Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied.

METHODS

We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis.

CONCLUSION

We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.

Identification and prognostic analysis of ferroptosis‑related gene HSPA5 to predict the progression of lung squamous cell carcinoma

Ferroptosis, an iron-dependent form of regulated cell death driven by excessive lipid peroxidation, is implicated in the development and therapeutic responses of cancer. However, the role of ferroptosis-related gene profiles in lung squamous cell carcinoma (LSCC) remains largely unknown. The present study aimed to identify the prognostic roles of ferroptosis-related genes in LSCC. Sequencing data from the Cancer Genome Atlas were analyzed and ferroptosis-related gene expression between tumor and para-tumor tissue was identified. The prognostic role of these genes was also assessed using Kaplan-Meier analyses and univariate and multivariate Cox proportional hazards regression model analyses. Immunological correlation, tumor stemness, drug sensitivity and the transcriptional differences of heat shock protein (HSP)A5 in LSCC were also analyzed. Thereafter, the expression of HSPA5 in 100 patients with metastatic LSCC was evaluated using immunohistochemistry (IHC) and the clinical significance of these markers with different risk factors was assessed. Of the 22 ferroptosis-related genes, the expression of HSPA5, HSPB1, glutathione peroxidase 4, Fanconi anemia complementation group D2, CDGSH iron sulfur domain 1, farnesyl-diphosphate farnesyltransferase 1, nuclear factor erythroid 2 like 2, solute carrier (SLC)1A5, ribosomal protein L8, nuclear receptor coactivator 4, transferrin receptor and SLC7A11 was significantly increased in LSCC compared with adjacent tissues. However, only high expression of HSPA5 was able to predict progression-free survival (PFS) and disease-free survival in LSCC. Although HSPA5 was also significantly elevated in patients with lung adenocarcinoma, HSPA5 expression did not predict the prognosis of patients with lung adenocarcinoma. Of note, a higher expression of HSPA5 was related to higher responses to chemotherapy but not to immunotherapy. In addition, HSPA5 expression was positively correlated with 'ferroptosis', 'cellular responses to hypoxia', 'tumor proliferation signature', 'G2M checkpoint', 'MYC targets' and 'TGFB'. IHC analysis also demonstrated that a high expression of HSPA5 in patients with metastatic LSCC in the study cohort was associated with shorter PFS and overall survival. In conclusion, the present study demonstrated that the expression of the ferroptosis-related gene HSPA5 may be a negative prognostic marker for LSCC.

Tissue-specific biological aging predicts progression in prostate cancer and acute myeloid leukemia

Introduction

Chronological aging is a well-recognized diagnostic and prognostic factor in multiple cancer types, yet the role of biological aging in manifesting cancer progression has not been fully explored yet.

Methods

Given the central role of chronological aging in prostate cancer and AML incidence, here we investigate a tissue-specific role of biological aging in prostate cancer and AML progression. We have employed Cox proportional hazards modeling to associate biological aging genes with cancer progression for patients from specific chronological aging groups and for patients with differences in initial cancer aggressiveness.

Results

Our prostate cancer-specific investigations nominated four biological aging genes (CD44, GADD45B, STAT3, GFAP) significantly associated with time to disease progression in prostate cancer in Taylor et al. patient cohort. Stratified survival analysis on Taylor dataset and validation on an independent TCGA and DKFZ PRAD patient cohorts demonstrated ability of these genes to predict prostate cancer progression, especially for patients with higher Gleason score and for patients younger than 60 years of age. We have further tested the generalizability of our approach and applied it to acute myeloid leukemia (AML). Our analysis nominated three AML-specific biological aging genes (CDC42EP2, CDC42, ALOX15B) significantly associated with time to AML overall survival, especially for patients with favorable cytogenetic risk score and for patients older than 56 years of age.

Discussion

Comparison of the identified PC and AML markers to genes selected at random and to known markers of progression demonstrated robustness of our results and nominated the identified biological aging genes as valuable markers of prostate cancer and AML progression, opening new avenues for personalized therapeutic management and potential novel treatment investigations.

Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases

High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.

Abnormal Gene Expression Regulation Mechanism of Myeloid Cell Nuclear Differentiation Antigen in Lung Adenocarcinoma

Lung adenocarcinoma (LA) is the main pathological type of lung cancer with a very low 5-year survival rate. In the present study, after downloading the mRNA, miRNA, and DNA methylation sequencing data from TCGA, combined with the downloaded clinical data, comparative analysis, prognostic analysis, GO and KEGG analysis, GSEA analysis, methylation analysis, transcriptional regulation and post-transcriptional regulation were performed. We found that both methylation and gene expression of MNDA in LA were down-regulated, while high expression of MNDA was associated with good overall survival in LA. To probe the mechanism, further analysis showed that SPI1 was the main transcription factor of MNDA, but it was also down-regulated in LA. At the same time, the expression of eight target miRNAs of MNDA was significantly up-regulated, and the expression of hsa-miR-33a-5p and hsa-miR-33b-5p were verified to directly target MNDA. In conclusion, the abnormal expression of MNDA in LA is the result of the combined effects of transcriptional and post-transcriptional regulation.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma

BACKGROUND

Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient.

PURPOSE

To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model.

STUDY TYPE

Retrospective.

POPULATION

A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts.

FIELD STRENGTH/SEQUENCE

T₂ -weighted imaging, contrast enhanced-T₁ -weighted imaging using fast spin echo sequences at 1.5 T scanner.

ASSESSMENT

Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Model^clinic ), radiomics features (Model^radiomics ), and clinical factors + radiomics signatures using logistic (Model^combined ), and BPNN (Model^BPNN ) methods were established, and model performances were compared.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance.

RESULTS

Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR = 2.883; 95% CI, 1.364-6.745), and T stage (OR = 1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. Model^BPNN demonstrated the highest performance among Model^combined , Model^radiomics , and Model^clinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695).

CONCLUSION

A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Multiomics Identification of Potential Targets for Alzheimer Disease and Antrocin as a Therapeutic Candidate

Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the development and progression of dementia remain poorly understood. Therefore, identifying essential genes and molecular pathways that are associated with this disease’s pathogenesis will help uncover potential treatments. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of AD, we integrated the differentially expressed genes (DEGs) from six microarray datasets of AD patients and controls. We identified ATPase H+ transporting V1 subunit A (ATP6V1A), BCL2 interacting protein 3 (BNIP3), calmodulin-dependent protein kinase IV (CAMK4), TOR signaling pathway regulator-like (TIPRL), and the translocase of outer mitochondrial membrane 70 (TOMM70) as upregulated DEGs common to the five datasets. Our analyses revealed that these genes exhibited brain-specific gene co-expression clustering with OPA1, ITFG1, OXCT1, ATP2A2, MAPK1, CDK14, MAP2K4, YWHAB, PARK2, CMAS, HSPA12A, and RGS17. Taking the mean relative expression levels of this geneset in different brain regions into account, we found that the frontal cortex (BA9) exhibited significantly (p < 0.05) higher expression levels of these DEGs, while the hippocampus exhibited the lowest levels. These DEGs are associated with mitochondrial dysfunction, inflammation processes, and various pathways involved in the pathogenesis of AD. Finally, our blood–brain barrier (BBB) predictions using the support vector machine (SVM) and LiCABEDS algorithm and molecular docking analysis suggested that antrocin is permeable to the BBB and exhibits robust ligand–receptor interactions with high binding affinities to CAMK4, TOMM70, and T1PRL. Our results also revealed good predictions for ADMET properties, drug-likeness, adherence to Lipinskís rules, and no alerts for pan-assay interference compounds (PAINS) Conclusions: These results suggest a new molecular signature for AD parthenogenesis and antrocin as a potential therapeutic agent. Further investigation is warranted.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor.

OBJECTIVE

This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network.

METHODS

The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software.

RESULTS

A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis.

CONCLUSION

We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

mTOR/EGFR/iNOS/MAP2K1/FGFR/TGFB1 Are Druggable Candidates for N-(2,4-Difluorophenyl)-2',4'-Difluoro-4-Hydroxybiphenyl-3-Carboxamide (NSC765598), With Consequent Anticancer Implications

Background

The application of computational and multi-omics approaches has aided our understanding of carcinogenesis and the development of therapeutic strategies. NSC765598 is a novel small molecule derivative of salicylanilide. This study aims to investigate the ligand-protein interactions of NSC765598 with its potential targets and to evaluate its anticancer activities in vitro.

Methods

We used multi-computational tools and clinical databases, respectively, to identify the potential drug target for NSC765598 and analyze the genetic profile and prognostic relevance of the targets in multiple cancers. We evaluated the in vitro anticancer activities against the National Cancer Institute 60 (NCI60) human tumor cell lines and used molecular docking to study the ligand-protein interactions. Finally, we used the DTP-COMPARE algorithm to compare the NSC765598 anticancer fingerprints with NCI standard agents.

Results

We identified mammalian target of rapamycin (mTOR)/epidermal growth factor receptor (EGFR)/inducible nitric oxide synthase (iNOS)/mitogen-activated protein 2 kinase 1 (MAP2K1)/fibroblast growth factor receptor (FGFR)/transforming growth factor-β1 (TGFB1) as potential targets for NSC765598. The targets were enriched in cancer-associated pathways, were overexpressed and were of prognostic relevance in multiple cancers. Among the identified targets, genetic alterations occurred most frequently in EGFR (7%), particularly in glioblastoma, esophageal squamous cell cancer, head and neck squamous cell cancer, and non–small-cell lung cancer, and were associated with poor prognoses and survival of patients, while other targets were less frequently altered. NSC765598 displayed selective antiproliferative and cytotoxic preferences for NSCLC (50% growth inhibition (GI₅₀) = 1.12–3.95 µM; total growth inhibition (TGI) = 3.72–16.60 μM), leukemia (GI₅₀ = 1.20–3.10 µM; TGI = 3.90–12.70 μM), melanoma (GI₅₀ = 1.45–3.59 µM), and renal cancer (GI₅₀ = 1.38–3.40 µM; TGI = 4.84–13.70 μM) cell lines, while panels of colon, breast, ovarian, prostate, and central nervous system (CNS) cancer cell lines were less sensitive to NSC765598. Interestingly, NSC765598 docked well into the binding cavity of the targets by conventional H-bonds, van der Waal forces, and a variety of π-interactions, with higher preferences for EGFR (ΔG = −11.0 kcal/mol), NOS2 (ΔG = −11.0 kcal/mol), and mTOR (ΔG = −8.8 kcal/mol). NSC765598 shares similar anti-cancer fingerprints with NCI standard agents displayed acceptable physicochemical values and met the criteria of drug-likeness.

Conclusion

NSC765598 displayed significant anticancer and potential multi-target properties, thus serve as a novel candidate worthy of further preclinical studies.

Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.Abbreviations UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein-Protein Interaction.

Integrated Bioinformatics Analysis Exhibits Pivotal Exercise-Induced Genes and Corresponding Pathways in Malignant Melanoma

Malignant melanoma represents a sort of neoplasm deriving from melanocytes or cells developing from melanocytes. The balance of energy and energy-associated body composition and body mass index could be altered by exercise, thereby directly affecting the microenvironment of neoplasm. However, few studies have examined the mechanism of genes induced by exercise and the pathways involved in melanoma. This study used three separate datasets to perform comprehensive bioinformatics analysis and then screened the probable genes and pathways in the process of exercise-promoted melanoma. In total, 1,627 differentially expressed genes (DEGs) induced by exercise were recognized. All selected genes were largely enriched in NF-kappa B, Chemokine signaling pathways, and the immune response after gene set enrichment analysis. The protein-protein interaction network was applied to excavate DEGs and identified the most relevant and pivotal genes. The top 6 hub genes (Itgb2, Wdfy4, Itgam, Cybb, Mmp2, and Parp14) were identified, and importantly, 5 hub genes (Itgb2, Wdfy4, Itgam, Cybb, and Parp14) were related to weak disease-free survival and overall survival (OS). In conclusion, our findings demonstrate the prognostic value of exercise-induced genes and uncovered the pathways of these genes in melanoma, implying that these genes might act as prognostic biomarkers for melanoma.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Carcinoma diagnosis and prognosis are still hindered by the lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method's robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic least absolute shrinkage and selection operator (LASSO) regression method [the averaged 1- to 4-years predictive area under the receiver operating characteristic curve (AUC) measure, 0.6998], the proposed one outperforms more accurately by 0.7208 with fewer predictors, particularly its averaged 1- to 3-years AUC reaches 0.723, vs. classic 0.6917 on The Cancer Genome Atlas (TCGA). In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results, are available at https://github.com/gladex/PM-FSOR.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary

Metastatic cervical carcinoma from unknown primary (MCCUP) accounts for 1–4% of all head and neck tumors, and identifying the primary site in MCCUP is challenging. The most common histopathological type of MCCUP is squamous cell carcinoma (SCC), and it remains difficult to identify the primary site pathologically. Therefore, it seems necessary and urgent to develop novel and effective methods to determine the primary site in MCCUP. In the present study, the RNA sequencing data of four types of SCC and Pan-Cancer from the cancer genome atlas (TCGA) were obtained. And after data pre-processing, their differentially expressed genes (DEGs) were identified, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these significantly changed genes of four types of SCC share lots of similar molecular functions and histological features. Then three machine learning models, [Random Forest (RF), support vector machine (SVM), and neural network (NN)] which consisted of ten genes to distinguish these four types of SCC were developed. Among the three models with prediction tests, the RF model worked best in the external validation set, with an overall predictive accuracy of 88.2%, sensitivity of 88.71%, and specificity of 95.42%. The NN model is the second in efficacy, with an overall accuracy of 82.02%, sensitivity of 81.23%, and specificity of 93.04%. The SVM model is the last, with an overall accuracy of 76.69%, sensitivity of 74.81%, and specificity of 90.84%. The present analysis of similarities and differences among the four types of SCC, and novel models developments for distinguishing four types of SCC with informatics methods shed lights on precision MCCUP diagnosis in the future.

Scutellaria baicalensis Flavones as Potent Drugs against Acute Respiratory Injury during SARS-CoV-2 Infection: Structural Biology Approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in severe damage to the respiratory system. With no specific treatment to date, it is crucial to identify potent inhibitors of SARS-CoV-2 Chymotrypsin-like protease (3CLpro) that could also modulate the enzymes involved in the respiratory damage that accompanies SARS-CoV-2 infection. Here, flavones isolated from Scutellaria baicalensis (baicalein, baicalin, wogonin, norwogonin, and oroxylin A) were studied as possible compounds in the treatment of SARS-CoV-2 and SARS-CoV-2-induced acute lung injuries. We used structural bioinformatics and cheminformatics to (i) identify the critical molecular features of flavones for their binding activity at human and SARS-CoV-2 enzymes; (ii) predict their drug-likeness and lead-likeness features; (iii) calculate their pharmacokinetic profile, with an emphasis on toxicology; (iv) predict their pharmacodynamic profiles, with the identification of their human body targets involved in the respiratory system injuries; and (v) dock the ligands to SARS-CoV-2 3CLpro. All flavones presented appropriate drug-like and kinetics features, except for baicalin. Flavones could bind to SARS-CoV-2 3CLpro at a similar site, but interact slightly differently with the protease. Flavones’ pharmacodynamic profiles predict that (i) wogonin strongly binds at the cyclooxygenase2 and nitric oxide synthase; (ii) baicalein and norwogonin could modulate lysine-specific demethylase 4D-like and arachidonate 15-lipoxygenase; and (iii) baicalein, wogonin, norwogonin, and oroxylin A bind to SARS-CoV-2 3CLpro. Our results propose these flavones as possible potent drugs against respiratory damage that occurs during SARS-CoV-2 infections, with a strong recommendation for baicalein.

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

Integration of gene profile to explore the hub genes of lung adenocarcinoma: A quasi-experimental study

BACKGROUND

Lung cancer is a leading cause of morbidity diseases worldwide, but the key mechanisms of lung cancer remain elusive. This study aims to integrate of GSE 118370 and GSE 32863 profile and identify the key genes and pathway involved in human lung adenocarcinoma.

METHODS

R software (RStudio, Version info: R 3.2.3, Forrester, USA) were utilized to find the differentially expressed genes. All the differentially expressed genes were analyzed by gene ontology, kyoto encyclopedia of genes and genomes. Protein-protein interaction networks were constructed by STRING database and analyzed by Cytohubber and Module. The cancer genome atlas database was used to verification the expression of hub genes. Quantitative reverse transcription-PCR was used to verify the bio-information results.

RESULTS

Sixty-four lung adenocarcinoma and 64 adjacent normal tissues were used for integration analysis. Five hundred ninety-nine co-expression genes were locked. Biological processes mainly enriched in angiogenesis. Cellular component focused on extracellular exosome and molecular function aimed on protein disulfide isomerase activity. Cytohubber analysis showed that GNG11, FPR2, P4HB, PIK3R1, CDC20, ADCY4, TIMP1, IL6, CXC chemokine ligand (CXCL)12, and GAS6 acted as the hub genes during lung adenocarcinoma. Module analysis presented Chemokine signaling pathway was a key pathway. Quantitative reverse transcription-PCR showed that the expression level of GNG11, FPR2, PIK3R1, ADCY4, IL6, CXCL12, and GAS6 were significantly decreased and P4HB, CDC20 and TIMP1 were increased in human adenocarcinoma tissues (P < .05). The cancer genome atlas online analysis showed GNG11 was not associated with survival.

CONCLUSIONS

This study firstly reported GNG11 acting as a hub gene in adenocarcinoma. GNG11 could be used as a biomarker for human adenocarcinoma. Chemokine signaling pathway might play important roles in lung adenocarcinoma.

Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

Analysis of circulating miRNAs (cmiRNAs) before surgical operation (BSO) and after the surgical operation (ASO) has been informative for lung adenocarcinoma (LUAD) diagnosis, progression, and outcomes of treatment. Thus, we performed a biological network analysis to identify the potential target genes (PTGs) of the overexpressed cmiRNA signatures from LUAD samples that had undergone surgical therapy. Differential expression (DE) analysis of microarray datasets, including cmiRNAs (GSE137140) and cmRNAs (GSE69732), was conducted using the Limma package. cmiR-1246 was predicted as a significantly upregulated cmiRNA of LUAD samples BSO and ASO. Then, 9802 miR-1246 target genes (TGs) were predicted using 12 TG prediction platforms (MiRWalk, miRDB, and TargetScan). Briefly, 425 highly expressed overlapping miRNA-1246 TGs were observed between the prediction platform and the cmiRNA dataset. ClueGO predicted cell projection morphogenesis, chemosensory behavior, and glycosaminoglycan binding, and the PI3K-Akt signaling pathways were enriched metabolic interactions regulating miRNA-1245 overlapping TGs in LUAD. Using 425 overlapping miR-1246 TGs, a protein-protein interaction network was constructed. Then, 12 PTGs of three different Walktrap modules were identified; among them, ubiquitin-conjugating enzyme E2C (UBE2C), troponin T1(TNNT1), T-cell receptor alpha locus interacting protein (TRAIP), and ubiquitin c-terminal hydrolase L1(UCHL1) were positively correlated with miR-1246, and the high expression of these genes was associated with better overall survival of LUAD. We conclude that PTGs of cmiRNA-1246 and key pathways, namely, ubiquitin-mediated proteolysis, glycosaminoglycan binding, the DNA metabolic process, and the PI3K-Akt-mTOR signaling pathway, the neurotrophin and cardiomyopathy signaling pathway, and the MAPK signaling pathway provide new insights on a noninvasive prognostic biomarker for LUAD.

Novel module and hub genes of distinctive breast cancer associated fibroblasts identified by weighted gene co-expression network analysis

BACKGROUND

As abundant and heterogeneous stromal cells in tumor microenvironment, carcinoma-associated fibroblasts (CAFs) are critically involved in cancer progression.

METHODS

To identify co-expression module and hub genes of distinctive breast CAFs, weighted gene co-expression network analysis (WGCNA) was conducted based on the expression array results of CAFs from seven chemo-sensitive breast cancer (BC) patients and seven chemo-resistant ones before neo-adjuvant chemotherapy.

RESULTS

A total of 4916 genes were included in WGCNA, and 12 modules were determined. Module-trait assay showed that the blue module (cor = 0.97, P < 0.001) was associated with CAF-related chemo-resistance, which was enriched mainly as "inflammatory response", "interferon-gamma-mediated signaling" and "NIK/NF-kappaB signaling" pathways. Moreover, CXCL8, CXCL10, CXCL11, PLSCR1, RIPK2 and USP18 were found to be potentially associated with chemo-resistance related to CAFs and prognosis of BC.

CONCLUSIONS

Our current data offered valuable insights into the molecular mechanisms of distinctive breast CAFs, which was beneficial for revealing how chemo-resistance of BC was initiated.

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

A-CaMP: a tool for anti-cancer and antimicrobial peptide generation

Anti-cancer peptides (ACPs) play a vital role in the cell signaling process. Antimicrobial peptides (AMPs) provide immunity against pathogenic microbes, AMPs present activity against pathogenic microbes. Some of them are known to possess both anticancer and antimicrobial activity. However, so far, no tools have been developed that could predict potential ACPs from wild and mutated cancerous protein sequences in the numerous public databases. In the present study, we developed a A-CaMP tool that allows rapid fingerprinting of the anti-cancer and antimicrobial peptides, which play a crucial role in current bioinformatics research. Besides, we compared the performance and functionality of our A-CaMP tool with those of other methods available online. A-CaMP scans the target protein sequences provided by the user against the datasets. It possesses a robust coding architecture, has been developed in PERL language and is scalable of therefore has extensive applications in bioinformatics. It was observed to achieve a prediction accuracy of 93.4%, which is much higher than that of any of the existing tools. Sequence alignment studies also highlight the potential use of A-CaMP as a tool for the identification of AMPs. A-CaMP is the first open source tool that uses clinical data and proposes final peptides along with the necessary information; this includes wild and mutant sequence and peptides, which lays the foundation for its application in therapies for cancer and bacterial infections. Communicated by Ramaswamy H. Sarma.

Integrated PPI- and WGCNA-Retrieval of Hub Gene Signatures Shared Between Barrett's Esophagus and Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC) is a deadly cancer with high mortality rate, especially in economically advanced countries, while Barrett's esophagus (BE) is reported to be a precursor that strongly increases the risk of EAC. Due to the complexity of these diseases, their molecular mechanisms have not been revealed clearly. This study aims to explore the gene signatures shared between BE and EAC based on integrated network analysis. We obtained EAC- and BE-associated microarray datasets GSE26886, GSE1420, GSE37200, and GSE37203 from the Gene Expression Omnibus and ArrayExpress using systematic meta-analysis. These data were accompanied by clinical data and RNAseq data from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to explore the relationship between gene sets and clinical traits as well as to discover the key relationships behind the co-expression modules. A differentially expressed gene-based protein-protein interaction (PPI) complex was used to extract hub genes through Cytoscape plugins. As a result, 403 DEGs were excavated, comprising 236 upregulated and 167 downregulated genes, which are involved in the cell cycle and replication pathways. Forty key genes were identified using modules of MCODE, CytoHubba, and CytoNCA with different algorithms. A dark-gray module with 207 genes was identified which having a high correlation with phenotype (gender) in the WGCNA. Furthermore, five shared hub gene signatures (SHGS), namely, pre-mRNA processing factor 4 (PRPF4), serine and arginine-rich splicing factor 1 (SRSF1), heterogeneous nuclear ribonucleoprotein M (HNRNPM), DExH-Box Helicase 9 (DHX9), and origin recognition complex subunit 2 (ORC2), were identified between BE and EAC. SHGS enrichment denotes that RNA metabolism and splicosomes play a key role in esophageal cancer development and progress. We conclude that the PPI complex and WGCNA co-expression network highlight the importance of phenotypic identifying hub gene signatures for BE and EAC.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Viewing the Emphasis on State-of-the-Art Magnetic Nanoparticles: Synthesis, Physical Properties, and Applications in Cancer Theranostics

Cancer-related mortality is a leading cause of death among both men and women around the world. Target-specific therapeutic drugs, early diagnosis, and treatment are crucial to reducing the mortality rate. One of the recent trends in modern medicine is "Theranostics," a combination of therapeutics and diagnosis. Extensive interest in magnetic nanoparticles (MNPs) and ultrasmall superparamagnetic iron oxide nanoparticles (NPs) has been increasing due to their biocompatibility, superparamagnetism, less-toxicity, enhanced programmed cell death, and auto-phagocytosis on cancer cells. MNPs act as a multifunctional, noninvasive, ligand conjugated nano-imaging vehicle in targeted drug delivery and diagnosis. In this review, we primarily discuss the significance of the crystal structure, magnetic properties, and the most common method for synthesis of the smaller sized MNPs and their limitations. Next, the recent applications of MNPs in cancer therapy and theranostics are discussed, with certain preclinical and clinical experiments. The focus is on implementation and understanding of the mechanism of action of MNPs in cancer therapy through passive and active targeting drug delivery (magnetic drug targeting and targeting ligand conjugated MNPs). In addition, the theranostic application of MNPs with a dual and multimodal imaging system for early diagnosis and treatment of various cancer types including breast, cervical, glioblastoma, and lung cancer is reviewed. In the near future, the theranostic potential of MNPs with multimodality imaging techniques may enhance the acuity of personalized medicine in the diagnosis and treatment of individual patients.