UNASSIGNED: Small cell lung cancer (SCLC) is a highly malignant and aggressive neuroendocrine tumor. With the rise of immunotherapy, it has provided a new direction for SCLC. However, due to the lack of prognostic biomarkers, the median overall survival of SCLC is still to be improved. This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC.
UNASSIGNED: Gene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus (GEO) database, and tumor microenvironment (TME) infiltration profile data were obtained using CIBERSORT. The robust rank aggregation (RRA) method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes (DEGs) between normal and tumor tissue samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of the robust DEGs. Subsequently, protein-protein interaction networks and key modules were constructed by Cytoscape, and hub genes were selected from the whole network using the plugin cytoHubba. Survival analysis of hub genes was performed by Kaplan-Meier plotter in 18 patients with extensive-stage SCLC.
UNASSIGNED: A total of 312 robust DEGs, including 55 upregulated and 257 downregulated genes, were screened from 129 SCLC tissue samples and 44 normal tissue samples. GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection, focal adhesion, complement and coagulation cascades, tumor necrosis factor (TNF) signaling pathway, and ECM-receptor interaction, which are closely associated with the development and progression of SCLC. Subsequently, three DEGs modules and six hub genes (ITGA10, DUSP12, PTGS2, FOS, TGFBR2, and ICAM1) were identified through screening with the Cytoscape plugins MCODE and cytoHubba, respectively. Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+ T cells were the predominant infiltrating immune cells in SCLC. In addition, Kaplan-Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential prognostic biomarker of SCLC.
UNASSIGNED: Hub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC, and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.

This study was aimed at investigating the pathogenesis of chronic obstructive pulmonary disease (COPD) caused by smoking-based on bioinformatics analysis and in vitro experimental evidence. The GEO, GEO2R, TargetScan, miRDB, miRWalk, DAVID, and STRING databases were used for bioinformatics analysis. The mRNA expression and the protein levels were determined by real-time PCR and ELISA. After taking the intersection of the diversified results of the databases, four differentially expressed miRNAs (hsa-miR-146a, hsa-miR-708, hsa-miR-150, and hsa-miR-454) were screened out. Subsequently, a total of 57 target genes of the selected miRNAs were obtained. The results of DAVID analysis showed that the selected miRNAs participated in COPD pathogenesis through long-term potentiation, the TGF-β signaling pathway, the PI3K-Akt signaling pathway, etc. The results of STRING prediction showed that TP53, EP300, and MAPK1 were the key nodes of the PPI network. The results of the confirmatory experiment showed that, compared with the control group, the mRNA expression of ZEB1, MAPK1, EP300, and SP1 were up-regulated, while the expression of MYB was down-regulated and the protein levels of ZEB1, MAPK1, and EP300 were increased. Taken together, miRNAs (hsa-miR-146a, hsa-miR-708, hsa-miR-150, and hsa-miR-454) and their regulated target genes and downstream protein molecules (ZEB1, EP300, and MAPK1) may be closely related to the pathological process of COPD.

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

UNASSIGNED: Nephroblastoma, also known as Wilms\' tumor (WT), remains one of the major causes of tumor-related deaths worldwide in children. Cancer stem cells (CSCs) are considered to be the main culprits in cancer resistance and disease recurrence, which are reported in multiple types of tumors. However, the research on CSCs in WT is limited. Therefore, our study aimed to identify the key genes related to CSCs in WT to provide new ideas for treating WT.
UNASSIGNED: The RNA-seq and clinical data of WT samples were obtained from the University of California Santa Cruz (UCSC) Xena database, which included 120 WT and six para-cancerous tissues. The mRNA stemness index (mRNAsi) based on mRNA expression was calculated to evaluate tumor stem cell characteristics in WT patients. A Kaplan-Meier (KM) analysis was performed to explore the clinical characteristics of the mRNAsi in WT. A weighted gene co-expression network analysis (WGCNA) was used to identify the key modules and genes related to the mRNAsi. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to explore the signaling pathways based on the key genes. The expression levels of the key genes were validated by the Gene Expression Omnibus (GEO) database. Further, the important upstream genes were identified by DisNor and gene co-expression analyses.
UNASSIGNED: The mRNAsi was significantly upregulated in WT (P=7.2e-05) and showed an upward trend in line with the pathological stage. Patients with lower mRNAsi scores had better overall survival (OS) than those with higher mRNAsi scores (P=0.0087). Eleven genes were defined as the key genes associated with the mRNAsi based on our WGCNA analysis [cor.MM (correlation. Module membership) >0.8 and cor.GS (correlation. Gene significance) >0.45] and were closely related to cell proliferation-related signaling pathways (P<0.05). Moreover, using protein interaction analysis, we identified ATM and CDKN1A as the key upstream regulatory genes of the 11 key genes.
UNASSIGNED: Our study showed that the mRNAsi score was a potential prognostic factors in WT and identified the upstream genes ATM and CDKN1A and 11 genes closely related to the mRNAsi, which may provide new insights for CSC-targeted therapy in WT and improve clinical outcomes for WT patients.

The NAC transcription factor (TF) family is one of the largest TF families in plants, which has been widely reported in rice, maize and common wheat. However, the significance of the NAC TF family in wild emmer wheat (Triticum turgidum ssp. dicoccoides) is not yet well understood. In this study, a genome-wide investigation of NAC genes was conducted in the wild emmer genome and 249 NAC family members (TdNACs) were identified. The results showed that all of these genes contained NAM/NAC-conserved domains and most of them were predicted to be located on the nucleus. Phylogenetic analysis showed that these 249 TdNACs can be classified into seven clades, which are likely to be involved in the regulation of grain protein content, starch synthesis and response to biotic and abiotic stresses. Expression pattern analysis revealed that TdNACs were highly expressed in different wheat tissues such as grain, root, leaves and shoots. We found that TdNAC8470 was phylogenetically close to NAC genes that regulate either grain protein or starch accumulation. Overexpression of TdNAC8470 in rice showed increased grain starch concentration but decreased grain Fe, Zn and Mn contents compared with wild-type plants. Protein interaction analysis indicated that TdNAC8470 might interact with granule-bound starch synthase 1 (TdGBSS1) to regulate grain starch accumulation. Our work provides a comprehensive understanding of the NAC TFs family in wild emmer wheat and establishes the way for future functional analysis and genetic improvement of increasing grain starch content in wheat.

An extensive understanding of the interactions between host cellular and viral proteins provides clues for studying novel antiviral strategies. Porcine circovirus type 3 (PCV3) and type 4 (PCV4) have recently been identified as viruses that can potentially damage the swine industry. Herein, 401 putative PCV3 Cap-binding and 484 putative PCV4 Cap-binding proteins were characterized using co-immunoprecipitation and liquid chromatography-mass spectrometry. Both PCV3 and PCV4 Caps shared 278 identical interacting proteins, but some putative interacting proteins (123 for PCV3 Cap and 206 for PCV4 Cap) differed. A protein-protein interaction network was constructed, and according to gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) database analyses, both PCV3 Cap- and PCV4 Cap-binding proteins participated mainly in ribosome biogenesis, nucleic acid binding, and ATP-dependent RNA helicase activities. Verification assays of eight putative interacting proteins indicated that nucleophosmin-1, nucleolin, DEAD-box RNA helicase 21, heterogeneous nuclear ribonucleoprotein A2/B1, YTH N6-methyladenosine RNA binding protein 1, and Y-box binding protein 1 bound directly to both PCV3 and PCV4 Caps, but ring finger protein 2 and signal transducer and activator of transcription 6 did not. Therefore, the interaction network provided helpful information to support further research into the underlying mechanisms of PCV3 and PCV4 infection.

Swine acute diarrhea syndrome coronavirus (SADS-CoV) is an enterovirus that can cause acute diarrhea and death in piglets and cause serious economic losses to the pig industry. SADS-CoV membrane (M) protein mainly plays a key role in biological processes, such as virus assembly, budding, and host innate immune regulation. Understanding the interaction between M protein and host proteins is very important to define the molecular mechanism of cells at the protein level and to understand specific cellular physiological pathways. In this study, 289 host proteins interacting with M protein were identified by glutathione-S-transferase (GST) pull-down combined with liquid chromatography-mass spectrometry (LC-MS/MS), and the protein-protein interaction (PPI) network was established by Gene Ontology (GO) terms and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways analysis. Results showed that SADS-CoV M protein was mainly associated with the host metabolism, signal transduction, and innate immunity. The Co-Immunoprecipitation (CO-IP) validation results of six randomly selected proteins, namely, Rab11b, voltage-dependent anion-selective channel 1 (VDAC1), Ribosomal Protein L18 (RPL18), RALY, Ras Homolog Family Member A (RHOA), and Annexin A2 (ANXA2), were consistent with LC-MS results. In addition, overexpression of RPL18 and PHOA significantly promoted SADS-CoV replication, while overexpression of RALY antagonized viral replication. This work will help to clarify the function of SADS-CoV M protein in the life cycle of SADS-CoV.

Motivation: A protein complex is the combination of proteins which interact with each other. Protein-protein interaction (PPI) networks are composed of multiple protein complexes. It is very difficult to recognize protein complexes from PPI data due to the noise of PPI. Results: We proposed a new method, called Topology and Semantic Similarity Network (TSSN), based on topological structure characteristics and biological characteristics to construct the PPI. Experiments show that the TSSN can filter the noise of PPI data. We proposed a new algorithm, called Neighbor Nodes of Proteins (NNP), for recognizing protein complexes by considering their topology information. Experiments show that the algorithm can identify more protein complexes and more accurately. The recognition of protein complexes is vital in research on evolution analysis. Availability and implementation: https://github.com/bioinformatical-code/NNP.

BACKGROUND: Pulmonary arterial hypertension (PH) secondary to pulmonary fibrosis (PF) is one of the most common complications in PF patients, it causes severe disease and usually have a poor prognosis. Whether the combination of PH and PF is a unique disease phenotype is unclear. We aimed to screen the key modules associated with PH-PF immune infiltration based on WGCNA and identify the hub genes for molecular typing.
METHODS: Using the gene expression profile GSE24988 of PF patients with or without PH from the Gene Expression Omnibus (GEO) database, we evaluated immune cell infiltration using Cibersortx and immune cell gene signature files. Different immune cell types were screened using the Wilcoxon test; differentially expressed genes were screened using samr. The molecular pathways implicated in these differential responses were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. A weighted co-expression network of the differential genes was constructed, relevant co-expression modules were identified, and relationships between modules and differential immune cell infiltration were calculated. The modules most relevant to this disease were identified using weighted correlation network analysis. From these, we constructed a co-expression network; using the STRING database, we integrated the values into the human protein-protein interaction network before constructing a co-expression interaction subnet, screening genes associated with immunity and unsupervised molecular typing, and analyzing the immune cell infiltration and expression of key genes in each disease type.
RESULTS: Of the 22 immune cell types from the PF GEO data, 20 different immune cell types were identified. There were 1622 differentially expressed genes (295 upregulated and 1327 downregulated). The resulting weighted co-expression network identified six co-expression modules. These were screened to identify the modules most relevant to the disease phenotype (the green module). By calculating the correlations between modules and the differentially infiltrated immune cells, extracting the green module co-expression network (46 genes), extracting 25 key genes using gene significance and module-membership thresholds, and combining these with the 10 key genes in the human protein-protein interaction network, we identified five immune cell-related marker genes that might be applied as biomarkers. Using these marker genes, we evaluated these disease samples using unsupervised clustering molecular typing.
CONCLUSIONS: Our results demonstrated that all PF combined with PH samples belonged to four categories. Studies on the five key genes are required to validate their diagnostic and prognostic value.

Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.