scholarly journals Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning

Cells ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 287
Author(s):  
Khaled Bin Satter ◽  
Paul Minh Huy Tran ◽  
Lynn Kim Hoang Tran ◽  
Zach Ramsey ◽  
Katheine Pinkerton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Analysis ◽  
Roc Analysis ◽  
Gene Signature ◽  
Beta Catenin ◽  
Related Gene ◽  
Highly Active ◽  
Multiple Datasets ◽  
Discovery Dataset

Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.

Development and validation of an unfolded protein response-related gene signature to predict overall survival in HNSCC.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18033-e18033
Author(s):  
Jun Chen ◽  
Bei Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Unfolded Protein Response ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Related Gene ◽  
Hallmarks Of Cancer ◽  
Unfolded Protein ◽  
Protein Response

e18033 Background: Genomic expression profiles have enabled the classification of head and neck squamous cell carcinoma (HNSCC) into molecular sub-types and provide prognostic information, which have implications for the personalized treatment of HNSCC beyond clinical and pathological features. Methods: Gene-expression profiling was identified in TCGA- HNSCC (n = 492) and validated with the Gene Expression Ominibus (GEO) dataset(n = 270) for which RNA sequencing data and clinical covariates were available. A single-sample gene set enrichment analysis (ssGSEA) algorithm were used to quantified the levels of various hallmarks of cancer. And LASSO Cox regression model was used to screen robust prognostic biomarkers to identify the best set of survival-associated gene signatures in HNSCC. Statistical analyses were performed using R version 3.4.4. Results: We identified unfolded protein response as the primary risk factor for survival（cox coefficient = 17.4 [8.4-26.3], P < 0.001）among various hallmarks of cancer in TCGA- HNSCC. And unfolded protein response ssGESA scores were significantly elevated in patients who died during follow up (P = 0.009). Kaplan-Meier analysis showed that patients with low ssGSEA scores of unfolded protein response exhibited better OS (HR = 0.69, P = 0.008). And we established an unfolded protein response-related gene signature based on lasso cox. We then apply the unfolded protein response -related gene signature to classify patients into the high risk group and the low risk group with the cutoff of 0.18. Adjusted for stage，age，gender, our signature was an independent risk factor for overall survival in TCGA cohorts (HR = 0.39 [0.28-0.53],P = < 0.001). In external independent cohorts, similar results were observed. In the validation cohort GEO65858, the patients with high unfolded protein response score showed longer survival (HR = 0.62 [0.38-1.0], P = 0.049). And adjusted for stage，age，HPV state, the multivariate cox regression analysis showed that unfolded protein response-related gene signature exhibited an independent risk prediction for overall survival in 270 patients with HNSCC (HR = 0.57 [0.35-0.94], P = 0.026). Conclusions: By analyzing the gene-expression data with bioinformation approach, we developed and validated a risk prediction model with unfolded protein response -related expression scores in HNSCC, which have the potential to identify patients who could have better overall survival.

CIP elicitors on the defense response of A. macrocephala and its related gene expression analysis

2020 ◽  
Vol 245 ◽  
pp. 153107
Author(s):  
Peifeng Zhang ◽  
Fang Zheng ◽  
Lei Chen ◽  
Xiaofang Lu ◽  
Wei Tian
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Defense Response ◽  
Gene Expression Analysis ◽  
Related Gene

CrossICC: iterative consensus clustering of cross-platform gene expression data without adjusting batch effect

2019 ◽  
Vol 21 (5) ◽  
pp. 1818-1824 ◽  
Author(s):  
Qi Zhao ◽  
Yu Sun ◽  
Zekun Liu ◽  
Hongwan Zhang ◽  
Xingyang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Signature ◽  
Unsupervised Clustering ◽  
Batch Effect ◽  
Consensus Clustering ◽  
Expression Data ◽  
Personalized Care ◽  
Cancer Subtypes ◽  
Multiple Datasets

Abstract   Unsupervised clustering of high-throughput gene expression data is widely adopted for cancer subtyping. However, cancer subtypes derived from a single dataset are usually not applicable across multiple datasets from different platforms. Merging different datasets is necessary to determine accurate and applicable cancer subtypes but is still embarrassing due to the batch effect. CrossICC is an R package designed for the unsupervised clustering of gene expression data from multiple datasets/platforms without the requirement of batch effect adjustment. CrossICC utilizes an iterative strategy to derive the optimal gene signature and cluster numbers from a consensus similarity matrix generated by consensus clustering. This package also provides abundant functions to visualize the identified subtypes and evaluate subtyping performance. We expected that CrossICC could be used to discover the robust cancer subtypes with significant translational implications in personalized care for cancer patients. Availability and Implementation The package is implemented in R and available at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL v3 License.

scholarly journals A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection

Neurosurgery ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 202-210 ◽  
Author(s):  
William C Chen ◽  
Harish N Vasudevan ◽  
Abrar Choudhury ◽  
Melike Pekmezci ◽  
Calixto-Hope G Lucas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Gene Expression Analysis ◽  
Validation Cohort ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Who Grade ◽  
Independent Validation ◽  
Meningioma Recurrence ◽  
Targeted Gene Expression

Abstract BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence. RESULTS We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P &lt; .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P &lt; .0001), and overall survival (OS, P &lt; .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis. CONCLUSION The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.

Identification of tumor biomarkers for sunitinib in advanced renal cell carcinoma (RCC).

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 470-470
Author(s):  
Hongyue Dai ◽  
Mayer N. Fishman ◽  
Keith A. Ching ◽  
James Andrew Williams ◽  
Jamie K. Teer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Treatment Duration ◽  
Gene Expression Analysis ◽  
Pearson Correlation ◽  
Standard Of Care ◽  
Gene Signature ◽  
Resistance Mechanisms ◽  
Potential Association ◽  
Total Cancer

470 Background: Sunitinib is a standard of care for advanced RCC. Despite efforts to identify predictive molecular markers for patient selection, none are available, likely due to multiple resistance mechanisms. Using the Total Cancer Care (TCC) database, which integrates patient clinical, molecular, and biospecimen data, we devised a tumor genomics and transcriptomics experiment to identify differences between RCC patients who derive prolonged clinical benefit from sunitinib versus those who are resistant. Methods: A discovery set of 34 RCC patients treated with sunitinib at the approved regimen were identified in the TCC database (n=16 treated for ≤6 months, having primarily discontinued for reasons other than tolerability; n=18 treated for ≥18 months). Tumor samples were analyzed by whole exome sequencing (WES) and by parallel 400-gene expression profiling. Following gene mutation identification and supervised gene expression analysis, molecular differences between the two groups were identified and tested for potential association with treatment duration. Results: Of the 34 cases identified, 24 remained for analysis following sample QC failure and clinical review (n=10 and 14 treated for ≤6 and ≥18 months, respectively). Gene expression analysis revealed a 37-gene signature associated with treatment duration: MAPK8 (JNK1) was a leading candidate biomarker (Pearson correlation with log [treatment duration]=–0.70; p=0.06 after Bonferroni multiplicity correction). Pathway-based WES analyses identified 25 potential variants of interest, none remaining statistically significant after correction. However, following genome-wide analysis, a single variant in an intronic region of ING3 was statistically associated with treatment duration (p=0.02). Conclusions: Activation of the PI3K/AKT pathway was a marker of resistance to sunitinib. In contrast, activation of the angiogenic, NOTCH, or JAK-STAT pathways was, to some degree, associated with sensitivity to therapy. However, neither VHL alteration nor lack of expression, nor alteration in chromatin-rearrangement genes, was associated with sunitinib treatment duration. These findings require further validation in a larger and independent cohort.

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