Immune infiltration landscape and potential drug-targeted implications for hepatocellular carcinoma with ‘progression/hyper-progression’ recurrence by Jing-Xuan Xu, Yue-Xiang Su, Yuan-Yuan Chen, Yi-Yue Huang, Zu-Shun Chen, Yu-Chong Peng, Lu-Nan Qi

“…Co-expression and protein interaction analyses were used to identify characteristic genes in ITM closely associated with type III–IV recurrence, which was matched with drug targets for Huaier granules (HG) and lenvatinib. …”
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Neuroinflammation mediates the progression of neonate hypoxia-ischemia brain damage to Alzheimer’s disease: a bioinformatics and experimental study by Shengjie Zhang, Ruqiu Zhang, Zhaoqin Chen, Zihan Shao, An Li, Fan Li, Fang Huang

“…After identifying the common differentially expressed genes (DEGs) of Alzheimer’s disease and neonate Hypoxia-Ischemia Brain Damage by limma package analysis, five kinds of analyses were performed on them, namely Gene Ontology (GO) and pathway enrichment analysis, protein–protein interaction network, DEG-transcription factor interactions and DEG-microRNA interactions, protein-drug interactions and protein-disease association analysis, and gene-inflammation association analysis and protein-inflammation association analysis.ResultsIn total, 12 common DEGs were identified including HSPB1, VIM, MVD, TUBB4A, AACS, ANXA6, DIRAS2, RPH3A, CEND1, KALM, THOP1, AREL1. …”
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In silico analysis of hub genes and regulatory networks implicates the putamen in non-motor Parkinson’s disease disorders by Stephen Tunmise Akanbi, Joshua Ayodele Yusuf, Thompson Oluwaferanmi Ayandele, Dayo Samson Oladipupo, Darasimi Racheal Olorunlowu, Blessing Dorcas Olawuyi, Joshua Oluwafisayo Adepoju, Dolapo Emmanuel Opasina, Elizabeth Kehinde Opoola, Uchenna Victor Ugwuanyi, Olufunto Omodele Adeleye

“…Differential gene expression analysis was carried out using with Limma, filtering for genes with |logFC|> 1 (fold change) and p < 0.05 (p-value). Protein–Protein Interaction networks were constructed using stringDB (combined score > 0.7) and analyzed in Cytoscape to identify hub genes based on various topological measures (EPC, MCC, MNC, Degree, and EcCentricity). …”
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Proteomic analysis of spinal dorsal horn in prior exercise protection against neuropathic pain by Binglin Chen, Jiabao Guo, Chan Gong, Chenchen Zhu, Yang Wu, Shengbo Wang, Yili Zheng, Haixia Lu

“…Gene Ontology (GO) annotations, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein–protein interaction networks were employed to explore the biological importance of DEPs. …”
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Bergapten alleviates senescence of chondrocytes in osteoarthritis by YE Yuanlan, LIU Kaiwen, PAN Yiming

“…The obtained targets were intersected with Venny2.1.0, protein-protein interaction (PPI) analysis was performed using the STRING database, and molecular docking was conducted by AutoDock Vina. …”
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Possible linking and treatment between Parkinson’s disease and inflammatory bowel disease: a study of Mendelian randomization based on gut–brain axis by Beiming Wang, Xiaoyin Bai, Yingmai Yang, Hong Yang

“…Enrichment analysis and protein‒protein interaction (PPI) network were done to explore common pathogenesis pathways. …”
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Elevated miR-221-3p inhibits epithelial–mesenchymal transition and biochemical recurrence of prostate cancer via targeting KPNA2: an evidence-based and knowledge-guided strategy by Dingchao Li, Jingang Jian, Manhong Shi, Zihao Chen, Anguo Zhao, Xuedong Wei, Yuhua Huang, Yalan Chen, Jianquan Hou, Yuxin Lin

“…Then a knowledge-guided network strategy was applied to prioritize key targets of miR-221-3p in PCa progression based both on topological and functional characterization of genes in multi-omics miRNA-mRNA and protein-protein interaction networks. Finally, a key gene was computationally identified and experimentally validated using cell line and clinical samples through EdU assay, scratch assay, transwell assay, dual-luciferase reporter assay and the epithelial-to-mesenchymal transition (EMT)-related analysis. …”
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Cardiovascular Risk Biomarkers in Women with and Without Polycystic Ovary Syndrome by Manjula Nandakumar, Priya Das, Thozhukat Sathyapalan, Alexandra E. Butler, Stephen L. Atkin

“…Functional enrichment and protein–protein interaction analyses elucidated the pathways involved. …”
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CCR7 and CD48 as Predicted Targets in Acute Rejection Related to M1 Macrophage after Pediatric Kidney Transplantation by Jie Zhang, Jun Pei, Chengjun Yu, Jin Luo, Yifan Hong, Yi Hua, Guanghui Wei

“…At the same time, we used the protein–protein interaction (PPI) network and Cytoscape software to determine the key genes. …”
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Identification of potential druggable targets for endometriosis through Mendelian randomization analysis by Peng Chen, Xin Wei, Xiao-Ke Li, Yi-Hang Zhou, Yi-Hang Zhou, Qi-Fang Liu, Ling Ou-Yang

“…Additionally, we conducted a protein-protein interaction (PPI) network as well as functional enrichment analyses to unveil potential associations among target proteins.ResultsMR analysis revealed that a decrease of one standard deviation (SD) in plasma R-Spondin 3 (RSPO3) level had a protective effect on EM (OR = 1.0029; 95% confidence interval (95% CI): 1.0015–1.0043; P = 3.2567e-05; Bonferroni P &lt; 5.63 × 10−5). …”
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Integrative Bioinformatics Analysis to Identify Key Ferroptosis-Related Genes and Immune Infiltration in Aortic Aneurysm and Dissection: Implication of PTGS2 by An W, Luo J, Zhang C, Xiao Q

“…Function enrichment analyses revealed that the FRGs were mainly associated with responses to chemical stress and cytokine signaling in the immune system. Protein–protein interaction network analyses identified 8 hub FRGs including EZH2, EGFR, HIF1A, IL6, PTGS2, MAPK1, IL1B and SRC. …”
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Identifying a Serum Exosomal-Associated lncRNA/circRNA-miRNA-mRNA Network in Coronary Heart Disease by Jia Mao, Yufei Zhou, Licheng Lu, Ping Zhang, Runhua Ren, Yaqiong Wang, Jing Wang

“…Subsequently, a protein-protein interaction (PPI) network was established to screen out on which hub genes enrichment analyses should be performed, and a ceRNA network (lncRNA/circRNA-miRNA-mRNA) was constructed to elucidate ceRNA axes in CHD. …”
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Exploring the mechanism of rosmarinic acid in the treatment of lung adenocarcinoma based on bioinformatics methods and experimental validation by Chaowang Zhou, Ruqian Zhong, Lei Zhang, Renyi Yang, Yuxin Luo, Huijun Lei, Liang Li, Jianzhong Cao, Zhiying Yuan, Xiaoning Tan, Mengzhou Xie, Haoyu Qu, Zuomei He

“…Secondly, GO and KEGG enrichment analysis of RDEGs were performed, and protein–protein interaction networks (PPIs) were constructed to identify and visualize hub RDEGs. …”
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Dual disease co-expression analysis reveals potential roles of estrogen-related genes in postmenopausal osteoporosis and Parkinson’s disease by Dongdong Yu, Dongdong Yu, Jian Kang, Chengguo Ju, Qingyan Wang, Ye Qiao, Ye Qiao, Long Qiao, Long Qiao, Dongxiang Yang

“…Differentially expressed genes (DEGs) among PMO, PD, and ERGs were subjected to an array of bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses, in addition to protein-protein interaction (PPI) network analysis. The study also involved constructing TF-gene interactions, TF-microRNA coregulatory networks, interactions of hub genes with diseases, and validation through quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsThe colocalization analysis uncovered shared genetic variants between PD and osteoporosis, with a posterior probability of colocalization (PPH4) measured at 0.967. …”
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Identification and validation of the important role of KIF11 in the development and progression of endometrial cancer by Biying Wang, Lunmin Bao, Xiaoduo Li, Guang Sun, Wu Yang, Nanzi Xie, Ling Lei, Wei Chen, Hailong Zhang, Man Chen, Xing Zhao, Xiufang Wan, Rui Yuan, Hongmei Jiang

“…DEGs were further screened for hub genes through protein–protein interaction (PPI) network analysis and machine learning. …”
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Exploration of the mechanism of Lithospermum erythrorhizon oil in treating atopic dermatitis based on network pharmacology and experimental validation of the PI3K-Akt pathway regul... by Weisheng Hu, Yinlan Wang, Yingjie Zhou, Junbao Shi, Zengyan Li, Xiaoling Jiang, Qinyuan Wu, Changming Zhong, Huilan Weng, Sijie Ouyang, Yuan Jing, Xianxiang Cai, Mingda Ye, Ning Huang

“…Common targets were selected by intersecting these with AD-related targets from GeneCards. A protein-protein interaction (PPI) network was built using STRING, and functional analysis Gene Ontology (GO) and pathway enrichment Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on Metascape. …”
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Unveiling the role of ASPP1 in cancer progression: pan-cancer bioinformatics and experimental validation in colorectal cancer by Keyuan Xiao, Xiang Li, Ihsan Ullah, Wenqing Hu, Kaiqiang Wang, Fan Yang, Chengyu Yang, Chunqi Feng, Liang Zong, Xinghua Li

“…In-depth protein-protein interaction (PPI) analysis revealed ASPP1’s involvement in apoptosis and cancer progression networks. …”
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Genome-wide identification of the Sec14 gene family and the response to salt and drought stress in soybean (Glycine max) by Jinyu Zhang, Liying Zou, Li Wang, Dongchao Zhang, Ao Shen, Yongqi Lei, Maoni Chao, Xinjuan Xu, Zhiwei Xue, Zhongwen Huang

“…Furthermore, the protein interaction network and miRNA regulation associated with GmSec14s were predicted to elucidate the potential functions of GmSec14 members. …”
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Effects on the Functionality and Structure of Rice-Pea Composite Protein by Industry-scale Microfluidizer Combined with pH Cycling Treatment by Yunfeng LU, Taotao DAI, Zhaoying LI, Jialong HAN, Ti LI, Chengmei LIU, Jun CHEN

“…The results of intrinsic fluorescence spectroscopy, surface hydrophobicity, sulfhydryl disulfide bonding, and circular dichroism indicated that the two proteins interacted to form new co-assembly composites. …”
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Integrating bulk and single-cell RNA sequencing data: unveiling RNA methylation and autophagy-related signatures in chronic obstructive pulmonary disease patients by Shi-Xia Liao, Lan-Ying Zhang, Ling-Mei Shi, Huai-Yu Hu, Yan-Hui Gu, Ting-Hua Wang, Yao Ouyang, Peng-Peng Sun

“…Differentially expressed genes (DEGs) were identified between COPD and non-COPD samples, and protein–protein interaction networks were constructed. Univariate logistic regression identified shared genes between DEGs and RNA methylation gene sets. …”
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