Computational advances of tumor marker selection and sample classification in cancer proteomics

Tang, Jing; Luo, Yongchao; Fu, Jianbo; Zhang, Yang; Li, Yang; Xiao, Zuoxiang; Lou, Yan; Qiu, Yunqing; Zhu, Feng

doi:10.1016/j.csbj.2020.07.009

Cited by 41 publications

(26 citation statements)

References 196 publications

(209 reference statements)

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“…Cancer is a serious threat to human life and health. 1 Tumor markers play an important role in the early diagnosis and treatment of cancers. 2 As effective cancer monitors, exosomes have shown promising advantages in liquid biopsies because of their noninvasive measurement and predictability of the disease.…”

Section: Introductionmentioning

confidence: 99%

Immunoassay-aptasensor for the determination of tumor-derived exosomes based on the combination of magnetic nanoparticles and hybridization chain reaction

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Immunoassay-aptasensor for the determination of tumor-derived exosomes based on the combination of magnetic nanoparticles and hybridization chain reaction

et al. 2021

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“…screened ten discriminant features, which provided a fast and effective diagnostic standard for Kashin–Beck disease [ 30 ]. We also used “ e107 ” package to carry out SVM-RFE algorithm and identify key genes in occurrence and development of AF with discriminative power [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Chen

et al. 2022

BMC Med Genomics

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Objective We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail. Methods Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells. Results 129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells. Conclusions In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.

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“…Initially, analysis of DE genes between pancreatic tumor tissues and normal tissues was performed using the ‘limma’ package ( ) in R ( 27 , 28 ), according to the screening criteria (adjusted P≤0.10 and |log fold-change|≥1.5). DE chemokines and DE chemokine receptors were determined by overlapping the DE genes with a dataset containing all chemokines and their receptors; this analysis was performed using the ‘VennDiagram’ package in R ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

CXCL5 expression in tumor tissues is associated with poor prognosis in patients with pancreatic cancer

Wang

et al. 2020

Oncol Lett

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Immunotherapy based on the tumor microenvironment is a feasible method for treating cancer; therefore, it is necessary to investigate the immune microenvironment of pancreatic cancer and the influencing factors of the immune microenvironment. Chemokines are an important factor affecting the tumor immune microenvironment. In the present study, chemokines or chemokine receptors were screened to identify those differentially expressed in pancreatic cancer compared with normal controls and associated with patient prognosis. Chemokines or chemokine receptors that are differentially expressed in pancreatic cancer tumor tissues were initially screened using the Gene Expression Omnibus database. Next, survival analysis was performed using GEPIA, a website based on The Cancer Genome Atlas (TCGA) database. Immunohistochemical staining of CXCL5 was performed in tissue microarrays (TMAs) containing 119 cases of pancreatic cancer. Histochemistry score (H-SCORE) was used to evaluate the expression of CXCL5. Next, association analysis of the H-SCORE of CXCL5 and the clinical characteristics of patients was performed, as well as Kaplan-Meier survival and Cox multivariate regression analyses. The results of the bioinformatics analysis demonstrated that CXCL5 was highly expressed in pancreatic cancer tissues. High expression of CXCL5 in pancreatic cancer tissues was associated with a poor prognosis in patients in TCGA cohort. The expression level of CXCL5 in tumor tissues was significantly higher compared with that in adjacent peritumoral normal tissues in the immunohistochemical analysis. There was no significant association between CXCL5 expression in pancreatic cancer tumor tissues and clinicopathological factors. Patients with pancreatic cancer with high CXCL5 expression had a poor prognosis, as determined by Kaplan-Meier survival analysis based on the TMA dataset. The results of Cox multivariate regression analysis showed that CXCL5 was an independent factor for a poor prognosis in patients with pancreatic cancer. In conclusion, the results of the present study revealed that the chemokine CXCL5 was highly expressed in pancreatic cancer tissues; high CXCL5 expression was associated with a poor prognosis in patients with pancreatic cancer.

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Computational advances of tumor marker selection and sample classification in cancer proteomics

Abstract: Graphical abstract

Cited by 41 publications

References 196 publications

Immunoassay-aptasensor for the determination of tumor-derived exosomes based on the combination of magnetic nanoparticles and hybridization chain reaction

Immunoassay-aptasensor for the determination of tumor-derived exosomes based on the combination of magnetic nanoparticles and hybridization chain reaction

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

CXCL5 expression in tumor tissues is associated with poor prognosis in patients with pancreatic cancer

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