic profiling of MuM against Mu50 (wild type) without any treatment, MuM and Mu50 in response to high dose spermine and Mu50 in response to spermine-beta-lactam synergy is provided in this article. These comparisons will enhance our current understanding of mechanisms of spermine-beta-lactam synergy sensitization effects on MRSA.

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Web-Based Application for Accurately Classifying Cancer Type from Microarray Gene Expression Data Using a Support Vector Machine (SVM) Learning Algorithm

Pawar

2019

Bioinformatics and Biomedical Engineering

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Identifying indispensable proteins of the type III secretion systems of Salmonella enterica serovar Typhimurium strain LT2

Cited by 6 publications

References 2 publications

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

Transcriptomic data for analyzing global gene expression patterns in Methicillin-resistance Staphylococcus aureus in response to spermine and oxacillin stress

Web-Based Application for Accurately Classifying Cancer Type from Microarray Gene Expression Data Using a Support Vector Machine (SVM) Learning Algorithm

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