Identifying statistically significant combinatorial markers for survival analysis

Relator, Raissa; Terada, Aika; Sese, Jun

doi:10.1186/s12920-018-0346-x

Cited by 13 publications

(12 citation statements)

References 35 publications

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“… 51 , 52 Expression of C1ORF55 has been associated with prognosis of breast cancer patients. 53 Although we are unaware of studies suggesting a link between C1ORF55 and sepsis, the results of our analysis suggest that it may be beneficial for the early diagnosis of sepsis. Dual specificity phosphatase 5 (DUSP5) is induced during LPS-mediated inflammation and inhibits the activity of NF-κ B.…”

Section: Discussionmentioning

confidence: 82%

Identification of Potential Early Diagnostic Biomarkers of Sepsis

Huang

Wen-feng

et al. 2021

JIR

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Objective The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. Methods We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples. Results A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells. Conclusion In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.

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Section: Discussionmentioning

confidence: 82%

Identification of Potential Early Diagnostic Biomarkers of Sepsis

Huang

Wen-feng

et al. 2021

JIR

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“…Because of the complexity and the heterogeneity of colorectal cancer, multiple biomarkers are needed for the necessary prognostic power to accurately subtype this disease. However, discovery of novel biomarkers with high‐order combinatorial interactions is extremely challenging due to the combinatorial explosion (the size of genomic/proteomic data and arbitrariness of combinations) and constraints of multiple hypothesis testing for significance evaluation [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of a prognostic signature in colorectal cancer using combinatorial algorithm‐driven analysis

Alnabulsi

Wang

Pang

et al. 2022

The Journal of Pathology CR

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Colorectal carcinoma is one of the most common types of malignancy and a leading cause of cancer-related death. Although clinicopathological parameters provide invaluable prognostic information, the accuracy of prognosis can be improved by using molecular biomarker signatures. Using a large dataset of immunohistochemistrybased biomarkers (n = 66), this study has developed an effective methodology for identifying optimal biomarker combinations as a prognostic tool. Biomarkers were screened and assigned to related subsets before being analysed using an iterative algorithm customised for evaluating combinatorial interactions between biomarkers based on their combined statistical power. A signature consisting of six biomarkers was identified as the best combination in terms of prognostic power. The combination of biomarkers (STAT1, UCP1, p-cofilin, LIMK2, FOXP3, and ICOS) was significantly associated with overall survival when computed as a linear variable (χ 2 = 53.183, p < 0.001) and as a cluster variable (χ 2 = 67.625, p < 0.001). This signature was also significantly independent of age, extramural vascular invasion, tumour stage, and lymph node metastasis (Wald = 32.898, p < 0.001). Assessment of the results in an external cohort showed that the signature was significantly associated with prognosis (χ 2 = 14.217, p = 0.007). This study developed and optimised an innovative discovery approach which could be adapted for the discovery of biomarkers and molecular interactions in a range of biological and clinical studies. Furthermore, this study identified a protein signature that can be utilised as an independent prognostic method and for potential therapeutic interventions.

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“…The resulting patterns, called closed patterns, are then tested for association with breast cancer risk. Closed-pattern mining and related techniques that avoid the task of exhaustive enumeration have been used previously to detect genetic epistasis (17)(18)(19), but not to discover risk haplotypes composed of SNPs underlying GWAS hits. We applied Chromosome Overlap to phased genotype data in the UK Biobank (UKBB) (20) to discover risk haplotypes of large effect composed of SNPs in the vicinity of three of the strongest GWAS hits (by p-value) in the GWAS Catalog (https://www.ebi.ac.uk/gwas/home) associated with breast cancer risk: rs2981578 on chromosome 10q26 in an intron of FGFR2 (21); rs554219 on chromosome 11q13 upstream of CCND1 (22); and rs4784227 on 16q12 in an intron of CASC16 (23).…”

Section: Introductionmentioning

confidence: 99%

Potential Misrepresentation of Inherited Breast Cancer Risk by Common Germline Alleles

Letsou

Wang

Moon

et al. 2022

Preprint

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Hundreds of common variants have been found to confer small but significant differences in breast cancer risk, supporting the polygenic additive model of inherited risk. This widely accepted model is at odds with twin data indicating highly elevated risk in a subgroup of women. Using a novel closed-pattern-mining algorithm, we provide evidence that rare variants or haplotypes may underlie the association of breast cancer risk with common germline alleles. Our method, called Chromosome Overlap, consists in iteratively pairing chromosomes from affected individuals and looking for noncontiguous patterns of shared alleles without exhaustive enumeration. We applied Chromosome Overlap to haplotypes of genotyped SNPs from 9,011 female breast cancer cases from the UK Biobank (UKBB) at three topologically associating domains containing well-established common-allele “hits” for breast cancer. A total of 181,034 UKBB women of “white British” ancestry were used to assess the discovered haplotypes, and 55,346 cases and controls of European ancestry in the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) case-control study were used for replication. Out of twenty rare (frequency < ∼0.1%) risk haplotypes of large effect identified in UKBB atP< 1.0 × 10−5, four (hazard ratio: 4.22–20.2) were subsequently replicated in DRIVE (odds ratio: 2.13–11.9) atP< 0.05. Our results support the genetic heterogeneity and rare-variant/haplotype basis of breast cancer risk and suggest a novel type of “synthetic association” wherein common risk alleles on a rare risk haplotype may misrepresent disease risk through their tagging of many “false positive” haplotypes.SignificanceChromosome Overlap reveals that common alleles identified by GWAS may be poor surrogates for underlying high-risk haplotypes, necessitating a reappraisal of the polygenic model of disease risk.

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Identifying statistically significant combinatorial markers for survival analysis

Cited by 13 publications

References 35 publications

Identification of Potential Early Diagnostic Biomarkers of Sepsis

Identification of Potential Early Diagnostic Biomarkers of Sepsis

Identification of a prognostic signature in colorectal cancer using combinatorial algorithm‐driven analysis

Potential Misrepresentation of Inherited Breast Cancer Risk by Common Germline Alleles

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