Statistical analysis of microarray gene expression data from a mouse model of toxoplasmosis

Shrikant, Pawar; Davis, Courtland H.; Rinehart, Claire A.

doi:10.1186/1471-2105-12-s7-a19

Cited by 5 publications

(3 citation statements)

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“…In this methodology, the regular utilitarian settings are separated through the investigation of both transcriptomic and proteomic datasets fair and square of protein communication organizations. This methodology was distributed in 2010 by Paul et al [56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] which is examined in segment 5.2. Creators of this distribution likewise produced omicsNET for discovering reliance between highlights of proteomics and transcriptomics.…”

Section: Methods and Resultsmentioning

confidence: 99%

<sup></sup>Techniques of Transcriptomic and Proteomic Data Integration

Patil¹,

Len²,

Bharat³

et al. 2020

Preprint

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As of not long ago, understanding the administrative conduct of cells has been sought after through autonomous investigation of the transcriptome or the proteome. In view of the focal creed, it was commonly accepted that there exist an immediate correspondence between mRNA records and produced protein articulations. In any case, late examinations have demonstrated that the relationship among's mRNA and Protein articulations can be low because of different factors, for example, unique half lives and post record apparatus. In this manner, a joint investigation of the transcriptomic and proteomic information can give valuable experiences that may not be translated from singular examination of mRNA or protein articulations. This article audits the current significant methodologies for joint investigation of transcriptomic and proteomic information. We order the various methodologies into eight primary classes dependent on the underlying calculation and last investigation objective. We further present analogies with different spaces and talk about the current exploration issues around there.

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Section: Methods and Resultsmentioning

confidence: 99%

<sup></sup>Techniques of Transcriptomic and Proteomic Data Integration

Patil¹,

Len²,

Bharat³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…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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“…Microarray analysisMas 5.0 normalization was performed for all the files at 0-min time point for Mu50 and MuM strains and strains with MuM–PBP2 and Mu50–PBP2 complementation plasmid [3] . For calculating upregulated genes, in control of all the P (present) call intensity values were considered for analysis and all the M (marginal) and A (absent) calls were regarded as 100.…”

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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Statistical analysis of microarray gene expression data from a mouse model of toxoplasmosis

Cited by 5 publications

References 0 publications

<sup></sup>Techniques of Transcriptomic and Proteomic Data Integration

<sup></sup>Techniques of Transcriptomic and Proteomic Data Integration

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

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