Anthropology and Health Informatics Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India.
Sci Rep. 2021 Nov 11;11(1):22036. doi: 10.1038/s41598-021-01508-8.
Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.
整合生物信息学分析有助于探索硝化甘油在不同类型癌症中的各种作用机制,并帮助预测硝化甘油影响癌症的靶基因。本研究使用了许多公开可用的数据库和工具。本研究的第一步是识别相互关联的基因。使用 Pubchem 和 SwissTargetPrediction 鉴定了硝化甘油的直接靶基因(激动剂、抑制剂、激动剂和抑制剂)。构建 PPI 网络以识别受 12 个直接靶基因影响的不同类型癌症,以及所鉴定的直接靶基因的接近系数。使用 CluePedia App 进行通路分析,以确定直接靶基因的生物分子功能。突变分析揭示了受突变基因影响的癌症类型和突变基因。虽然 PPI 网络构建揭示了受 12 个靶基因影响的癌症类型,但这一步仅揭示了受突变基因影响的癌症类型。仅选择突变基因进行进一步研究。将这些突变基因输入 STRING 以进行 NW 分析。NW 分析揭示了上述突变基因中的相互关联基因。本研究的第二步是预测和鉴定上调和下调基因。从上述步骤中获得了来自 GEO 数据库的鉴定癌症的数据集。对上述数据集进行 DEG 分析,以预测上调和下调基因。将步骤 1 中鉴定的相互关联基因与步骤 2 中获得的上调和下调基因进行比较,揭示了相互关联基因之间的共表达基因。使用 STRING 对共表达基因进行 NW 分析,以确定共表达基因的接近系数。对共表达基因进行基因本体论分析,以确定其功能。对共表达基因进行通路分析,以确定受共表达基因影响的癌症类型。在步骤 1 中的突变分析中鉴定的四种癌症与在该通路分析中鉴定的癌症相同。这进一步证实了在突变分析中鉴定的四种癌症。对上述共表达基因进行生存分析使用 Survexpress。通过生存分析,为四种类型的癌症鉴定了硝化甘油的生物标志物。这四种癌症是膀胱癌、子宫内膜癌、黑色素瘤和非小细胞肺癌。