The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders. The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples. Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples. This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches.

中文翻译：

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膀胱癌的六种潜在生物标志物：细胞周期分裂和凋亡途径中的关键蛋白

膀胱癌 (BC) 病理是由外源性环境和内源性分子因素引起的。牵涉到几个基因，但 BC 及其亚型的分子发病机制仍有争议。生物信息学分析在一项研究中评估大量蛋白质，增加了识别疾病可能生物标志物的机会。本研究的目的是使用几种生物信息学分析工具和方法来确定用于识别 BC 的生物标志物。在 GSE13507 和 GSE37817 数据集中用 T 检验比较每个探针组的 BC 和正常样本，并用 GSE52519 和 E-MTAB-1940 数据集验证统计探针组。利用差异基因表达、层次聚类、基因本体富集分析和启发式在线表型预测算法方法。在人类蛋白质图谱数据库中评估了具有统计学意义的蛋白质。GSE13507（6271个探针组）和GSE37817（3267个探针组）数据在提取没有基因注释信息的探针组后是显着的。通过分析 BC 样本中的前 100 个上调和下调探针组，进一步缩小了两个数据集 (2888) 中的常见探针组。在总共 400 个探针组中，有 68 个对于具有相似倍数变化值的两个数据集均显着（Pearson r：0.995）。蛋白质-蛋白质相互作用网络证明了 CCNB1、BUB1B 和 AURKB 之间的强烈相互作用。HPA 数据库显示 BC 和对照样品的 CKAP2L、AURKB、APIP 和 LGALS3 的蛋白质表达水平相似。该研究揭示了 BC 早期诊断的六种候选生物标志物。