Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues

Pan-cancer prognosis, immune infiltration, and drug resistance characterization of lung squamous cell carcinoma tumor microenvironment-related genes

Background

The tumor microenvironment (TME) plays an important role in cancer development; however, its implications in lung squamous cell carcinoma (LUSC) and pan-cancer have been poorly understood.

Methods

In this study, The Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissue using Expression Data (ESTIMATE) datasets were applied to identify differentially expressed genes. Additionally, online public databases were utilized for in-depth bioinformatics analysis of pan-cancer datasets to investigate the prognostic implications of TME-related genes further.

Results

Our study demonstrated a significant association between stromal scores, immune scores, and specific clinical characteristics in LUSC patients. C3AR1, CSF1R, CCL2, CCR1, and CD14 were identified as prognostic genes related to the TME. All TME-related prognostic genes demonstrated varying degrees of correlation with immune infiltration subtypes and tumor cell stemness. Moreover, our study revealed that TME-related prognostic genes, particularly C3AR1 and CCR1, might contribute to drug resistance in cancer cells.

Conclusions

The identified TME-related prognostic genes, particularly C3AR1 and CCR1, have potential implications for understanding and targeting drug resistance mechanisms in cancer cells.

Proteomics Analysis of the Polyomavirus DNA Replication Initiation Complex Reveals Novel Functional Phosphorylated Residues and Associated Proteins

Polyomavirus (PyV) Large T-antigen (LT) is the major viral regulatory protein that targets numerous cellular pathways for cellular transformation and viral replication. LT directly recruits the cellular replication factors involved in initiation of viral DNA replication through mutual interactions between LT, DNA polymerase alpha-primase (Polprim), and single-stranded DNA binding complex, (RPA). Activities and interactions of these complexes are known to be modulated by post-translational modifications; however, high-sensitivity proteomic analyses of the PTMs and proteins associated have been lacking. High-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS) of the immunoprecipitated factors (IPMS) identified 479 novel phosphorylated amino acid residues (PAARs) on the three factors; the function of one has been validated. IPMS revealed 374, 453, and 183 novel proteins associated with the three, respectively. A significant transcription-related process network identified by Gene Ontology (GO) enrichment analysis was unique to LT. Although unidentified by IPMS, the ETS protooncogene 1, transcription factor (ETS1) was significantly overconnected to our dataset indicating its involvement in PyV processes. This result was validated by demonstrating that ETS1 coimmunoprecipitates with LT. Identification of a novel PAAR that regulates PyV replication and LT's association with the protooncogenic Ets1 transcription factor demonstrates the value of these results for studies in PyV biology.

CAVIN2/SDPR Functioned as a Tumor Suppressor in Lung Adenocarcinoma from Systematic Analysis of Caveolae-Related Genes and Experimental Validation

Background: Caveolae-Related Genes include caveolins and cavins, which are the main component of the fossa and, play important roles in a variety of physiological and pathological processes. Although increasing evidence indicated that caveolins (CAVs) and cavins (CAVINs) are involved in carcinogenesis and progression, their clinical significance and biological function in lung cancer are still limited. Methods: We investigated the expression of CAVs and CAVINs at transcriptional levels using Oncomine and Gene Expression Profiling Interactive Analysis. The protein and mRNA expression levels of CAVs and CAVINs were determined by the human protein atlas website and our surgically resected samples, respectively. The clinical value of prognostic prediction based on the expression of CAVs and CAVINs was also assessed. cBioPortal, GeneMANIA and STRING were used to analyze the molecular characteristics of CAVs and CAVINs in lung adenocarcinoma (LUAD) comprehensively. Finally, we investigated the effect of CAVIN2/SDPR (serum deprivation protein response) on LUAD cells with biological experiments in vitro. Results: The expression of CAV1/2 and CAVIN1/2/3 were significantly downregulated in LUAD and lung squamous cell carcinoma (LUSC). The patients with high expression of CAV1, CAV2, CAV3, CAVIN1 and CAVIN2/SDPR were tightly correlated with a better prognosis in LUAD, while no statistical significances in LUSC. Further, our results found that CAVIN2/SDPR can be identified as a prognostic biomarker independent of other CAVINs in patients with LUAD. Mechanically, the overexpression of CAVIN2/SDPR inhibited cell proliferation and migration owing to the cell apoptosis induction and cell cycle arrest at S phase in LUAD cells. Conclusions: CAVIN2/SDPR functioned as a tumor suppressor, and was able to serve as prognostic biomarkers in precision medicine of LUAD. Mechanically, overexpression of CAVIN2/SDPR inhibited cell proliferation by inducing cell apoptosis and S phase arrest in LUAD cells.

Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Penetrating Exploration of Prognostic Correlations of the FKBP Gene Family with Lung Adenocarcinoma

The complexity of lung adenocarcinoma (LUAD), the development of which involves many interacting biological processes, makes it difficult to find therapeutic biomarkers for treatment. FK506-binding proteins (FKBPs) are composed of 12 members classified as conservative intracellular immunophilin family proteins, which are often connected to cyclophilin structures by tetratricopeptide repeat domains and have peptidyl prolyl isomerase activity that catalyzes proline from residues and turns the trans form into the cis form. Since FKBPs belong to chaperone molecules and promote protein folding, previous studies demonstrated that FKBP family members significantly contribute to the degradation of damaged, misfolded, abnormal, and foreign proteins. However, transcript expressions of this gene family in LUAD still need to be more fully investigated. In this research, we adopted high-throughput bioinformatics technology to analyze FKBP family genes in LUAD to provide credible information to clinicians and promote the development of novel cancer target drugs in the future. The current data revealed that the messenger (m)RNA levels of FKBP2, FKBP3, FKBP4, FKBP10, FKBP11, and FKBP14 were overexpressed in LUAD, and FKBP10 had connections to poor prognoses among LUAD patients in an overall survival (OS) analysis. Based on the above results, we selected FKBP10 to further conduct a comprehensive analysis of the downstream pathway and network. Through a DAVID analysis, we found that FKBP10 was involved in mitochondrial electron transport, NADH to ubiquinone transport, mitochondrial respiratory chain complex I assembly, etc. The MetaCore pathway analysis also indicated that FKBP10 was involved in "Ubiquinone metabolism", "Translation_(L)-selenoaminoacid incorporation in proteins during translation", and "Transcription_Negative regulation of HIF1A function". Collectively, this study revealed that FKBP family members are both significant prognostic biomarkers for lung cancer progression and promising clinical therapeutic targets, thus providing new targets for treating LUAD patients.

Association of the Timeless Gene with Prognosis and Clinical Characteristics of Human Lung Cancer

(1) Background: As the most common malignant tumor type worldwide, it is necessary to identify novel potential prognostic biomarkers to improve the poor prognosis of lung cancer. The Timeless gene, a circadian rhythm-related gene, is associated with several types of cancer. However, studies analyzing the clinical significance of the Timeless gene in patients with lung cancer are currently limited. (2) Methods: In the present study, the expression levels and prognostic potential of the Timeless gene and its co-expressed genes in different subtypes of lung cancer were explored using multiple bioinformatics approaches. The correlations between the Timeless gene and its co-expressed genes were validated using A549 and NCI-H226 cells by transfecting them with expression vectors and analyses using Western blot and reverse transcription-quantitative PCR. (3) Results: The Oncomine and GEPIA database analyses indicated that the expression of the Timeless gene was significantly higher in lung cancer as compared to that in the normal tissue. Using the UALCAN database, significant differences in Timeless gene expression were determined among different stages of lung cancer and between genders. A Kaplan-Meier plotter analysis indicated that high expression of the Timeless gene was associated with poor overall survival (OS) and progression-free survival (PFS) of patients with lung cancer. In the cBioPortal and GEPIA database analyses, extra spindle pole bodies like 1 (ESPL1) was the top correlated gene of Timeless in patients with lung cancer. Similar to the Timeless gene, high expression of the ESPL1 gene was also associated with poor OS and PFS. Of note, overexpression of the Timeless gene increased the expression level of ESPL1 at both the mRNA and protein levels. (4) Conclusion: The present study explored the clinical significance of the Timeless gene and its correlated gene ESPL1 in patients with lung cancer, thereby providing a potential therapeutic target for lung cancer.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Systematic analysis of expression profiles of HMGB family members for prognostic application in non-small cell lung cancer

Background: Lung cancer is a significant challenge to human health. Members of the high mobility group (HMG) superfamily (HMGB proteins) are implicated in a wide variety of physiological and pathophysiological processes, but the expression and prognostic value of HMGB family members in non-small cell lung cancer (NSCLC) have not been elucidated.

Methods: In this study, ONCOMINE, UALCAN, GEPIA, Kaplan–Meier Plotter, starBase, OncomiR databases, and GeneMANIA were utilized to evaluate the prognostic significance of HMGB family members in NSCLC.

Results: HMGB2/3 expression levels were higher in NSCLC patients. HMGB1 expression was higher in lung squamous cell carcinoma (LUSC) and was lower in lung adenocarcinoma (LUAD) tissue than in normal lung tissue. HMGB2 expression was related to cancer stage. Increased HMGB1 mRNA expression levels were associated with improved lung cancer prognosis, including overall survival (OS), first-progression survival (FP), and post-progression survival (PPS). There was no significant association between HMGB2 levels and prognostic indicators. HMGB3 expression was associated with poorer OS. GeneMANIA and GO/KEGG pathway analysis showed that HMGB family members mainly associated with chromosome condensation, regulation of chromatin organization, and nucleosome binding in NSCLC. HMGBs expression were closely correlated with infiltrating levels of specific types of immune cells in NSCLC, especially Th2 cells, Th17 cells, and mast cells. hsa-miR-25-3p, hsa-miR-374a-3p, and hsa-miR-93-5p were significantly positively correlated with HMGB1, HMGB2, and HMGB3, respectively. However, hsa-miR-30a-5p was predicted to significantly negatively regulate HMGB3 expression.

Conclusion: Our study revealed that HMGB1 is positively related to the improved prognosis in NSCLC, and demonstrate that HMGB3 might be a risk factor for poorer survival of NSCLC patients.

Comprehensive Landscape of STEAP Family Members Expression in Human Cancers: Unraveling the Potential Usefulness in Clinical Practice Using Integrated Bioinformatics Analysis

The human Six-Transmembrane Epithelial Antigen of the Prostate (STEAP) family comprises STEAP1-4. Several studies have pointed out STEAP proteins as putative biomarkers, as well as therapeutic targets in several types of human cancers, particularly in prostate cancer. However, the relationships and significance of the expression pattern of STEAP1-4 in cancer cases are barely known. Herein, the Oncomine database and cBioPortal platform were selected to predict the differential expression levels of STEAP members and clinical prognosis. The most common expression pattern observed was the combination of the over- and underexpression of distinct STEAP genes, but cervical and gastric cancer and lymphoma showed overexpression of all STEAP genes. It was also found that STEAP genes’ expression levels were already deregulated in benign lesions. Regarding the prognostic value, it was found that STEAP1 (prostate), STEAP2 (brain and central nervous system), STEAP3 (kidney, leukemia and testicular) and STEAP4 (bladder, cervical, gastric) overexpression correlate with lower patient survival rate. However, in prostate cancer, overexpression of the STEAP4 gene was correlated with a higher survival rate. Overall, this study first showed that the expression levels of STEAP genes are highly variable in human cancers, which may be related to different patients’ outcomes.

Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation.

METHODS

In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks.

RESULTS

We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways.

CONCLUSIONS

The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.

The High Expression of Minichromosome Maintenance Complex Component 5 Is an Adverse Prognostic Factor in Lung Adenocarcinoma

Background. Minichromosome maintenance (MCM) genes are crucial for genomic DNA replication and are important biomarkers in tumor biology. In this study, we aimed to identify the diagnostic, therapeutic, and prognostic value of the MCM2–10 genes in patients with lung cancer. Methods. We examined the expression levels, gene networks, and protein networks of lung cancer using data from the ONCOMINE, GeneMANIA, and STRING databases. We conducted a functional enrichment analysis of MCM2–10 using the clusterProfiler package using TCGA data. The correlation between the MCM2–10 expression and lung cancer prognosis was evaluated using Cox regression analysis. The influence of clinical variables on overall survival (OS) was evaluated using univariate and multivariate analyses. The TIMER database was used to evaluate the correlation between tumor infiltrating levels and lung cancer. Kaplan–Meier Plotter pan-cancer RNA sequencing was used to estimate the correlation between the MCM5 expression and OS in different immune cell subgroups in patients with lung adenocarcinoma (LUAD). Finally, the 1-, 3-, and 5-year predictions of LUAD were performed using nomogram and calibration analysis. Results. The expression of MCM2, 3, 4, 5, 6, 7, 8, and 10 in lung cancer was higher than that for normal samples. The MCM5 expression was associated with poor OS in patients with LUAD, and prognosis was related to TNM stage, smoking status, and pathological stage. The MCM5 expression is correlated with immune invasion in LUAD and may affect prognosis due to immune infiltration. Conclusion. MCM5 may serve as a molecular biomarker for LUAD prognosis.

SNRPD1/E/F/G Serve as Potential Prognostic Biomarkers in Lung Adenocarcinoma

Objectives: Sm proteins (SNRPB/D1/D2/D3/E/F/G), involved in pre-mRNA splicing, were previously reported in the tumorigenesis of several cancers. However, their specific role in lung adenocarcinoma (LUAD) remains obscure. Our study aims to feature abnormal expressions and mutations of genes for Sm proteins and assess their potential as therapeutic targets via integrated bioinformatics analysis.

Methods: In this research, we explored the expression pattern and prognostic worth of genes for Sm proteins in LUAD across TCGA, GEO, UALCAN, Oncomine, Metascape, David 6.8, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via univariate and multivariate cox regression analysis. Meanwhile, their expression patterns were validated by RT-qPCR. Gene mutations and co-expression of genes for Sm proteins were analyzed by the cBioPortal database. The PPI network for Sm proteins in LUAD was visualized by the STRING and Cytoscape. The correlations between genes for Sm proteins and immune infiltration were analyzed by using the “GSVA” R package.

Results: Sm proteins genes were found upregulated expression in both LUAD tissues and LUAD cell lines. Moreover, highly expressed mRNA levels for Sm proteins were strongly associated with short survival time in LUAD. Genes for Sm proteins were positively connected with the infiltration of Th2 cells, but negatively connected with the infiltration of mast cells, Th1 cells, and NK cells. Importantly, Cox regression analysis showed that high SNRPD1/E/F/G expression were independent risk factors for the overall survival of LUAD.

Conclusion: Our study showed that SNRPD1/E/F/G could independently predict the prognostic outcome of LUAD and was correlated with immune infiltration. Also, this report laid the foundation for additional exploration on the potential treatment target’s role of SNRPD1/E/F/G in LUAD.

Mutations and clinical significance of calcium voltage-gated channel subunit alpha 1E (CACNA1E) in non-small cell lung cancer

CACNA1E is a gene encoding the ion-conducting α1 subunit of R-type voltage-dependent calcium channels, whose roles in tumorigenesis remain to be determined. We previously showed that CACNA1E was significantly mutated in patients with non-small cell lung cancer (NSCLC) who were long-term exposed to household air pollution, with a mutation rate of 19% (15 of 79 cases). Here we showed that CACNA1E was also mutated in 207 (12.8%) of the 1616 patients with NSCLC in The Cancer Genome Atlas (TCGA) datasets. At mRNA and protein levels, CACNA1E was elevated in tumor tissues compared to counterpart non-tumoral lung tissues in NSCLCs of the public datasets and our settings, and its expression level was inversely associated with clinical outcome of the patients. Overexpression of wild type (WT) or A275S or R249G mutant CACNA1E transcripts promoted NSCLC cell proliferation with activation of epidermal growth factor receptor (EGFR) signaling pathway, whereas knockdown of this gene exerted inhibitory effects on NSCLC cells in vitro and in vivo. CACNA1E increased current density and Ca2+ entrance, whereas calcium channel blockers inhibited NSCLC cell proliferation. These data indicate that CACNA1E is required for NSCLC cell proliferation, and blockade of this oncoprotein may have therapeutic potentials for this deadly disease.

Analysis of the effect of NEKs on the prognosis of patients with non-small-cell lung carcinoma based on bioinformatics

NEKs are proteins that are involved in various cell processes and play important roles in the formation and development of cancer. However, few studies have examined the role of NEKs in the development of non-small-cell lung carcinoma (NSCLC). To address this problem, the Oncomine, UALCAN, and the Human Protein Atlas databases were used to analyze differential NEK expression and its clinicopathological parameters, while the Kaplan-Meier, cBioPortal, GEPIA, and DAVID databases were used to analyze survival, gene mutations, similar genes, and biological enrichments. The rate of NEK family gene mutation was high (> 50%) in patients with NSCLC, in which NEK2/4/6/8/ was overexpressed and significantly correlated with tumor stage and nodal metastasis status. In addition, the high expression of NEK2/3mRNA was significantly associated with poor prognosis in patients with NSCLC, while high expression of NEK1/4/6/7/8/9/10/11mRNA was associated with good prognosis. In summary, these results suggest that NEK2/4/6/8 may be a potential prognostic biomarker for the survival of patients with NSCLC.

Construction and Validation of a Combined Ferroptosis and Hypoxia Prognostic Signature for Hepatocellular Carcinoma

Background: Ferroptosis, as a unique programmed cell death modality, has been found to be closely related to the occurrence and development of hepatocellular carcinoma (HCC). Hypoxia signaling pathway has been found to be extensively involved in the transformation and growth of HCC and to inhibit anti-tumor therapy through various approaches. However, there is no high-throughput study to explore the potential link between ferroptosis and hypoxia, as well as their combined effect on the prognosis of HCC.

Methods: We included 370 patients in The Cancer Genome Atlas (TCGA) database and 231 patients in the International Cancer Genome Consortium (ICGC) database. Univariate COX regression and Least Absolute Shrinkage and Selection Operator approach were used to construct ferroptosis-related genes (FRGs) and hypoxia-related genes (HRGs) prognostic signature (FHPS). Kaplan–Meier method and Receiver Operating Characteristic curves were analyzed to evaluate the predictive capability of FHPS. CIBERSOR and single-sample Gene Set Enrichment Analysis were used to explore the connection between FHPS and tumor immune microenvironment. Immunohistochemical staining was used to compare the protein expression of prognostic FRGs and HRGs between normal liver tissue and HCC tissue. In addition, the nomogram was established to facilitate the clinical application of FHPS.

Results: Ten FRGs and HRGs were used to establish the FHPS. We found consistent results in the TCGA training cohort, as well as in the independent ICGC validation cohort, that patients in the high-FHPS subgroup had advanced tumor staging, shorter survival time, and higher mortality. Moreover, patients in the high-FHPS subgroup showed ferroptosis suppressive, high hypoxia, and immunosuppression status. Finally, the nomogram showed a strong prognostic capability to predict overall survival (OS) for HCC patients.

Conclusion: We developed a novel prognostic signature combining ferroptosis and hypoxia to predict OS, ferroptosis, hypoxia, and immune status, which provides a new idea for individualized treatment of HCC patients.

Bimolecular Fluorescence Complementation to Visualize Protein-Protein Interactions in Cells

Examining protein-protein interactions provides critical insight into numerous human diseases and infections. Here we describe a protocol for bimolecular fluorescence complementation, which can be used to directly visualize and characterize intracellular protein-protein interactions and ascertain their localization using fluorescence microscopy.

Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets

Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15-20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.

Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models

Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset's molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, "external" TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models' way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.

Histone deacetylase 6-mediated downregulation of TMEM100 expedites the development and progression of non-small cell lung cancer

The significance of epigenetic modulation, involving acetylation, methylation, as well as ubiquitination has been indicated in the regulation of gene expression and tumor progression. Here, we elucidated the role of histone deacetylase 6 (HDAC6) in regulating epithelial-mesenchymal transition (EMT)-mediated metastasis via mRNA in non-small cell lung cancer (NSCLC). Three microarrays associated with lung cancer metastasis or recurrence, GSE23361, GSE7880 and GSE162102, were downloaded from the GEO database. Transmembrane protein 100 (TMEM100) was revealed to be the only one mRNA that was significantly downregulated in three microarrays. TMEM100, poorly expressed in lung cancer tissues, was associated with poor prognosis of lung cancer patients. Moreover, TMEM100 transcription was regulated by HDAC6 which repressed TMEM100 expression by deacetylation modification on the TMEM100 promoter. Knockdown of HDAC6 or overexpression of TMEM100 in NSCLC cells significantly inhibited TGF-β1-induced EMT and metastasis and suppressed the activation of Wnt/β-catenin signaling pathway. Altogether, our study highlights HDAC6 as a lung cancer metastasis supporter through the suppression of TMEM100 and the induction of Wnt/β-catenin signaling pathway.

Amine oxidase, copper containing 3 exerts anti‑mesenchymal transformation and enhances CD4+ T‑cell recruitment to prolong survival in lung cancer

Lung cancer remains notorious for its poor prognosis. Despite the advent of tyrosine kinase inhibitors and immune checkpoint inhibitors, the probability of curing the disease in lung cancer patients remains low. Novel mechanisms and treatment strategies are needed to provide hope to patients. Advanced strategies of next generation sequencing (NGS) and bioinformatics were used to analyze normal and lung cancer tissues from lung cancer patients. Amine oxidases have been linked to leukocyte migration and tumorigenesis. However, the roles of amine oxidases in lung cancer are not well-understood. Our results indicated that amine oxidase, copper containing 3 (AOC3) was significantly decreased in the tumor tissue compared with the normal tissue, at both the mRNA and protein level, in the included lung cancer patients and public databases. Lower expression of AOC3 conferred a poorer survival probability across the different cohorts. Epigenetic silencing of AOC3 via miR-3691-5p caused tumor promotion and progression by increasing migration and epithelial-mesenchymal transition (EMT). Furthermore, knockdown of AOC3 caused less CD4⁺ T-cell attachment onto lung cancer cells and reduced transendothelial migration in vitro, as well as reducing CD4⁺ T-cell trafficking to the lung in vivo. In conclusion, the present study revealed that downregulation of AOC3 mediated lung cancer promotion and progression, as well as decrease of immune cell recruitment. This novel finding could expand our understanding of the dysregulation of the tumor immune microenvironment and could help to develop a novel strategy for the treatment of lung cancer.

Diagnostic and Prognostic Significance of Keap1 mRNA Expression for Lung Cancer Based on Microarray and Clinical Information from Oncomine Database

We performed a bioinformatics analysis with validation by multiple databases, aiming to evaluate the diagnostic and prognostic value of Kelch-like ECH-associated protein 1 (Keap1) mRNA for lung cancer, and to explore possible mechanisms. Diagnostic performance of Keap1 mRNA was determined by receiver operating characteristic (ROC) curve analysis. Prognostic implication of Keap1 mRNA was estimated by Kaplan-Meier survival analysis. Co-expressed genes with both Keap1 and Nfe2L2 were identified by LinkedOmics. Mechanisms of Keap1-Nfe2L2-co-expressed genes underlying the pathogenesis of lung cancer were explored by function enrichment and pathway analysis. The ROC curve analysis determined a good diagnostic performance of Keap1 mRNA for lung squamous cell carcinoma (LUSC), with an area under the ROC curve (AUC) of 0.833, sensitivity of 72.7%, and specificity of 90.6% (P<0.001). Multivariate Cox regression recognized high Keap1 mRNA to be an independent risk factor of mortality for overall lung cancer [hazard ratio (HR): 11.034, P=0.044], but an independent antagonistic factor for lung adenocarcinoma (LUAD) (HR: 0.404, P<0.001). Validation by UALCAN and GEPIA supported Oncomine findings regarding the diagnostic value of Keap1 mRNA for LUSC, but denied its prognostic value. After screening, we identified 17 co-expressed genes with both Keap1 and Nfe2L2 for LUAD, and 22 for LUSC, mainly enriched in signaling pathway of oxidative stress-induced gene expression via Nrf2. In conclusion, Keap1 mRNA has a good diagnostic performance, but controversial prognostic efficacy for LUSC. The pathogenesis of lung cancer is associated with Keap1-Nfe2L2-co-expressed genes by signaling pathway of oxidative stress-induced gene expression via Nrf2.

Screening of Atherosclerotic Druggable Targets from the Proteome Network of Differentially Expressed Genes

Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein-protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.

Systems Toxicology Approach for Assessing Developmental Neurotoxicity in Larval Zebrafish

Adverse outcomes that result from chemical toxicity are rarely caused by dysregulation of individual proteins; rather, they are often caused by system-level perturbations in networks of molecular events. To fully understand the mechanisms of toxicity, it is necessary to recognize the interactions of molecules, pathways, and biological processes within these networks. The developing brain is a prime example of an extremely complex network, which makes developmental neurotoxicity one of the most challenging areas in toxicology. We have developed a systems toxicology method that uses a computable biological network to represent molecular interactions in the developing brain of zebrafish larvae. The network is curated from scientific literature and describes interactions between biological processes, signaling pathways, and adverse outcomes associated with neurotoxicity. This allows us to identify important signaling hubs, pathway interactions, and emergent adverse outcomes, providing a more complete understanding of neurotoxicity. Here, we describe the construction of a zebrafish developmental neurotoxicity network and its validation by integration with publicly available neurotoxicity-related transcriptomic datasets. Our network analysis identified consistent regulation of tumor suppressors p53 and retinoblastoma 1 (Rb1) as well as the oncogene Krüppel-like factor (Klf8) in response to chemically induced developmental neurotoxicity. The developed network can be used to interpret transcriptomic data in a neurotoxicological context.

Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome

The 3q29 deletion (3q29Del) confers high risk for schizophrenia and other neurodevelopmental and psychiatric disorders. However, no single gene in this interval is definitively associated with disease, prompting the hypothesis that neuropsychiatric sequelae emerge upon loss of multiple functionally-connected genes. 3q29 genes are unevenly annotated and the impact of 3q29Del on the human neural transcriptome is unknown. To systematically formulate unbiased hypotheses about molecular mechanisms linking 3q29Del to neuropsychiatric illness, we conducted a systems-level network analysis of the non-pathological adult human cortical transcriptome and generated evidence-based predictions that relate 3q29 genes to novel functions and disease associations. The 21 protein-coding genes located in the interval segregated into seven clusters of highly co-expressed genes, demonstrating both convergent and distributed effects of 3q29Del across the interrogated transcriptomic landscape. Pathway analysis of these clusters indicated involvement in nervous-system functions, including synaptic signaling and organization, as well as core cellular functions, including transcriptional regulation, posttranslational modifications, chromatin remodeling, and mitochondrial metabolism. Top network-neighbors of 3q29 genes showed significant overlap with known schizophrenia, autism, and intellectual disability-risk genes, suggesting that 3q29Del biology is relevant to idiopathic disease. Leveraging "guilt by association", we propose nine 3q29 genes, including one hub gene, as prioritized drivers of neuropsychiatric risk. These results provide testable hypotheses for experimental analysis on causal drivers and mechanisms of the largest known genetic risk factor for schizophrenia and highlight the study of normal function in non-pathological postmortem tissue to further our understanding of psychiatric genetics, especially for rare syndromes like 3q29Del, where access to neural tissue from carriers is unavailable or limited.

Comprehensive Analysis of Expression and Prognostic Value of GATAs in Lung Cancer

GATAs are a family of transcription factors that play sophisticated and extensive roles in cell fate transitions and tissue morphogenesis during embryonic development. Emerging evidence indicate that GATAs are involved in tumorigenesis of lung cancer (LC). However, the distinct roles, diverse expression patterns and prognostic values of six GATA family members in LC have yet to be elucidated. In the present study, the diverse expression patterns, prognostic values, genetic mutations, protein-protein interaction(PPI) networks of GATAs, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway in LC patients were analyzed using a serious of databases, including ONCOMINE database, Cancer Cell Line Encyclopedia database, the Human Protein Atlas, the Gene Expression Profiling Interactive Analysis database, the Kaplan-Meier plotter, cBioPortal, String database and database Database for Annotation, Visualization, and Integrated Discovery. The mRNA expression levels of GATA1/2/4/5/6 were downregulated, while GATA3 showed abnormal expressions of up-regulation and down-regulation in patients with LC. Aberrant GATAs mRNA expression was connected with prognosis. Furthermore, genetic alterations mainly appeared in GATA4. Gene Ontology enrichment and network analysis demonstrated that GATAs and their 50 interactors were primarily associated with positive regulation of transcription from RNA polymerase II promoter, transcription factor complex, transcription factor binding Jak-STAT signaling pathway. This comprehensive bioinformatic analysis demonstrated that GATA1/2/3/4/6 may be new prognosis factors, and GATA2/5/6 may be potential targets for personalized therapy for patients with LC, but further studies are requisite to analyze the mechanism of their carcinogenicity and investigate novel drug treatment. Finally, these findings would conduce to a better understanding of the unique roles of GATAs in LC.

Comprehensive analysis of prognostic value and immune infiltration of kindlin family members in non-small cell lung cancer

BACKGROUND

Kindlin Family Members have been reported to be aberrantly expressed in various human cancer types and involved in tumorigenesis, tumor progression, and chemoresistance. However, their roles in non-small cell lung cancer (NSCLC) remain poorly elucidated.

METHODS

We analyzed the prognostic value and immune infiltration of Kindlins in NSCLC through Oncomine, GEPIA, UALCAN, CCLE, Kaplan‑Meier plotter, cBioPortal, TIMER, GeneMANIA, STRING, and DAVID database. Additionally, the mRNA expression levels of Kindlins were verified in 30 paired NSCLC tissues and NSCLC cell lines by real-time PCR.

RESULTS

The expression level of FERMT1 was remarkably increased in NSCLC tissues and NSCLC cell lines, while FERMT2 and FERMT3 were reduced. Kindlins expressions were associated with individual cancer stages and nodal metastasis. We also found that higher expression level of FERMT1 was obviously correlated with worse overall survival (OS) in patients with NSCLC, while higher FERMT2 was strongly associated with better overall survival (OS) and first progression (FP). Additionally, the expression of FERMT2 and FERMT3 were obviously correlated with the immune infiltration of diverse immune cells. Functional enrichment analysis has shown that Kindlins may be significantly correlated with intracellular signal transduction, ATP binding and the PI3K-Akt signaling pathway in NSCLC.

CONCLUSIONS

The research provides a new perspective on the distinct roles of Kindlins in NSCLC and likely has important implications for future novel biomarkers and therapeutic targets in NSCLC.

Dynamic roles of inflammasomes in inflammatory tumor microenvironment

The inflammatory tumor microenvironment has been known to be closely connected to all stages of cancer development, including initiation, promotion, and progression. Systemic inflammation in the tumor microenvironment is increasingly being recognized as an important prognostic marker in cancer patients. Inflammasomes are master regulators in the first line of host defense for the initiation of innate immune responses. Inflammasomes sense pathogen-associated molecular patterns and damage-associated molecular patterns, following recruitment of immune cells into infection sites. Therefore, dysregulated expression/activation of inflammasomes is implicated in pathogenesis of diverse inflammatory disorders. Recent studies have demonstrated that inflammasomes play a vital role in regulating the development and progression of cancer. This review focuses on fate-determining roles of the inflammasomes and the principal downstream effector cytokine, IL-1β, in the tumor microenvironment.

Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

The epithelial lining of the small intestine consists of multiple cell types, including Paneth cells and goblet cells, that work in cohort to maintain gut health. 3D in vitro cultures of human primary epithelial cells, called organoids, have become a key model to study the functions of Paneth cells and goblet cells in normal and diseased conditions. Advances in these models include the ability to skew differentiation to particular lineages, providing a useful tool to study cell type specific function/dysfunction in the context of the epithelium. Here, we use comprehensive profiling of mRNA, microRNA and long non-coding RNA expression to confirm that Paneth cell and goblet cell enrichment of murine small intestinal organoids (enteroids) establishes a physiologically accurate model. We employ network analysis to infer the regulatory landscape altered by skewing differentiation, and using knowledge of cell type specific markers, we predict key regulators of cell type specific functions: Cebpa, Jun, Nr1d1 and Rxra specific to Paneth cells, Gfi1b and Myc specific for goblet cells and Ets1, Nr3c1 and Vdr shared between them. Links identified between these regulators and cellular phenotypes of inflammatory bowel disease (IBD) suggest that global regulatory rewiring during or after differentiation of Paneth cells and goblet cells could contribute to IBD aetiology. Future application of cell type enriched enteroids combined with the presented computational workflow can be used to disentangle multifactorial mechanisms of these cell types and propose regulators whose pharmacological targeting could be advantageous in treating IBD patients with Crohn's disease or ulcerative colitis.

Mass spectrometry-based protein‒protein interaction techniques and their applications in studies of DNA damage repair

Proteins are major functional units that are tightly connected to form complex and dynamic networks. These networks enable cells and organisms to operate properly and respond efficiently to environmental cues. Over the past decades, many biochemical methods have been developed to search for protein-binding partners in order to understand how protein networks are constructed and connected. At the same time, rapid development in proteomics and mass spectrometry (MS) techniques makes it possible to identify interacting proteins and build comprehensive protein‒protein interaction networks. The resulting interactomes and networks have proven informative in the investigation of biological functions, such as in the field of DNA damage repair. In recent years, a number of proteins involved in DNA damage response and DNA repair pathways have been uncovered with MS-based protein‒protein interaction studies. As the technologies for enriching associated proteins and MS become more sophisticated, the studies of protein‒protein interactions are entering a new era. In this review, we summarize the strategies and recent developments for exploring protein‒protein interaction. In addition, we discuss the application of these tools in the investigation of protein‒protein interaction networks involved in DNA damage response and DNA repair.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

BACKGROUND

Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment.

RESULTS

The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes - one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups.

CONCLUSION

A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies - non-treatment and treatment - are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Ceramide synthases: insights into the expression and prognosis of lung cancer

CerSs (ceramide synthases), a group of enzymes that catalyze the formation of ceramides from sphingoid base and acyl-CoA substrates. As far, six types of CerSs (CerS1-CerS6) have been found in mammals. Each of these enzymes have unique characteristics, but maybe more noteworthy is the ability of individual CerS isoform to produce a ceramide with a characteristic acyl chain distribution. As key regulators of sphingolipid metabolism, CerSs highlight their unique characteristics and have emerging roles in regulating programmed cell death, cancer and many other aspects of biology. However, the role of CerSs in lung cancer has not been fully elucidated. In this study, there was no significant change in the sequence or copy number of CerSs gene, which could explain the stability of malignant tumor development through COSMIC database. In addition, gene expression in lung cancer was examined using the Oncomine^TM database, and the prognostic value of each gene in non-small cell lung cancer (NSCLC) was analyzed by Kaplan-Meier analysis. The results showed that high mRNA expression levels of CerS2, CerS3, CerS4 and CerS5 in all NSCLC patients were associated with improved prognosis. Among them, CerS2 and CerS5 are also highly expressed in adenocarcinoma (Ade), but not in squamous cell carcinoma (SCC). In contrast, high or low expression of CerS1 and CerS6 no difference was observed in patients with NSCLC, Ade and SCC. Integrated the data of this study suggested that these CerSs may be a potential tumor markers or drug target of new research direction.

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis

BACKGROUND

Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task.

RESULTS

We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches.

CONCLUSIONS

We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity

Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.

Computational medicine: quantitative modeling of complex diseases

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

Transcription levels and prognostic significance of the NFI family members in human cancers

Background

The nuclear factor I (NFI) is a family of transcription factors consisting of four distinct but closely related genes, NFIA, NFIB, NFIC and NFIX, which are important in the development of various tissues and organs in mammals. Recent study results have shown that NFI family may play a critical role in the progression of various human tumors and have been identified as key tumor suppressors and oncogenes for many cancers. However, the expression levels and distinctive prognostic values of the NFI family remain poorly explored in most cancers.

Materials and Methods

In the present study, the differences in mRNA expression of the NFI family in various cancers were investigated using the Oncomine and TCGA databases, and the mRNA expression, genetic alteration and DNA methylation of the NFI family members in various cancers were examined using cBioPortal for Cancer Genomics. In addition, the prognostic significance of the NFI family was assessed in multiple cancers using the Kaplan–Meier plotter (KM plotter) and SurvExpress databases.

Results

The mRNA expression levels in the NFI family were significantly downregulated in most cancers compared with normal tissues and DNA hypermethylation might downregulate the NFI family expression. Although NFIX expression was not downregulated in kidney, colorectal and prostate cancers. Furthermore, NFIB expression was upregulated in gastric cancer. Further survival analyses based on the KM plotter and SurvExpress databases showed dysregulations of the NFI genes were significantly correlated with survival outcomes in breast, lung, and head and neck cancers. Decreased expression levels of NFIA, NFIB and NFIC were associated with poor overall survival (OS) in head and neck cancer. Low mRNA expression of NFIA and NFIB was significantly associated with OS and first progression in lung adenocarcinoma, but not in lung squamous cell carcinoma. In addition, potential correlations between NFI family members and survival outcomes were also observed in liver, esophageal, kidney and cervical cancer.

Conclusion

The results from the present study indicated certain members of the NFI family could be promising therapeutic targets and novel prognostic biomarkers for human cancers.

IRF1 Negatively Regulates Oncogenic KPNA2 Expression Under Growth Stimulation and Hypoxia in Lung Cancer Cells

Purpose

Karyopherin alpha 2 (KPNA2) has been reported as an oncogenic protein in numerous human cancers and is currently considered a potential therapeutic target. However, the transcriptional regulation and physiological conditions underlying KPNA2 expression remain unclear. The aim of the present study was to investigate the role and regulation of interferon regulatory factor-1 (IRF1) in modulating KPNA2 expression in lung adenocarcinoma (ADC).

Materials and methods

Bioinformatics tools and chromatin immunoprecipitation were used to analyze the transcription factor (TF) binding sites in the KPNA2 promoter region. We searched for a potential role of IRF1 in non-small-cell lung cancer (NSCLC) using Oncomine and Kaplan-Meier Plotter datasets. qRT-PCR was applied to examine the role of IRF1 and signaling involved in regulating KPNA2 transcription. Western blotting was used to determine the effects of extracellular stimulation and intracellular signaling on the modulation of KPNA2-related TF expression.

Results

IRF1 was identified as a novel TF that suppresses KPNA2 gene expression. We observed that IRF1 expression was lower in cancerous tissues than in normal lung tissues and that its low expression was correlated with poor prognosis in NSCLC. Notably, both ataxia telangiectasia mutated (ATM) and mechanistic target of rapamycin (mTOR) inhibitors reduced KPNA2 expression, which was accompanied by increased expression of IRF1 but decreased expression of E2F1, a TF that promotes KPNA2 expression in lung ADC cells. IRF1 knockdown restored the reduced levels of KPNA2 in ATM inhibitor-treated cells. We further demonstrated that epidermal growth factor (EGF)-activated mTOR and hypoxia-induced ATM suppressed IRF1 expression but promoted E2F1 expression, which in turn upregulated KPNA2 expression in lung ADC cells.

Conclusion

IRF1 acts as a potential tumor suppressor in NSCLC. EGF and hypoxia promote KPNA2 expression by simultaneously suppressing IRF1 expression and enhancing E2F1 expression in lung ADC cells. Our study provides new insights into targeted therapy for lung cancer.

Sex-specific SNP-SNP interaction analyses within topologically associated domains reveals ANGPT1 as a novel tumor suppressor gene for lung cancer

Genetic interaction has been recognized to be an important cause of the missing heritability. The topologically associating domain (TAD) is a self-interacting genomic region, and the DNA sequences within a TAD physically interact with each other more frequently. Sex differences influence cancer susceptibility at the genetic level. Here, we performed both regular and sex-specific genetic interaction analyses within TAD to identify susceptibility genes for lung cancer in 5204 lung cancer patients and 7389 controls. We found that one SNP pair, rs4262299-rs1654701, was associated with lung cancer in women after multiple testing corrections (combined P = 8.52 × 10^-9 ). Single-SNP analyses did not detect significant association signals for these two SNPs. Both identified SNPs are located in the intron region of ANGPT1. We further found that 5% of nonsmall cell lung cancer patients have an alteration in ANGPT1, indicated the potential role of ANGPT1 in the neoplastic progression in lung cancer. The expression of ANGPT1 was significantly down-regulated in patients in lung squamous cell carcinoma and lung adenocarcinoma. We checked the interaction effect on the ANGPT1 expression and lung cancer and found that the minor allele "G" of rs1654701 increased ANGPT1 gene expression and decreased lung cancer risk with the increased dosage of "A" of rs4262299, which consistent with the tumor suppressor function of ANGPT1. Survival analyses found that the high expression of ANGPT1 was individually associated with a higher survival probability in lung cancer patients. In summary, our results suggest that ANGPT1 may be a novel tumor suppressor gene for lung cancer.

Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID)

Protein-protein interaction data is fundamental in molecular biology, and numerous online databases provide access to this data. However, the huge quantity, complexity, and variety of PPI data can be overwhelming, and rather than helping to address research problems, the data may add to their complexity and reduce interpretability. This protocol focuses on solutions for some of the main challenges of using PPI data, including accessing data, ensuring relevance by integrating useful annotations, and improving interpretability. While the issues are generic, we highlight how to perform such operations using Integrated Interactions Database (IID; http://ophid.utoronto.ca/iid ).

GRWD1 promotes cell proliferation and migration in non-small cell lung cancer by activating the Notch pathway

GRWD1 is a member of the WD repeat protein family that is over-expressed in various cancer cell lines and associated with poor prognosis in patients with cancer. However, its biological function and mechanism in non-small cell lung cancer (NSCLC) remain unclear. In this study, we aimed to elucidate the role of GRWD1 in NSCLC. Immunohistochemistry on tumor specimens from 170 patients showed that GRWD1 is highly expressed in NSCLC tissues and positively correlated with tumor size, lymph node metastasis, and P-TNM stage, but negatively correlated with differentiation and prognosis. We found that GRWD1 promotes cell colony formation by affecting the expression of Cyclin B1, CDK1, and p27 and inducing G2/M transition. GRWD1 was also found to stimulate cell migration through RhoA, RhoC, and CDC42, and induce epithelial-mesenchymal transition by affecting the expression of E-cadherin, N-cadherin, Vimentin, Snail, Zeb1, and ZO-1. Our results indicated that the GRWD1 can activate the Notch signaling pathway by affecting the Notch intracellular domain and promoting the expression of Hes1. Our use of DAPT to suppress Notch signaling confirmed that GRWD1 promotes the progression of NSCLC through the Notch signaling pathway and may be a potential prognostic biomarker and therapeutic target for this disease.

Expression and prognostic analyses of ITGA11, ITGB4 and ITGB8 in human non-small cell lung cancer

Background

Integrins play a crucial role in the regulation process of cell proliferation, migration, differentiation, tumor invasion and metastasis. ITGA11, ITGB4 and ITGB8 are three encoding genes of integrins family. Accumulative evidences have proved that abnormal expression of ITGA11, ITGB4 and ITGB8 are a common phenomenon in different malignances. However, their expression patterns and prognostic roles for patients with non-small cell lung cancer (NSCLC) have not been completely illustrated.

Methods

We investigated the expression patterns and prognostic values of ITGA11, ITGB4 and ITGB8 in patients with NSCLC through using a series of databases and various datasets, including ONCOMINE, GEPIA, HPA, TCGA and GEO datasets.

Results

We found that the expression levels of ITGA11 and ITGB4 were significantly upregulated in both LUAD and LUSC, while ITGB8 was obviously upregulated in LUSC. Additionally, higher expression level of ITGB4 revealed a worse OS in LUAD.

Conclusion

Our findings suggested that ITGA11 and ITGB4 might have the potential ability to act as diagnostic biomarkers for both LUAD and LUSC, while ITGB8 might serve as diagnostic biomarker for LUSC. Furthermore, ITGB4 could serve as a potential prognostic biomarker for LUAD.

Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases

Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario.

RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the "RankerGUI pipeline", a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms' data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

Lower connectivity of tumor coexpression networks is not specific to cancer

Global level network analysis of molecular links is necessary for systems level view of complex diseases like cancer. Using genome-wide expression datasets, we constructed and compared gene co-expression based specific networks of pre-cancerous tumors (adenoma) and cancerous tumors (carcinoma) with paired normal networks to assess for any possible changes in network connectivity. Previously, loss of connectivity was reported as a characteristic of cancer samples. Here, we observed that pre-cancerous conditions also had significantly less connections than paired normal samples. We observed a loss of connectivity trend for colorectal adenoma, aldosterone producing adenoma and uterine leiomyoma. We also showed that the loss of connectivity trend is not specific to positive or negative correlation based networks. Differential hub genes, which were the most highly differentially less connected genes in tumor, were mostly different between different datasets. No common gene list could be defined which underlies the lower connectivity of tumor specific networks. Connectivity of colorectal cancer methylation targets was different from other genes. Extracellular space related terms were enriched in negative correlation based differential hubs and common methylation targets of colorectal carcinoma. Our results indicate a systems level change of lower connectivity as cells transform to not only cancer but also pre-cancerous conditions. This systems level behavior could not be attributed to a group of genes.

Minichromosome maintenance protein 10 as a marker for proliferation and prognosis in lung cancer

DNA replication is a vital process in cell division where anomalies can lead to tumorigenesis. Minichromosome maintenance complex component 10 (MCM10) plays a crucial role in this process. However, the role of MCM10 in lung cancer pathogenesis remains to be elucidated. In current study, using the publicly available lung cancer Gene Expression Omnibus (GEO) datasets, and Oncomine and the Cancer Genome Atlas databases, an increased expression of MCM10 was found in lung cancer tissues compared to normal lung tissues. The high expression of MCM10 was subsequently validated in clinical specimens by reverse transcription‑quantitative PCR and immunohistochemistry. Analysis of the GEO datasets revealed that the high MCM10 expression was significantly associated with early and late recurrence, pathological stage and worse overall survival (OS). Cox's proportional hazards regression analyses revealed that MCM10 expression was an independent risk factor for poor OS and worse recurrence‑free survival both in univariate and multivariate analysis. Furthermore, the increased expression of MCM10 was enriched in cell cycle‑related processes, while in vitro transfection with small interfering RNA targeting MCM10 significantly suppressed cell viability, clone formation and induced G1 phase arrest in A549 and H661 cell lines by regulating the expression of cyclin D1 (CCND1). In addition, the current results indicated a combined effect of MCM10‑CCND1 in predicting the prognosis of lung cancer patients. Altogether, the present study provided a novel potential molecular mechanism of lung cancer progression and may aid in development of novel treatment strategies.

Epigenetic identification of ADCY4 as a biomarker for breast cancer: an integrated analysis of adenylate cyclases

Aim: To explore the role of adenylyl cyclase isoforms and its epigenetics in cancer. Materials & methods: Adenylyl cyclase expression profiles, epigenetic alterations, prognostic value and molecular networks were assessed by use of public omics datasets. Results: ADCY4 was significantly downregulated in breast cancer. This downregulation was associated with promoter hypermethylation. High ADCY4 expression was correlated with better survival of patients with breast cancer and its different intrinsic subtypes and tumor stages. ADCY4 was shown to be strongly associated with G protein coupled receptors and the downstream cAMP signaling pathway, which was also significantly enriched in newly identified lysophosphatidic acid receptor 4 and glucagon-like peptide-1. Conclusion: ADCY4 may be used as an epigenetic biomarker for breast cancer, as well as a possible target for therapy.

Screening and identification of key biomarkers in lung squamous cell carcinoma by bioinformatics analysis

The high mortality rate of lung squamous cell carcinoma (LUSC) is in part due to the lack of early detection of its biomarkers. The identification of key molecules involved in LUSC is therefore required to improve clinical diagnosis and treatment outcomes. The present study used the microarray datasets GSE31552, GSE6044 and GSE12428 from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to construct the protein-protein interaction network of DEGs and hub genes module using STRING and Cytoscape. The 67 DEGs identified consisted of 42 upregulated genes and 25 downregulated genes. The pathways predicted by KEGG and GO enrichment analyses of DEGs mainly included cell cycle, cell proliferation, glycolysis or gluconeogenesis, and tetrahydrofolate metabolic process. Further analysis of the University of California Santa Cruz and ONCOMINE databases identified 17 hub genes. Overall, the present study demonstrated hub genes that were closely associated with clinical tissue samples of LUSC, and identified TYMS, CCNB2 and RFC4 as potential novel biomarkers of LUSC. The findings of the present study contribute to an improved understanding of the molecular mechanisms of carcinogenesis and progression of LUSC, and assist with the identification of potential diagnostic and therapeutic targets of LUSC.

Uridine-cytidine kinase 2 (UCK2): A potential diagnostic and prognostic biomarker for lung cancer

Lung cancer has the highest morbidity and mortality among all cancers. Discovery of early diagnostic and prognostic biomarkers of lung cancer can greatly facilitate the survival rate and reduce its mortality. In our study, by analyzing Gene Expression Omnibus and Oncomine databases, we found a novel potential oncogene uridine‐cytidine kinase 2 (UCK2), which was overexpressed in lung tumor tissues compared to adjacent nontumor tissues or normal lung. Then we confirmed this finding in clinical samples. Specifically, UCK2 was identified as highly expressed in stage IA lung cancer with a high diagnostic accuracy (area under the receiver operating characteristic curve > 0.9). We also found that high UCK2 expression was related to poorer clinicopathological features, such as higher T stage and N stage and higher probability of early recurrence. Furthermore, we found that patients with high UCK2 expression had poorer first progression survival and overall survival than patients with low UCK2 expression. Univariate and multivariate Cox regression analyses showed that UCK2 was an independent risk factor related with worse DFS and OS. By gene set enrichment analysis, tumor‐associated biological processes and signaling pathways were enriched in the UCK2 overexpression group, which indicated that UCK2 might play a vital role in lung cancer. Furthermore, in cytology experiments, we found that knockdown of UCK2 could suppress the proliferation and migration of lung cancer cells. In conclusion, our study indicated that UCK2 might be a potential early diagnostic and prognostic biomarker for lung cancer.