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Abstract：
Gallbladder cancer (GBC) is a rare but very aggressive most common digestive tract cancer with a high mortality rate due to delayed diagnosis at the advanced stage. Moreover, GBC progression shows asymptomatic characteristics making it impossible to detect at an early stage. In these circumstances, conventional therapy like surgery, chemotherapy, and radiotherapy becomes refractive. However, few studies reported some molecular markers like KRAS (Kirsten Rat Sarcoma) mutation, upregulation of HER2/neu, EGFR (Epidermal Growth Factor Receptor), and microRNAs in GBC. However, the absence of some specific early diagnostic and prognostic markers is the biggest hurdle for the therapy of GBC to date. The present study has been designed to identify some specific molecular markers for precise diagnosis, and prognosis, for successful treatment of the GBC. By In Silico a network-centric analysis of two microarray datasets; (GSE202479) and (GSE13222) from the Gene Expression Omnibus (GEO) database, shows 50 differentially expressed genes (DEGs) associated with GBC. Further network analysis revealed that 12 genes are highly interconnected based on the highest MCODE (Molecular Complex Detection) value, among all three genes; TRIP13 (Thyroid Receptor Interacting Protein), NEK2 (Never in Mitosis gene-A related Kinase 2), and TPX2 (Targeting Protein for Xklp2) having highest network interaction with transcription factors and miRNA suggesting critically associated with GBC. Further survival analysis data corroborate the association of these genes; TRIP13, NEK2, and TPX2 with GBC. Thus, TRIP13, NEK2, and TPX2 genes are significantly correlated with a greater risk of mortality, transforming them from mere biomarkers of the GBC for early detections and may emerge as prognostic markers for treatment.
摘要：
胆囊癌（GBC）是一种罕见但非常侵袭性的最常见消化道癌症，由于晚期诊断延迟，死亡率很高。此外，GBC进展表现出无症状的特征，因此无法在早期发现。在这种情况下,像手术这样的常规疗法，化疗,放射疗法变得屈光。然而,很少有研究报道一些分子标志物，如KRAS（Kirsten大鼠肉瘤）突变，HER2/neu的上调，EGFR(表皮生长因子受体)和GBC中的microRNA。然而,缺乏一些特异性的早期诊断和预后标志物是迄今为止GBC治疗的最大障碍.本研究旨在鉴定一些特定的分子标志物，以进行精确诊断。和预后，成功治疗GBC。通过在Silico中对两个微阵列数据集进行网络中心分析；（GSE202479）和（GSE13222）来自基因表达综合（GEO）数据库，显示与GBC相关的50个差异表达基因(DEGs)。进一步的网络分析显示，基于最高的MCODE（分子复合物检测）值，有12个基因高度互连，在所有三个基因中；TRIP13(甲状腺受体相互作用蛋白)，NEK2(Neverin有丝分裂基因-A相关激酶2),和TPX2（Xklp2的靶向蛋白）与转录因子和miRNA具有最高的网络相互作用，表明与GBC密切相关。进一步的生存分析数据证实了这些基因TRIP13、NEK2和TPX2与GBC的关联。因此，TRIP13，NEK2和TPX2基因与更高的死亡风险显着相关，将它们从单纯的GBC生物标志物转化为早期检测，并可能作为预后标志物用于治疗。
