Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis

Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model

The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan-Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.

Alfalfa xenomiR-162 targets G protein subunit gamma 11 to regulate milk protein synthesis in bovine mammary epithelial cells

OBJECTIVE

It was shown that microRNAs (miRNAs) play an important role in milk protein synthesis. However, the post-transcriptional regulation of casein expression by exogenous miRNA (xeno-miRNAs) in ruminants remains unclear. This study explores the regulatory roles of alfalfa xeno-miR162 on casein synthesis in bovine mammary epithelial cells (bMECs).

METHODS

The effects of alfalfa xenomiR-162 and G protein subunit gamma 11 (GNG11) on proliferation and milk protein metabolism of bMECs were detected by 5-Ethynyl-2'-Deoxyuridine (EdU) staining, flow cytometry, cell counting kit-8 (CCK-8), enzyme-linked immunosorbent assay, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blot. Dual-luciferase reporter assay was used to verify the targeting relationship between GNG11 and xenomiR-162.

RESULTS

Results showed that over-expression of xenomiR-162 inhibited cell proliferation but promoted apoptosis, which also up-regulated the expression of several casein coding genes, including CSN1S1, CSN1S2, and CSN3, while decreasing the expression of CSN2. Furthermore, the targeting relationship between GNG11 and xenomiR-162 was determined, and it was confirmed that GNG11 silencing also inhibited cell proliferation but promoted apoptosis and reduced the expression of casein coding genes and genes related to the mammalian target of rapamycin (mTOR) pathway.

CONCLUSION

Alfalfa xenomiR-162 appears to regulate bMECs proliferation and milk protein synthesis via GNG11 in the mTOR pathway, suggesting that this xeno-miRNA could be harnessed to modulate CSN3 expression in dairy cows, and increase κ-casein contents in milk.

Association of immunotoxicological indices with lung cancer biomarkers in poultry, grape, and rose farming workers

Exposure to occupational hazards like dust, pesticides, diesel emission particles, or physical hazards in the agricultural sector is known to cause adverse health effects on farm workers. Our study aimed at addressing the association of immunomodulatory status with plasma levels of lung cancer biomarkers in farming population, attempting to recognition of vulnerable farming group. Blood samples from apparently healthy 51 chicken husbandry, 19 grape orchard, and 21 rose greenhouse workers were subjected to evaluate plasma levels of two representative lung cancer biomarkers, pro-gastrin releasing peptide (Pro-GRP) and cytokeratin fragment 19 (CYFRA 21-1). Peripheral blood mononuclear cells obtained from farmers were used for natural killer (NK) cell phenotyping and cytokines (interferon-gamma, IFN-γ and interleukin-13, IL-13) profiling in the culture supernatant. Compared to the rose greenhouse farmers, the grape orchard and chicken husbandry workers revealed a significantly upregulated plasma Pro-GRP and CYFRA 21-1 level. A low proportion of NK cells was observed among the female grape orchard workers and a lowered IFN- γ:IL-13 ratio was seen in the grape and chicken husbandry workers than the rose workers. Our findings imply that grape orchard and chicken husbandry workers have more disturbed immune homeostasis implicated with augmentation in the levels of lung cancer biomarkers than the rose greenhouse workers.

Exploring potential genes and pathways related to lung cancer: a graph theoretical analysis

Lung cancer is the primary and third most frequently detected form of cancer in both males and females. The present study tries to perform integrated analysis in male as well as female patients inclusively both smoker and non-smokers. This study aims to identify diagnostic biomarkers and therapeutic targets for lung cancer patients using human microarray profile datasets. Differentially expressed genes (DEGs) were identified using a PPI network from the String database, and major modules or clusters were extracted using MCODE. The Cytohubba plug-in was used to find hub genes from the PPI network using centralities approaches. Twenty significant hub genes (CCND1, CDK1, CCNB1, CDH1, TP53, CTNNB1, EGFR, ESR1, CDK2, CCNA2, RHOA, EGF, FN1, HSP90AA1, STAT3, JUN, NOTCH1, IL6, SRC, and CD44) were identified as promising diagnostic biomarkers and therapeutic targets for lung cancer treatment. Survival analysis and hub gene validation were also conducted. GO enrichment and pathway analysis were conducted to identify their important functions. These hub genes were also used to identify targeted drugs. The findings suggest that the identified genes have the potential to be used as diagnostic biomarkers and therapeutic targets for lung cancer treatment.

A Functional Genomics Review of Non-Small-Cell Lung Cancer in Never Smokers

There is currently a dearth of information regarding lung cancer in never smokers (LCINS). Additionally, there is a difference in somatic mutations, tumour mutational burden, and chromosomal aberrations between smokers and never smokers (NS), insinuating a different disease entity in LCINS. A better understanding of actionable driver alterations prevalent in LCINS and the genomic landscape will contribute to identifying new molecular targets of relevance for NS that will drastically improve outcomes. Differences in treatment outcomes between NS and smokers, as well as sexes, with NSCLC suggest unique tumour characteristics. Epidermal growth factor receptor (EGFR) tyrosine kinase mutations and echinoderm microtubule-associated protein-like 4 anaplastic lymphoma kinase (EML4-ALK) gene rearrangements are more common in NS and have been associated with chemotherapy resistance. Moreover, NS are less likely to benefit from immune mediators including PD-L1. Unravelling the genomic and epigenomic underpinnings of LCINS will aid in the development of not only novel targeted therapies but also more refined approaches. This review encompasses driver genes and pathways involved in the pathogenesis of LCINS and a deeper exploration of the genomic landscape and tumour microenvironment. We highlight the dire need to define the genetic and environmental aspects entailing the development of lung cancer in NS.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

Identification of a novel survival predictor, CSF2RB, for female lung cancer in never smokers (LCNS) by a bioinformatics analysis

Lung cancer in never smokers (LCNS) has been considered as a separate disease and the 7th cause of cancer-related death worldwide. However, limited research has focused on "female" cohorts, which have presented a higher incidence rate. In this study, the microarray data of lung cancer tissues derived from 54 female lung cancer patients, consisting of 43 nonsmokers and 11 smokers, were selected from GSE2109 dataset. A total of 249 differentially expressed genes (DEGs) including 102 up- and 147 down-regulated genes were identified and further analyzed for gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. By constructing protein-protein interaction (PPI) network and calculating key modules, 10 hub genes were screened out. The module analysis of the PPI network presented that the progression of female LCNS was significantly associated with immune response as chemokine activity and lipopolysaccharide response, and these biological processes (BP) might be mediated by chemokine signaling pathway and cytokine-cytokine receptor interaction. Then, survival analysis by Kaplan-Meier (K-M) Plotter online platform presented down-regulated gene colony stimulating factor 2 receptor beta common subunit (CSF2RB) of female LCNS might be involved in poor clinical outcome. Female LCNS with high expression of CSF2RB might be relevant with relative risk reduction of mortality, longer median survival time and higher 5-year survival rate, while female LCNS with low expression of CSF2RB might be implicated in a poor clinical outcome. In short, our results support CSF2RB to be a candidate survival predictor for female LCNS.

Tumor decellularization reveals proteomic and mechanical characteristics of the extracellular matrix of primary liver cancer

Tumor initiation and progression are critically dependent on interaction of cancer cells with their cellular and extracellular microenvironment. Alterations in the composition, integrity, and mechanical properties of the extracellular matrix (ECM) dictate tumor processes including cell proliferation, migration, and invasion. Also in primary liver cancer, consisting of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), the dysregulation of the extracellular environment by liver fibrosis and tumor desmoplasia is pertinent. Yet, the exact changes occurring in liver cancer ECM remain uncharacterized and underlying tumor-promoting mechanisms remain largely unknown. Herein, an integrative molecular and mechanical approach is used to extensively characterize the ECM of HCC and CCA tumors by utilizing an optimized decellularization technique. We identified a myriad of proteins in both tumor and adjacent liver tissue, uncovering distinct malignancy-related ECM signatures. The resolution of this approach unveiled additional ECM-related proteins compared to large liver cancer transcriptomic datasets. The differences in ECM protein composition resulted in divergent mechanical properties on a macro- and micro-scale that are tumor-type specific. Furthermore, the decellularized tumor ECM was employed to create a tumor-specific hydrogel that supports patient-derived tumor organoids, which provides a new avenue for personalized medicine applications. Taken together, this study contributes to a better understanding of alterations to composition, stiffness, and collagen alignment of the tumor ECM that occur during liver cancer development.

MiRNA and Potential Prognostic Value in Non-Smoking Females with Lung Adenocarcinoma by High-Throughput Sequencing

Background

Non-smoking females with lung adenocarcinoma (LUAD) account for a unique disease entity and miRNA play critical roles in cancer development and progression. The purpose of this study is to explore prognosis-related differentially expressed miRNA (DEmiRNA) and establish a prognostic model for non-smoking females with LUAD.

Methods

Eight specimens were collected from thoracic surgery of non-smoking females with LUAD and implemented the miRNA sequencing. The intersection of our miRNA sequencing data and TCGA database were identified as common DEmiRNA. Then, we predicted the target genes of the common DEmiRNAs (DETGs) and explored the functional enrichment and prognosis of DETGs. A risk model by overall survival (OS)-related DEmiRNA was constructed based on multivariate Cox regression analyses.

Results

A total of 34 overlapping DEmiRNA were obtained. The DETGs were enriched in pathways including "Cell cycle" and "miRNAs in cancer". The DETGs (KPNA2, CEP55, TRIP13, MYBL2) were risk factors, significantly related to OS, progression-free survival (PFS), and were also hub genes. ScRNA-seq data also validated the expression of the four DETGs. Hsa-mir-200a, hsa-mir-21, and hsa-mir-584 were significantly associated with OS. The prognostic prediction model constructed by the 3 DEmiRNA could effectively predict OS and can be used as an independent prognostic factor of non-smoking females with LUAD.

Conclusion

Hsa-mir-200a, hsa-mir-21, and hsa-mir-584 can serve as potential prognostic predictors in non-smoking females with LUAD. A novel prognostic model based on the three DEmiRNAs was also constructed to predict the survival of non-smoking females with LUAD and showed good performance. The result of our paper can be helpful for treatment and prognosis prediction for non-smoking females with LUAD.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Identification of genes and pathways associated with sex in Non-smoking lung cancer population

INTRODUCTION

Women represent a higher proportion than men among those with lung cancer in nonsmokers compared to smokers. The reason for this abnormally higher proportion is not yet clear, but sex differences suggest there may be a genetic component at play.

MATERIALS AND METHODS

The gene expression determined by Illumina RNA Sequencing and the relevant clinical information of lung cancer patients was download from TCGA. The differentially expressed genes (DEGs) were screened between males and females in both nonsmoking and smoking populations. The top 50 validated DEGs are represented with heatmaps. Based on the DEGs, GO functional and KEGG pathway enrichment analyses were performed. PPI networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect lung cancer patient prognosis.

RESULTS

In non-smoking patients, there were significantly more females than males (female 73.0% vs male 27.0%, P < 0.001). Such difference was not found in smoking patients (female 50.7% vs male 49.3%, P = 0.770). A total of 898 DEGs were identified in the non-smoking population, while a total of 992 DEGs were identified in the smoking population. Of these, only 122 genes were shared by both populations. Some pathways were enriched specifical in non-smoking population, such as cAMP signaling pathway and ovarian steroidogenesis. Several proteins related to estrogen function and MAPK/PI3K signaling, such as KRT16, ERBB4 and NTF4, showed differential effects on the lung adenocarcinoma progression in non-smoking males or females.

CONCLUSIONS

Some genetic differences between male and female in non-smoking lung adenocarcinoma patients have been identified. Potentially, ER signaling and MAPK/PI3K signaling partially participated in this discrepancy.

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

Application of two-dimensional difference gel electrophoresis to identify protein changes between center, margin, and adjacent non-tumor tissues obtained from non-small-cell lung cancer with adenocarcinoma or squamous cell carcinoma subtype

Lung cancer is responsible for the most cancer-related mortality worldwide and the mechanism of its development is poorly understood. Proteomics has become a powerful tool offering vital knowledge related to cancer development. Using a two-dimensional difference gel electrophoresis (2D-DIGE) approach, we sought to compare tissue samples from non-small-cell lung cancer (NSCLC) patients taken from the tumor center and tumor margin. Two subtypes of NSCLC, adenocarcinoma (ADC) and squamous cell carcinoma (SCC) were compared. Data are available via ProteomeXchange with identifier PXD032736 and PXD032962 for ADC and SCC, respectively. For ADC proteins, 26 significant canonical pathways were identified, including Rho signaling pathways, a semaphorin neuronal repulsive signaling pathway, and epithelial adherens junction signaling. For SCC proteins, nine significant canonical pathways were identified, including hypoxia-inducible factor-1α signaling, thyroid hormone biosynthesis, and phagosome maturation. Proteins differentiating the tumor center and tumor margin were linked to cancer invasion and progression, including cell migration, adhesion and invasion, cytoskeletal structure, protein folding, anaerobic metabolism, tumor angiogenesis, EMC transition, epithelial adherens junctions, and inflammatory responses. In conclusion, we identified several proteins that are important for the better characterization of tumor development and molecular specificity of both lung cancer subtypes. We also identified proteins that may be important as biomarkers and/or targets for anticancer therapy.

Network Pharmacology-Based Analysis of the Effects of Corydalis decumbens (Thunb.) Pers. in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is one of the most malignant tumors worldwide. The main treatment for NSCLC is based on Western medicine; however, the overall effect is unsatisfactory. This study aimed to investigate the potential therapeutic targets and pharmacological mechanisms of action of the traditional Chinese medicine Corydalis decumbens (Thunb.) Pers. in NSCLC based on network pharmacology and bioinformatics. The overlapping genes between Corydalis decumbens (Thunb.) Pers. and NSCLCs were screened using Venn analysis. Cytoscape 3.7.1 software was used to analyze the overlapping target protein-protein interaction (PPI) network. Gene ontology and pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomics database were performed to exploring biological functions of the overlapping genes. The gene expression profiling interactive analysis dataset was used to analyze the correlation between hub gene expression and disease. This study revealed 38 nodes with 191 edges, which may be therapeutic targets for NSCLC. PPI network analysis showed that the most likely association was between the genes AR and NCOA2, NCOA2, and RXRA and ESR1 and NCOA2. These overlapping genes were mainly enriched in the estrogen signaling pathway, calcium signaling pathway, cholinergic synapse, and PI3K-Akt signaling pathway. ESR2 mRNA levels were significantly downregulated in patients with lung adenocarcinoma (LUAD) getting worse, and KDR levels were lower in lung squamous cell carcinoma (LUSC) than those in normal tissue. PTGS2 expression was correlated with the median survival time of LUAD, and ESR1 expression was correlated with the median survival time of LUSC. The application of network pharmacology revealed the potential mechanism underlying the effects of Corydalis decumbens (Thunb.) Pers. in NSCLC treatment and provided a theoretical basis for further in-depth research in this field.

Subtype-dependent regulation of Gβγ signalling

G protein-coupled receptors (GPCRs) transmit information to the cell interior by transducing external signals to heterotrimeric G protein subunits, Gα and Gβγ subunits, localized on the inner leaflet of the plasma membrane. Though the initial focus was mainly on Gα-mediated events, Gβγ subunits were later identified as major contributors to GPCR-G protein signalling. A broad functional array of Gβγ signalling has recently been attributed to Gβ and Gγ subtype diversity, comprising 5 Gβ and 12 Gγ subtypes, respectively. In addition to displaying selectivity towards each other to form the Gβγ dimer, numerous studies have identified preferences of distinct Gβγ combinations for specific GPCRs, Gα subtypes and effector molecules. Importantly, Gβ and Gγ subtype-dependent regulation of downstream effectors, representing a diverse range of signalling pathways and physiological functions have been found. Here, we review the literature on the repercussions of Gβ and Gγ subtype diversity on direct and indirect regulation of GPCR/G protein signalling events and their physiological outcomes. Our discussion additionally provides perspective in understanding the intricacies underlying molecular regulation of subtype-specific roles of Gβγ signalling and associated diseases.

SELE gene as a characteristic prognostic biomarker of colorectal cancer

OBJECTIVE

To investigate the expression and clinical value of the E-selectin gene (SELE) in colorectal cancer (CRC).

METHODS

Using gene expression profiles and clinicopathological data for patients with CRC from The Cancer Genome Atlas, and tumor and adjacent normal tissues from 31 patients with CRC from Xianyang Central Hospital, we studied the correlation between SELE gene expression and clinical parameters using Kaplan-Meier and Cox proportional hazards regression analyses.

RESULTS

Higher expression of SELE was significantly associated with a poorer prognosis and shorter survival in patients with CRC. The median expression level of SELE was significantly higher in CRC tissues compared with healthy adjacent tissue. Cox regression analysis showed that the prognosis of CRC was significantly correlated with the expression of SELE. Immunohistochemical analysis also showed that positive expression of E-selectin increased significantly in line with increasing TNM stage.Conclusion: This study confirmed that SELE gene expression is an independent prognostic factor in patients with CRC.

Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein-protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan-Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.

Antitumor activity via apoptotic cell death pathway of water soluble copper(II) complexes: effect of the diamino unit on selectivity against lung cancer NCI-H460 cell line

The cytotoxicity against five human tumor cell lines (THP-1, U937, Molt-4, Colo-205 and NCI-H460) of three water soluble copper(II) coordination compounds containing the ligands 3,3'-(ethane-1,2-diylbis(azanediyl))dipropanamide (BCEN), 3,3'-(piperazine-1,4-diyl)dipropanamide (BPAP) or 3,3'-and (1,4-diazepane-1,4-diyl)dipropanamide (BPAH) are reported in this work. The ligands contain different diamine units (ethylenediamine, piperazine or homopiperazine) and two propanamide units attached to the diamine centers, resulting in N₂O₂ donor sets. The complex containing homopiperazine unit presented the best antiproliferative effect and selectivity against lung cancer cell line NCI-H460, showing inhibitory concentration (IC₅₀) of 58 μmol dm^-3 and Selectivity Index (SI) > 3.4. The mechanism of cell death promoted by the complex was investigated by Sub-G1 cell population analysis and annexin V and propidium iodide (PI) labeling techniques, suggesting that the complex promotes death by apoptosis. Transmission electron microscopy investigations are in agreement with the results presented by mitochondrial membrane potential analysis and also show the impairment of other organelles, including endoplasmic reticulum.

GOLM1 predicts poor prognosis of patients with NSCLC and is associated with the proliferation and chemo-sensitivity of cisplatin in NSCLC cells: bioinformatics analysis and laboratory validation

Lung cancer is one of the deadliest malignant tumors with non-small cell lung cancer (NSCLC) being the most prevalent type. Patients with NSCLC usually were diagnosed at the advance clinical stages, and these patients often had high rate of tumor-recurrence, thus leading to poor prognosis. Yet, the molecular mechanisms underlying NSCLC progression and recurrence are largely unknown. This study aimed to identify potential hub genes associated with the pathophysiology of NSCLC by bioinformatics analysis and laboratory validation. The GSE51852, GSE52248 and GSE75037 datasets were downloaded from the Gene Expression Omnibus database. The overlapping differentially expressed genes (DEGs) were analyzed by GEO2R tool. Gene Ontology (GO) and KEGG pathway enrichment analysis were performed on these overlapping DEGs. The protein-protein interaction network was constructed to identify hub genes from DEGs. The expression and survival analysis of these hub genes were performed by using the integrated bioinformatics tools. Finally, the effects of GOLM1 on the proliferation and chemo-sensitivity of NSCLC cells were determined by in vitro functional assays. A total of 197 overlapping DEGs (37 up-regulated and 160 down-regulated) were identified from the microarray datasets. Furthermore, the PPI network with 89 nodes and 768 edges was constructed and 17 hub genes were identified from PPI network by using MCODE analysis. The survival analysis revealed that the expression of 5 hub genes (FGF2, GOLM1, GPC3, IL6 and SPP1) were significantly correlated with overall survival of patients with lung cancer. Furthermore, the in vitro functional studies showed that GOLM1 overexpression promoted the NSCLC cell proliferation and colony formation; while GOLM1 knockdown exerted the opposite effects. Importantly, GOLM1 overexpression reduced the chemo-sensitivity of cisplatin in NSCLC cells by attenuating the inhibitory effects of cisplatin on the cell proliferation and colony formation. In conclusion, the present study showed that 5 hub genes including FGF2, GOLM1, GPC3, IL6 and SPP1 were deregulated in NSCLC tissues and may predict the prognosis of patients with NSCLC. GOLM1 may play an important role in regulating the cell proliferation and chemo-sensitivity of cisplatin in NSCLC.

Functional Signatures in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis of Sex-Based Differences in Transcriptomic Studies

While studies have established the existence of differences in the epidemiological and clinical patterns of lung adenocarcinoma between male and female patients, we know relatively little regarding the molecular mechanisms underlying such sex-based differences. In this study, we explore said differences through a meta-analysis of transcriptomic data. We performed a meta-analysis of the functional profiling of nine public datasets that included 1366 samples from Gene Expression Omnibus and The Cancer Genome Atlas databases. Meta-analysis results from data merged, normalized, and corrected for batch effect show an enrichment for Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to the immune response, nucleic acid metabolism, and purinergic signaling. We discovered the overrepresentation of terms associated with the immune response, particularly with the acute inflammatory response, and purinergic signaling in female lung adenocarcinoma patients, which could influence reported clinical differences. Further evaluations of the identified differential biological processes and pathways could lead to the discovery of new biomarkers and therapeutic targets. Our findings also emphasize the relevance of sex-specific analyses in biomedicine, which represents a crucial aspect influencing biological variability in disease.

Identification of novel transcription factor-microRNA-mRNA co-regulatory networks in pulmonary large-cell neuroendocrine carcinoma

Background

Large cell neuroendocrine carcinoma (LCNEC) of the lung is a rare neuroendocrine neoplasm. Previous studies have shown that microRNAs (miRNAs) are widely involved in tumor regulation through targeting critical genes. However, it is unclear which miRNAs play vital roles in the pathogenesis of LCNEC, and how they interact with transcription factors (TFs) to regulate cancer-related genes.

Methods

To determine the novel TF-miRNA-target gene feed-forward loop (FFL) model of LCNEC, we integrated multi-omics data from Gene Expression Omnibus (GEO), Transcriptional Regulatory Relationships Unraveled by Sentence-Based Text Mining (TRRUST), Transcriptional Regulatory Element Database (TRED), and The experimentally validated microRNA-target interactions database (miRTarBase database). First, expression profile datasets for mRNAs (GSE1037) and miRNAs (GSE19945) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. The target genes of the FFL were obtained from the miRTarBase database, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes. Then, we screened for key miRNAs in the FFL and performed gene regulatory network analysis based on key miRNAs. Finally, the TF-miRNA-target gene FFLs were constructed by the hypergeometric test.

Results

A total of 343 DEGs and 60 DEMs were identified in LCNEC tissues compared to normal tissues, including 210 down-regulated and 133 up-regulated genes, and 29 down-regulated and 31 up-regulated miRNAs. Finally, the regulatory network of TF-miRNA-target gene was established. The key regulatory network modules included ETS1-miR195-CD36, TAOK1-miR7-1-3P-GRIA1, E2F3-miR195-CD36, and TEAD1-miR30A-CTHRC1.

Conclusions

We constructed the TF-miRNA-target gene regulatory network, which is helpful for understanding the complex LCNEC regulatory mechanisms.

Assessment of Significant Pathway Signaling and Prognostic Value of GNG11 in Ovarian Serous Cystadenocarcinoma

BACKGROUND

GNG11 (G protein subunit gamma 11) is a member of guanine nucleotide-binding protein (G protein) gamma family. Few studies elucidated the role of GNG11 in human disease, especially in tumors. The present study initially analyzed the function of GNG11 in ovarian serous cystadenocarcinoma.

METHODS

The differential expression of GNG11 mRNA in ovarian cancer and normal tissues was evaluated through Oncomine, CCLE, Gepia, UCSC Xena and UALCAN databases. The protein expression of GNG11 was assessed via HPA database. Prognosis analysis was performed by Kaplan-Meier Plotter. Restrict survival analysis to subtypes including tumor grade, cancer stage and TP53 mutation status was then carried out. GSEA enrichment analysis was performed to explore the significant pathways associated with GNG11 in ovarian cancer. Finally, the upstream miRNAs of GNG11 were predicted by DIANA, Target Scan, miRDB and miRWalk databases, and the potential key KEGG pathways were subsequently determined by DIANA.

RESULTS

The mRNA expression of GNG11 was down-regulated in ovarian cancer patients (P<0.05). The cancer stage of patients correlated with the expression of GNG11 (P<0.05). Survival analysis indicated that GNG11 high expression statistically shortened the overall survival time of patients (HR=1.26, P=0.0043) compared with low expression group, especially for the patients with earlier stage (HR=2.48, P=0.035) and lower grade (HR=1.72, P=0.0016). Subsequently, the consistent upstream miRNA of GNG11, hsa-miR-22-5p, was predicted from 4 databases. The differential expression profile of hsa-miR-22-5p in blood was observed in ovarian cancer patients. According to the GSEA analysis on GNG11 and KEGG analysis on hsa-miR-22-5p, the consistent pathway of ECM-receptor interaction was observed (all P<0.01). ECM-receptor interaction pathway and differential expression of hsa-miR-22-5p in blood suggested the migration risk of ovarian cancer.

CONCLUSION

High expression of GNG11 indicated the poor prognosis of ovarian cancer patients. GNG11 might play a crucial role in the biological process of ovarian serous cystadenocarcinoma by ECM-receptor interaction pathway, thus affecting the prognosis of patients.

Comprehensive transcriptomic analysis identifies novel regulators of lung adenocarcinoma

Lung adenocarcinoma (LA) is a subtype of lung cancer that accounts for about 40% of all lung cancers. Analysis of molecular mechanisms controlling this cancer can help scientists to detect, control and treat LA. Here, a microarray dataset (GSE118370) containing six normal lung (NL) and six LA samples was screened using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG and ChEA were used to analyze DEGs-related gene ontology, pathways and transcription factors (TFs), respectively. The Protein-protein interaction network for DEGs and TFs was constructed by STRING and Cytoscape. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 350 genes were upregulated and 608 genes were downregulated in LA. The upregulated genes or LA-related gens were enriched in biological process and pathways such as extracellular matrix disassembly and p53 signaling pathway, whereas the downregulated genes or NL-related genes were enriched in cell adhesion and cell-surface receptor signaling pathway. ESR1, KIF18B, BIRC5, CHEK1, CCNB1 and AURKA were determined as hub genes for LA. FOXA1 and TFAP2A had the highest number of connectivity in LA-related TFs. hsa-miR-192-5p and hsa-miR-215-5p could target the highest number of LA-related genes. Metabolite analysis showed that Estrone and NADPH were among the top ten metabolites associated with LA-related genes. Taken together, LA-related genes, especially the hub genes, TFs, and metabolites might be used as novel markers for LA, as well as for diagnosis and guiding therapeutic strategies of LA.

Profiling the mRNA and miRNA in Peripheral Blood Mononuclear Cells in Subjects with Active Tuberculosis

Purpose

To identify candidate hub genes and miRNAs associated with active tuberculosis (ATB) and reveal the potential molecular mechanisms of disease progression.

Patients and Methods

The expression of mRNA and miRNA was evaluated in peripheral blood mononuclear cells (PBMC) from 4 ATB patients and 4 healthy donors (HD) using high throughput sequencing (HTS) and bioinformatics analysis. Moreover, differentially expressed miRNAs were validated with 35 ATB patients and 35 HDs using reverse transcription quantitative real-time PCR (RT-qPCR).

Results

A total of 2658 significantly differentially expressed genes (DEG) including 1415 up-regulated genes and 1243 down-regulated genes were identified in the ATB group compared with HDs, and the DEGs enriched in immune-related pathways, especially in TNF signaling pathway, cytokine–cytokine receptor interaction, mitogen-activated protein kinase (MAPK) signaling pathways and tuberculosis. Additionally, 10 hub genes were acquired according to protein–protein interaction (PPI) analysis of DEGs. Moreover, 26 differentially expressed miRNAs were found in ATB group compared with HDs. Furthermore, RT-qPCR results showed that hsa-miR-23a-5p (P=0.0106), hsa-miR-183-5p (P=0.0027), hsa-miR-193a-5p (P=0.0021) and hsa-miR-941(P=0.0001) were significantly increased in the ATB patients compared with HD group, and the hsa-miR-16-1-3p was significantly decreased (P=0.0032).

Conclusion

Our research provided a characteristic profile of mRNAs and miRNAs expressed in ATB subjects, and 10 hub genes related with ATB were found, which will contribute to explore the role of miRNAs and hub genes in the pathogenesis of ATB, and improve the ability of differential diagnosis and treatment for the disease.

Identification of therapeutic targets and mechanisms of tumorigenesis in non-small cell lung cancer using multiple-microarray analysis

Lung cancer is the most commonly occurring cancer attributed to the leading cause of cancer-related deaths globally. Non-small cell lung cancer (NSCLC) comprises 85% to 90% of lung cancers. The survival rate of patients with advanced stage NSCLC is in months. Moreover, the underlying molecular mechanisms still remain to be understood.We used 2 sets of microarray data in combination with various bioinformatic approaches to identify the differentially expressed genes (DEGs) in NSCLC patients.We identified a total of 419 DEGs using the Limma package. Gene set enrichment analysis demonstrated that "Citrate cycle (TCA cycle)," "RNA degradation," and "Pyrimidine metabolism" pathways were significantly enriched in the NSCLC samples. Gene Ontology annotations of the 419 DEGs primarily comprised "glycosaminoglycan binding," "cargo receptor activity," and "organic acid binding." Kyoto Encyclopedia of Genes and Genomes analysis revealed that DEGs were enriched in pathways related to "Malaria," "Cell cycle," and "IL-17 signaling pathway." Protein protein interaction network analysis showed that the hub genes constituted of CDK1, CDC20, BUB1, BUB1B, TOP2A, CCNA2, KIF20A, CCNB1, KIF2C, and NUSAP1.Taken together, the identified hub genes and pathways will help understand NSCLC tumorigenesis and develop prognostic markers and therapeutic targets against NSCLC.

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.

High Expression of UBB, RAC1, and ITGB1 Predicts Worse Prognosis among Nonsmoking Patients with Lung Adenocarcinoma through Bioinformatics Analysis

Purpose

The molecular mechanism underlying the tumorigenesis and progression of lung adenocarcinoma (LUAD) in nonsmoking patients remains unclear. This study was conducted to select crucial therapeutic and prognostic biomarkers for nonsmoking patients with LUAD.

Methods

Microarray datasets from the Gene Expression Omnibus (GSE32863 and GSE75037) were analyzed for differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis of DEGs was performed, and protein-protein interaction network was then constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. Hub genes were then identified by the rank of degree. Overall survival (OS) analyses of hub genes were performed among nonsmoking patients with LUAD in Kaplan-Meier plotter. The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases were applied to verify hub genes. In addition, we performed Gene Set Enrichment Analysis (GSEA) of hub genes.

Results

We identified 1283 DEGs, including 743 downregulated and 540 upregulated genes. GO enrichment analyses showed that DEGs were significantly enriched in collagen-containing extracellular matrix and extracellular matrix organization. Moreover, 19 hub genes were identified, and 12 hub genes were closely associated with OS. Although no obvious difference was detected in ITGB1, the downregulation of UBB and upregulation of RAC1 were observed in LUAD tissues of nonsmoking patients. Immunohistochemistry in THPA database confirmed that UBB and ITGB1 were downregulated, while RAC1 was upregulated in LUAD. GSEA suggested that ribosome, B cell receptor signaling pathway, and cell cycle were associated with UBB, RAC1, and ITGB1 expression, respectively.

Conclusions

Our study provides insights into the underlying molecular mechanisms of the carcinogenesis and progression of LUAD in nonsmoking patients and demonstrated UBB, RAC1, and ITGB1 as therapeutic and prognostic indicators for nonsmoking LUAD. This is the first study to report the crucial role of UBB in nonsmoking LUAD.

The Double-Edge Sword of Autophagy in Cancer: From Tumor Suppression to Pro-tumor Activity

During tumorigenesis, cancer cells are exposed to a wide variety of intrinsic and extrinsic stresses that challenge homeostasis and growth. Cancer cells display activation of distinct mechanisms for adaptation and growth even in the presence of stress. Autophagy is a catabolic mechanism that aides in the degradation of damaged intracellular material and metabolite recycling. This activity helps meet metabolic needs during nutrient deprivation, genotoxic stress, growth factor withdrawal and hypoxia. However, autophagy plays a paradoxical role in tumorigenesis, depending on the stage of tumor development. Early in tumorigenesis, autophagy is a tumor suppressor via degradation of potentially oncogenic molecules. However, in advanced stages, autophagy promotes the survival of tumor cells by ameliorating stress in the microenvironment. These roles of autophagy are intricate due to their interconnection with other distinct cellular pathways. In this review, we present a broad view of the participation of autophagy in distinct phases of tumor development. Moreover, autophagy participation in important cellular processes such as cell death, metabolic reprogramming, metastasis, immune evasion and treatment resistance that all contribute to tumor development, is reviewed. Finally, the contribution of the hypoxic and nutrient deficient tumor microenvironment in regulation of autophagy and these hallmarks for the development of more aggressive tumors is discussed.

Exploring celiac disease candidate pathways by global gene expression profiling and gene network cluster analysis

Celiac disease (CeD) is a gastrointestinal autoimmune disorder, whose specific molecular basis is not yet fully interpreted. Therefore, in this study, we compared the global gene expression profile of duodenum tissues from CeD patients, both at the time of disease diagnosis and after two years of the gluten-free diet. A series of advanced systems biology approaches like differential gene expression, protein–protein interactions, gene network-cluster analysis were deployed to annotate the candidate pathways relevant to CeD pathogenesis. The duodenum tissues from CeD patients revealed the differential expression of 106 up- and 193 down-regulated genes. The pathway enrichment of differentially expressed genes (DEGs) highlights the involvement of biological pathways related to loss of cell division regulation (cell cycle, p53 signalling pathway), immune system processes (NOD-like receptor signalling pathway, Th1, and Th2 cell differentiation, IL-17 signalling pathway) and impaired metabolism and absorption (mineral and vitamin absorptions and drug metabolism) in celiac disease. The molecular dysfunctions of these 3 biological events tend to increase the number of intraepithelial lymphocytes (IELs) and villous atrophy of the duodenal mucosa promoting the development of CeD. For the first time, this study highlights the involvement of aberrant cell division, immune system, absorption, and metabolism pathways in CeD pathophysiology and presents potential novel therapeutic opportunities.

Comprehensive Genomic Analysis Reveals the Prognostic Role of LRRK2 Copy-Number Variations in Human Malignancies

Genetic alterations of leucine-rich repeat kinase 2 (LRRK2), one of the most important contributors to familial Parkinson’s disease (PD), have been hypothesized to play a role in cancer development due to demographical and preclinical data. Here, we sought to define the prevalence and prognostic significance of LRRK2 somatic mutations across all types of human malignancies by querying the publicly available online genomic database cBioPortal. Ninety-six different studies with 14,041 cases were included in the analysis, and 761/14,041 (5.4%) showed genetic alterations in LRRK2. Among these, 585 (76.9%) were point mutations, indels or fusions, 168 (22.1%) were copy number variations (CNVs), and 8 (1.0%) showed both types of alterations. One case showed the somatic mutation R1441C. A significant difference in terms of overall survival (OS) was noted between cases harboring somatic LRRK2 whole deletions, amplifications, and CNV-unaltered cases (median OS: 20.09, 57.40, and 106.57 months, respectively; p = 0.0008). These results suggest that both LRRK2 amplifications and whole gene deletions could play a role in cancer development, paving the way for future research in terms of potential treatment with LRRK2 small molecule inhibitors for LRRK2-amplified cases.

Gastric cancer risk-scoring system based on analysis of a competing endogenous RNA network

Background

Long noncoding RNAs (lncRNAs) can play vital roles in tumor initiation, progression, invasion, and metastasis. However, the functional role of the lncRNA-based competing endogenous RNA (ceRNA) networks in gastric cancer (GC) is still unclear. We aimed to identify novel lncRNAs and their association with GC prognosis.

Methods

The lncRNA, miRNA, and mRNA expression profiles of GC patients data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using the edge-R package. Then, the relationship among lncRNAs-miRNAs-mRNAs was integrated into a constructed ceRNA network with Cytoscape software. Using Cox regression analysis, a risk score system based on DEGs associated with patient prognosis in GC was established. Finally, a nomogram was founded to predict the prognosis of GC patients.

Results

A total of 971 differentially expressed lncRNAs (DElncRNAs), 144 differentially expressed miRNAs (DEmiRNAs) and 2,789 differentially expressed mRNAs (DEmRNAs) were identified and found to be associated with GC risk. Using the bioinformatics method, a ceRNA network involving 62 DElncRNAs, 21 DEmiRNAs and 59 DEmRNAs was constructed. Based on the results of the Cox regression analysis, a risk-scoring system involving 3 lncRNAs (i.e., ADAMTS9-AS1, C15orf54, and AL391152.1) was set up for the survival analysis of GC patients. The area under the receiver operating characteristic (ROC) curve for the risk-scoring system was 0.674, with a C-index of 0.64 [95% confidence interval (CI): 0.59–0.69, P=2.806485e−08]. Univariate and multivariate Cox regression analyses demonstrated that the risk-scoring system was an independent prognostic factor for GC. The risk-scoring system is positively associated with advanced tumor grade. The expression of these 3 lncRNAs were validated in GEPIA database. A nomogram based on these 3 lncRNAs was created to predict the prognosis of GC patients.

Conclusions

Our study established a novel lncRNA-expression-based ceRNA network and an ADAMTS9-AS1-C15orf54-AL391152.1-based risk-scoring system, which can be used to predict the prognosis of GC patients.

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

Exploration of estrogen receptor-associated hub genes and potential molecular mechanisms in non-smoking females with lung adenocarcinoma using integrated bioinformatics analysis

The present study aimed to explore important estrogen receptor-associated genes and to determine the potential pathogenic and prognostic factors for lung adenocarcinoma in non-smoking females. The gene expression profiles of the two datasets (GSE32863 and GSE75037) were downloaded from the Gene Expression Omnibus (GEO) database. Data for non-smoking female patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database were also downloaded. The Linear Models for Microarray Data package in R was used to explore the differentially expressed genes (DEGs) between samples from non-smoking female patients with lung adenocarcinoma and samples of adjacent non-cancerous lung tissue. The Database for Annotation, Visualization and Integrated Discovery was used for functional enrichment of the DEGs. The Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software were used to obtain a protein-protein interaction (PPI) network and to identify the hub genes. In addition, the network between the estrogen receptor and the DEGs was constructed. A Kaplan-Meier survival plot was used to analyze the overall survival (OS). In total, 248 DEGs were identified in the GEO database, and 2,362 DEGs were identified in TCGA database. The intersection of the two datasets (DEGs in GEO and TCGA) revealed 170 DEGs, and these were selected for further investigation. Gene Ontology was used to group the 170 DEGs into biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis was subsequently performed. A total of 27 hub genes, including caveolin 1 (CAV1), matrix metallopeptidase 9 (MMP9), secreted phosphoprotein 1 (SPP1) and collagen type I α 1 chain (COL1A1), were closely associated with the estrogen receptor. CAV1 and SPP1 were associated with the OS. However, MMP9 and COL1A1 did not have any significant effect on OS. In summary, the identification of CAV1, MMP9, SPP1 and COL1A1 may provide novel insights into the molecular mechanism of lung adenocarcinoma in non-smoking female patients, and the results obtained in the current study may guide future clinical studies.