Dataset Information

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

ABSTRACT: Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges.Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison.A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome.Stratification of cases based on clinical data, careful selection of two groups for comparison, and the application of logistic regression analysis substantially improved predictive accuracy in comparison to conventional KM approaches. B cell-related genes dominated the list of prognostic genes in early stage SQCC of the lung and triple negative breast cancer.

SUBMITTER: Mount DW

PROVIDER: S-EPMC4110620 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

Mount David W DW Putnam Charles W CW Centouri Sara M SM Manziello Ann M AM Pandey Ritu R Garland Linda L LL Martinez Jesse D JD

BMC medical genomics 20140610

<h4>Background</h4>Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions p ...[more]

PMID: 24916928

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Project description:To examine the clinical value of miR-198-5p in lung squamous cell carcinoma (LUSC).Gene Expression Omnibus (GEO) microarray datasets were used to explore the miR-198-5p expression and its diagnostic value in LUSC. Real-time reverse transcription quantitative polymerase chain reaction was used to evaluate the expression of miR-198-5p in 23 formalin-fixed, paraffin-embedded (FFPE) LUSC tissues and corresponding non-cancerous tissues. The correlation between miR-198-5p expression and clinic pathological features was assessed. Meanwhile, putative target messenger RNAs of miR-198-5p were identified based on the analysis of differentially expressed genes in the Cancer Genome Atlas (TCGA) and 12 miRNA prediction tools. Subsequently, the putative target genes were sent to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses.MiR-198-5p was low expressed in LUSC tissues. The combined standard mean difference (SMD) values of miR-198-5p expression based on GEO datasets were -?0.30 (95% confidence interval (CI) -?0.54, -?0.06) and -?0.39 (95% CI -?0.83, 0.05) using fixed effect model and random effect model, respectively. The sensitivity and specificity were not sufficiently high, as the area under the curve (AUC) was 0.7749 (Q*?=?0.7143) based on summarized receiver operating characteristic (SROC) curves constructed using GEO datasets. Based on the in-house RT-qPCR, miR-198-5p expression was 4.3826?±?1.7660 in LUSC tissues and 4.4522?±?1.8263 in adjacent normal tissues (P?=?0.885). The expression of miR-198-5p was significantly higher in patients with early TNM stages (I-II) than that in cases with advanced TNM stages (III-IV) (5.4400?±?1.5277 vs 3.5690?±?1.5228, P?=?0.008). Continuous variable-based meta-analysis of GEO and PCR data displayed the SMD values of -?0.26 (95% CI -?0.48, -?0.04) and -?0.34 (95% CI -?0.71, 0.04) based on fixed and random effect models, respectively. As for the diagnostic value of miR-198-5p, the AUC based on the SROC curve using GEO and PCR data was 0.7351 (Q*?=?0.6812). In total, 542 genes were identified as the targets of miR-198-5p. The most enriched Gene Ontology terms were epidermis development among biological processes, cell junction among cellular components, and protein dimerization activity among molecule functions. The pathway of non-small cell lung cancer was the most significant pathway identified using Kyoto Encyclopedia of Genes and Genomes analysis.The expression of miR-198-5p is related to the TNM stage. Thus, miR-198-5p might play an important role via its target genes in LUSC.

Project description:BACKGROUND:HPV-16-positive HNSCC and HPV-16-negative HNSCC have different clinical factors, representing distinct forms of cancers. The study aimed to identify patient-specific factors for HPV-16-positive HNSCC based on baseline clinical data. METHOD:Factors associated with HPV-16-positive HNSCC were identified using the data from 210 patients diagnosed with HNSCC at University College of London Hospital between January 1, 2003, and April 30, 2015, inclusive. A series of models were developed using logistic regression methods, and the overall model fit was compared using Akaike Information Criterion. Survival analysis was carried with Cox proportional hazards model for survival-time outcomes. The survival time for individual patients was defined as the time from diagnosis of HNSCC to the date of death from any cause. For patients who did not die, they were censored at the end of study on April 30, 2015. RESULTS:Of the 210 patients, 151 (72%) were found to have HPV-16-positive HNSCC. The logistic regression model showed that the prevalence of developing HPV-16-positive HNSCC was 3.79 times higher in patients with Type 2 Diabetes Mellitus (T2DM) (odd ratio [OR], 3.79; 95% CI, 1.70-8.44) than in those without T2DM, and 8.84 times higher in patients with history of primary HNSCC (OR, 8.84; 95% CI, 2.30-33.88) than in those without a history of primary HNSCC. HPV-16-positive HNSCC was also observed more in tonsils (OR, 4.02; 95% CL, 1.56-10.36) and less in non-alcohol drinker's oral cavity (OR, 0.14; 95% CI, 0.03-0.56). Furthermore, individual patients were followed-up for 1 to 13 years (median of 1 year). Patients with HPV-positive HNSCC had a median survival of 5 years (95% CI, 2.6-7.3 years). Among HPV-16-positive HNSCC cohort, T2DM was a risk for poorer prognosis (hazard ratio, 2.57; 95% Cl, 1.09-6.07), and had lower median survival of 3 years (95% CI, 1.8-4.1 years), as compared to 6 years (95% CI, 2.8-9.1 years) in non-T2DM. CONCLUSIONS:Patient-specific factors for HPV-positive HNSCC are T2DM, history of primary HNSCC and tonsillar site. T2DM is associated with poorer prognosis. These findings suggest that it might be beneficial if routine HPV-16 screening is carried out in T2DM patients which can provide better therapeutic and management strategies.

Dataset Information

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

Publications

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

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