Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma

A Novel Signature Based on m6A Regulator-Mediated Genes Along Glycolytic Pathway Predicts Prognosis and Immunotherapy Responses of Gastric Cancer Patients

N6-methyladenosine (m6A) modification is involved in many aspects of gastric cancer (GC). Moreover, m6A and glycolysis-related genes (GRGs) play important roles in immunotherapeutic and prognostic implication of GC. However, GRGs involved in m6A regulation have never been analyzed comprehensively in GC. Herein, the study aims to identify and validate a novel signature based on m6A-related GRGs in GC patients. Therefore, a m6A-related GRGs signature is established, which can predict the survival of patients with GC and remain an independent prognostic factor in multivariate analyses. Clinical significance of the model is well validated in internal cohort and independent validation cohort. In addition, the expression levels of risk model-related GRGs in clinical samples are validated. Consistent with the database results, all model genes are up-regulated in expression except DCN. After regrouping the patients based on this risk model, the study can effectively distinguish between them in respect to immune-cell infiltration microenvironment and immunotherapeutic response. Additionally, candidate drugs targeting risk model-related GRGs are confirmed. Finally, a nomogram combining risk scores and clinical parameters is created, and calibration plots show that the nomogram can accurately predict survival. This risk model can serve as a reliable assessment tool for predicting prognosis and immunotherapeutic responses in GC patients.

KIF20B Correlates with LUAD Progression and Is an Independent Risk Factor

OBJECTIVE

Kinesin family proteins (KIFs) play crucial roles in human tumorigenesis and progression. This study aimed to investigate the expression and association of Kinesin family member 20B (KIF20B) with lung adenocarcinoma (LUAD).

METHODS

RNA-seq data from LUAD patients (n = 535) were extracted from TCGA. KIF20B expression was compared between tumor tissues and controls, and between different stages of the disease. Survival and Cox regression analyses were performed, as well as in vitro cellular experiments on A549 cells.

RESULTS

KIF20B is upregulated in LUAD tumor tissues compared with controls and is higher in advanced stages. Patients with high expression of KIF20B have shorter survival times. KIF20B is an independent risk factor for the prognosis of LUAD. High KIF20B expression samples were enriched in signaling pathways related to tumor progression. si-KIF20B transfection reduced migration and invasion of A549 cells and increased apoptosis. The expression of p53 and Bax proteins was upregulated by si-KIF20B, while Bcl-2 was down-regulated.

DISCUSSION

This study reveals that high KIF20B expression is an independent risk factor for the poor prognosis of LUAD. The inhibition of KIF20B might be of great value for suppressing LUAD progression.

Biological and clinical significance of the glypican-3 gene in human lung adenocarcinoma: An in silico analysis

Glypican-3 (GPC3), a membrane-bound heparan sulfate proteoglycan, has long been found to be dysregulated in human lung adenocarcinomas (LUADs). Nevertheless, the function, mutational profile, epigenetic regulation, co-expression profile, and clinicopathological significance of the GPC3 gene in LUAD progression are not well understood. In this study, we analyzed cancer microarray datasets from publicly available databases using bioinformatics tools to elucidate the above parameters. We observed significant downregulation of GPC3 in LUAD tissues compared to their normal counterparts, and this downregulation was associated with shorter overall survival (OS) and relapse-free survival (RFS). Nevertheless, no significant differences in the methylation pattern of GPC3 were observed between LUAD and normal tissues, although lower promoter methylation was observed in male patients. GPC3 expression was also found to correlate significantly with infiltration of B cells, CD8+, CD4+, macrophages, neutrophils, and dendritic cells in LUAD. In addition, a total of 11 missense mutations were identified in LUAD patients, and ~1.4% to 2.2% of LUAD patients had copy number amplifications in GPC3. Seventeen genes, mainly involved in dopamine receptor-mediated signaling pathways, were frequently co-expressed with GPC3. We also found 11 TFs and 7 miRNAs interacting with GPC3 and contributing to disease progression. Finally, we identified 3 potential inhibitors of GPC3 in human LUAD, namely heparitin, gemcitabine and arbutin. In conclusion, GPC3 may play an important role in the development of LUAD and could serve as a promising biomarker in LUAD.

Accelerated epigenetic age, inflammation, and gene expression in lung: comparisons of smokers and vapers with non-smokers

BACKGROUND

Cigarette smoking and aging are the main risk factors for pulmonary diseases, including cancer. Epigenetic aging may explain the relationship between smoking, electronic cigarette vaping, and pulmonary health. No study has examined smoking and vaping-related epigenetic aging in relation to lung biomarkers.

METHODS

Lung epigenetic aging measured by DNA methylation (mAge) and its acceleration (mAA) was assessed in young (age 21-30) electronic cigarette vapers (EC, n = 14, including 3 never-smoking EC), smokers (SM, n = 16), and non-EC/non-SM (NS, n = 39). We investigated relationships of mAge estimates with chronological age (Horvath-mAge), lifespan/mortality (Grim-mAge), telomere length (TL-mAge), smoking/EC history, urinary biomarkers, lung cytokines, and transcriptome.

RESULTS

Compared to NS, EC and SM had significantly older Grim-mAge, shorter TL-mAge, significantly accelerated Grim-mAge and decelerated TL-mAge. Among SM, Grim-mAA was associated with nicotine intake and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). For EC, Horvath-mAA was significantly correlated with puffs per day. Overall, cytokines (IL-1β, IL-6, and IL-8) and 759 transcripts (651 unique genes) were significantly associated with Grim-mAA. Grim-mAA-associated genes were highly enriched in immune-related pathways and genes that play a role in the morphology and structures of cells/tissues.

CONCLUSIONS

Faster lung mAge for SM is consistent with prior studies of blood. Faster lung mAge for EC compared to NS indicates possible adverse pulmonary effects of EC on biological aging. Our findings support further research, particularly on epigenetic markers, on effects of smoking and vaping on pulmonary health. Given that most EC are former smokers, further study is needed to understand unique effects of electronic cigarettes on biological aging.

The colocatome as a spatial -omic reveals shared microenvironment features between tumour-stroma assembloids and human lung cancer

Computational frameworks to quantify and compare microenvironment spatial features of in-vitro patient-derived models and clinical specimens are needed. Here, we acquired and analysed multiplexed immunofluorescence images of human lung adenocarcinoma (LUAD) alongside tumour-stroma assembloids constructed with organoids and fibroblasts harvested from the leading edge (Tumour-Adjacent Fibroblasts;TAFs) or core (Tumour Core Fibroblasts;TCFs) of human LUAD. We introduce the concept of the "colocatome" as a spatial -omic dimension to catalogue all proximate and distant colocalisations between malignant and fibroblast subpopulations in both the assembloids and clinical specimens. The colocatome expands upon the colocalisation quotient (CLQ) through a nomalisation strategy that involves permutation analysis and thereby allows comparisons of CLQs under different conditions. Using colocatome analysis, we report that both TAFs and TCFs protected cancer cells from targeted oncogene treatment by uniquely reorganising the tumour-stroma cytoarchitecture, rather than by promoting cellular heterogeneity or selection. Moreover, we show that the assembloids' colocatome recapitulates the tumour-stroma cytoarchitecture defining the tumour microenvironment of LUAD clinical samples and thereby can serve as a functional spatial readout to guide translational discoveries.

NR2F1 Regulates TGF-β1-Mediated Epithelial-Mesenchymal Transition Affecting Platinum Sensitivity and Immune Response in Ovarian Cancer

The mechanism underlying platinum resistance in ovarian cancer (OC) remains unclear. We used bioinformatic analyses to screen differentially expressed genes responsible for platinum resistance and explore NR2F1′s correlation with prognostic implication and OC staging. Moreover, Gene-set enrichment analysis (GSEA) and Gene Ontology (GO) analyses were used for pathway analysis. Epithelial-mesenchymal transition (EMT) properties, invasion, and migration capacities were analyzed by biochemical methods. The association between NR2F1 and cancer-associated fibroblast (CAF) infiltration and immunotherapeutic responses were also researched. A total of 13 co-upregulated genes and one co-downregulated gene were obtained. Among them, NR2F1 revealed the highest correlation with a poor prognosis and positively correlated with OC staging. GSEA and GO analysis suggested the induction of EMT via TGFβ-1 might be a possible mechanism that NR2F1 participates in resistance. In vitro experiments showed that NR2F1 knockdown did not affect cell proliferation, but suppressed cell invasion and migration with or without cisplatin treatment through the EMT pathway. We also found that NR2F1 could regulate TGF-β1 signaling, and treating with TGF-β1 could reverse these effects. Additionally, NR2F1 was predominantly associated with immunosuppressive CAF infiltration, which might cause a poor response to immune check blockades. In conclusion, NR2F1 regulates TGF-β1-mediated EMT affecting platinum sensitivity and immune response in OC patients.

Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort

Simple Summary

Pleural effusion (PE) occurs as a consequence of various pathologies. Malignant effusion due to lung cancer is one of the most frequent causes. A method for accurate differentiation of malignant from benign PE is an unmet clinical need. Proteomics profiling of PE has shown promising results. However, mass spectrometry (MS) analysis typically involves the tedious elimination of abundant proteins before analysis, and clinical annotation of proteomics profiled cohorts is limited. This study compares the proteomes of malignant PE and nonmalignant PE, identifies lung cancer malignant markers in agreement with other studies, and identifies markers strongly associated with patient survival.

Abstract

Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10⁻⁶).

A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.

Long non-coding RNAs PGM5-AS1 upregulates Decorin (DCN) to inhibit cervical cancer progression by sponging miR-4284

Long non-coding RNAs (lncRNAs) have been widely studied and play crucial roles in cervical cancer (CC) progression. Here, we investigated the function and mechanism of lncRNA PGM5-AS1 action in CC cells. Using real-time quantitative polymerase chain reaction or western blotting, PGM5-AS1 and decorin (DCN) were downregulated in CC tissues and cells, whereas miR-4284 was upregulated. Luciferase assay, RNA pull-down assay, and western blotting showed that PGM5-AS1 could sponge miR-4284 to upregulate DCN expression in CC cells. Additionally, cell functional experiments showed that PGM5-AS1 overexpression led to decreased proliferation, migration, and invasion of CC cells. However, the inhibitory effect of PGM5-AS1 overexpression on CC cells was partly relieved by DCN knockdown because of the targeting interaction between PGM5-AS1, miR-4284, and DCN. In summary, this study identified that PGM5-AS1 negatively regulates CC cell malignancy by targeting miR-4284/DCN.

Osteoclasts differential-related prognostic biomarker for osteosarcoma based on single cell, bulk cell and gene expression datasets

Osteosarcoma (OS) is one of the most common primary bone malignant tumors. Osteoclasts have been shown to have a valuable role in OS. In the present study, we analyzed the differentiation states of osteoclasts in OS and their prognostic significance based on integrated scRNA-seq and bulk RNA-seq data. Osteoclasts in distinct differentiation states were characterized, and 661 osteoclasts differentiation-related genes (ODRGs) were obtained. ORDGs in distinct differentiation states were enriched in distinct functions and pathways. TPM1, S100A13, LOXL1, PSMD10, ST3GAL4, PEF1, SERPINE2, TUBB, FAM207A, TUBA1A, and DCN were identified as the significant survival-predicting ODRGs. We successfully developed a risk score model based on these survival-predicting ODRGs. In addition, we generated a nomogram applicable for clinical with both ODRGs signatures and clinicopathological parameters, and validated in OS cohorts to predict OS patient outcome. This study proposed and verified the important roles of osteoclasts differentiation in the prognosis of patients with OS, suggesting promising therapeutic targets for OS.

Matrix Remodeling-Associated Protein 8 as a Novel Indicator Contributing to Glioma Immune Response by Regulating Ferroptosis

Glioma is a highly malignant brain tumor with a poor survival rate. Novel biomarkers that act as prompt indicators of glioma are urgently needed. In this study, we identified and validated prognosis-related differentially expressed genes by datasets of glioma in the GEO and TCGA databases. Ferroptosis is a newly recognized process of cell death playing a vital role in cancer biology. Pearson correlation coefficient were used to discovery the prognosis-related genes which have the highest correlation with ferroptosis. Matrix remodeling-associated protein 8 (MXRA8) was identified as a novel prognosis indicator which may be involved in ferroptosis. The expression of MXRA8 was significantly higher in glioma compared with normal brain tissue, and increased expression of MXRA8 was associated with unfavorable survivals. Furthermore, in vitro analysis showed that knockdown of MXRA8 inhibited the cell viability in T98G and U251 cells and increased the sensitivity of glioma cells to temozolomide. We further observed that downregulation of MXRA8 elevated the levels of intracellular ferrous iron and lipid peroxidation, accompanied by upregulation of NCOA4 and suppression of FTH1. Moreover, co-expression analyses showed that GO term and KEGG pathways were mainly enriched in immunity-related pathways, such as neutrophil-related immunity, adaptive immune response, and cytokine binding. Through ssGSEA algorithm and TISIDB database, immunological analyses showed that MXRA8 was significantly correlated with various immune infiltration cells including NK cells, macrophages, and neutrophils. Meanwhile, MXRA8 was also associated with chemokines and multiple immunoinhibitory molecules, such as TGF-β1, IL-10, PD-L1, and CTLA4. We also found that MXRA8 was positively associated with immune infiltration score, and patients with higher immune score underwent worse overall survivals. Moreover, IHC staining indicated a highly positive correlation of MXRA8 with a macrophage marker CSF1R. The co-cultured models of glioma cells and M2 macrophages showed MXRA8 knockdown glioma cells alleviated the infiltration of M2 macrophage, while the reduced M2 macrophage infiltration generated by MXRA8 could be rescued by Fer-1 treatment. These results suggest that MXRA8 promotes glioma progression and highlight the pivotal role of MXRA8 in ferroptosis and immune microenvironment of glioma. Therefore, MXRA8 may serve as a novel prognostic marker and therapeutic target for glioma.

Prognostic Value and Therapeutic Potential of CBX Family Members in Ovarian Cancer

Background: Ovarian cancer (OV) is one of the common malignant tumors and has a poor prognosis. Chromobox (CBX) family proteins are critical components of epigenetic regulation complexes that repress target genes transcriptionally via chromatin modification. Some studies have investigated the function specifications among several CBXs members in multiple cancer types, however, little is known about the functions and prognostic roles of distinct CBXs family proteins in ovarian cancer.

Methods: In this study, several bioinformatics databases and in vitro experiments were used to analyze the expression profiles, prognostic values, and therapeutic potential of the CBXs family (CBX1-8) in ovarian cancer.

Results: It was found that higher expression of CBX3/8 and lower expression of CBX1/6/7 were detected in OV tissues. CBX2/4/5/8 were significantly correlated with individual cancer stages of OV. The expression of CBX1/2/3 were all significantly associated with worse overall survival (OS) and progression-free survival (PFS) for OV patients, whereas the expression of other five CBXs members showed either irrelevant (CBX5 and CBX8) or inconsistent (CBX4, CBX6, and CBX7) results for both OS and PFS in OV. These results showed that only CBX3 had consistent results in expression and prognosis. Further cell experiments also showed that CBX3 promoted the proliferation of ovarian cancer cells. CBX3 was highly expressed in chemoresistant OV tissues. These results indicated that CBX3 was the most likely prognostic indicator and new therapeutic target in OV. Furthermore, gene enrichment analysis suggests that the CBXs family was primarily involved in mast cell activation and mast cell mediated immunity. Individual CBXs members were associated with varying degrees of the infiltration of immune cells, especially B cells. Finally, a high genetic alteration rate of CBXs family (39%) was observed in OV. The low methylation status of CBX3/8 in OV may be associated with their high expression levels.

Conclusions: Taken together, these findings exhibited the pivotal value of CBXs family members (especially CBX3) in the prognosis and chemoresistance of ovarian cancer. Our results may provide new insight to explore new prognostic biomarkers and therapeutic targets for ovarian cancer.

Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

Aberrant expression of PROS1 correlates with human papillary thyroid cancer progression

Background

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). Considering the important association between cellular immunity and PTC progression, it is worth exploring the biological significance of immune-related signaling in PTC.

Methods

Several bioinformatics tools, such as R software, WEB-based Gene SeT AnaLysis Toolkit (WebGestalt), Database for Annotation, Visualization and Integrated Discovery (DAVID), Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to identify the immune-related hub genes in PTC. Furthermore, in vitro experiments were adopted to identify the proliferation and migration ability of PROS1 knockdown groups and control groups in PTC cells.

Results

The differentially expressed genes (DEGs) of five datasets from Gene Expression Omnibus (GEO) contained 154 upregulated genes and 193 downregulated genes, with Protein S (PROS1) being the only immune-related hub gene. Quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) have been conducted to prove the high expression of PROS1 in PTC. Moreover, PROS1 expression was significantly correlated with lymph nodes classification. Furthermore, knockdown of PROS1 by shRNAs inhibited the cell proliferation and cell migration in PTC cells.

Conclusions

The findings unveiled the clinical relevance and significance of PROS1 in PTC and provided potential immune-related biomarkers for PTC development and prognosis.

Glycolysis Define Two Prognostic Subgroups of Lung Adenocarcinoma With Different Mutation Characteristics and Immune Infiltration Signatures

Increasing studies have proved that malignant tumors are associated with energy metabolism. This study was aimed to explore biological variables that impact the prognosis of patients in the glycolysis-related subgroups of lung adenocarcinoma (LUAD). The mRNA expression profiling and mutation data in large LUAD samples were collected from the Cancer Genome Atlas (TCGA) database. Then, we identified the expression level and prognostic value of glycolysis-related genes, as well as the fractions of 22 immune cells in the tumor microenvironment. The differences between glycolysis activity, mutation, and immune infiltrates were discussed in these groups, respectively. Two hundred fifty-five glycolysis-related genes were identified from gene set enrichment analysis (GSEA), of which 43 genes had prognostic values (p < 0.05). Next, we constructed a glycolysis-related competing endogenous RNA (ceRNA) network which related to the survival of LUAD. Then, two subgroups of LUAD (clusters 1 and 2) were identified by applying unsupervised consensus clustering to 43 glycolysis-related genes. The survival analysis showed that the cluster 1 patients had a worse prognosis (p < 0.001), and upregulated differentially expressed genes (DEGs) are interestingly enriched in malignancy-related biological processes. The differences between the two subgroups are SPTA1, KEAP1, USH2A, and KRAS among top 10 mutated signatures, which may be the underlying mechanism of grouping. Combined high tumor mutational burden (TMB) with tumor subgroups preferably predicts the prognosis of LUAD patients. The CIBERSORT algorithm results revealed that low TMB samples were concerned with increased infiltration level of memory resting CD4+ T cell (p = 0.03), resting mast cells (p = 0.044), and neutrophils (p = 0.002) in cluster 1 and high TMB samples were concerned with increased infiltration level of memory B cells, plasma cells, CD4 memory-activated T cells, macrophages M1, and activated mast cells in cluster 2, while reduced infiltration of monocytes, resting dendritic cells, and resting mast cells was captured in cluster 2. In conclusion, significant different gene expression characteristics were pooled according to the two subgroups of LUAD. The combination of subgroups, TMB and tumor-infiltrating immune cell signature, might be a novel prognostic biomarker in LUAD.

The screening of immune-related biomarkers for prognosis of lung adenocarcinoma

Lung adenocarcinoma (LUAD) accounts for a frequently seen non-small cell lung cancer (NSCLC) histological subtype, and it is associated with dismal prognostic outcome. However, the benefits of traditional treatment are still limited, and the efficacies of immunotherapy are quite different. Therefore, it is of great significance to identify novel immune-related therapeutic targets in lung adenocarcinoma. In this study, we identified a set of immune-related biomarkers for prognosis of lung adenocarcinoma, which could provide new ideas for immunotherapy of lung adenocarcinoma. Datasets related to LUAD were filtered from the GEO database. The appropriate packages were used to identify differentially expressed genes (DEGs) and to carry out enrichment analysis, followed by the construction of prognostic biomarkers. The Kaplan-Meier (K-M) curves were plotted to analyze patient survival based on hub genes. Associations between the expression of selected biomarkers and six types of tumor-infiltrating immune cells were evaluated based on the online tool TIMER. After analyzing five GEO datasets(GSE32867, GSE46539, GSE63459, GSE75037 and GSE116959), we discovered altogether 67 DEGs, among which, 15 showed up-regulation while 52 showed down-regulation. Enrichments of integrated DEGs were identified in the ontology categories. CAV1, CFD, FMO2 and CLEC3B were eventually selected as independent prognostic biomarkers, they were correlated with clinical outcomes of LUAD patients. Moreover, a positive correlation was observed between biomarker expression and all different types of immune infiltration, and the expression level of the four biomarkers was all positively related to macrophage.

Use of cucurbitacins for lung cancer research and therapy

As the main substance in some traditional Chinese medicines, cucurbitacins have been used to treat hepatitis for decades in China. Currently, the use of cucurbitacins against cancer and other diseases has achieved towering popularity among researchers worldwide, as detailed in this review with summarized tables. Numerous studies have reported the potential tumor-killing activities of cucurbitacins in multiple aspects of human malignancies. Continuous research on its anticancer activity mechanisms also brings a glimmer of light to the treatment of patients with lung cancer. In line with the promising roles of cucurbitacins against cancer, through various molecular signaling pathways, it is justifiable to propose the use of cucurbitacins as a potential mainline chemotherapy before the onset and after the diagnosis of lung cancers. Here, this article mainly summarized the findings about the biological functions and underlying mechanisms of cucurbitacins on lung cancer pathogenesis and treatment. In addition, we also discussed the safety and efficacy of their application for further research and even clinical practice.

The deubiquitinase USP36 Regulates DNA replication stress and confers therapeutic resistance through PrimPol stabilization

PrimPol has been recently identified as a DNA damage tolerant polymerase that plays an important role in replication stress response. However, the regulatory mechanisms of PrimPol are not well defined. In this study, we identify that the deubiquitinase USP36 interferes with degradation of PrimPol to regulate the replication stress response. Mechanistically, USP36 is deubiquitinated following DNA replication stress, which in turn facilitates its upregulation and interaction with PrimPol. USP36 deubiquitinates K29-linked polyubiquitination of PrimPol and increases its protein stability. Depletion of USP36 results in replication stress-related defects and elevates cell sensitivity to DNA-damage agents, such as cisplatin and olaparib. Moreover, USP36 expression positively correlates with the level of PrimPol protein and poor prognosis in patient samples. These findings indicate that the regulation of PrimPol K29-linked ubiquitination by USP36 plays a critical role in DNA replication stress and chemotherapy response.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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.

Caveolin-1 promotes radioresistance via IRGM-regulated autophagy in lung cancer

Background

Radiotherapy is the standard therapeutic approach for non-small cell lung cancers (NSCLCs). However, radiotherapy resistance accounts for major treatment failures in NSCLC patients. Recently, targeting autophagy-related signaling has shown potential to improve radiotherapy. Furthermore, some studies have reported that caveolin-1 (Cav1), a primary scaffolding protein of caveolae, is positively associated with NSCLC progression and cell autophagy. However, the function of Cav1-mediated autophagy in NSCLC radioresistance remains largely unknown.

Methods

The NSCLC irradiation (IR)-resistant cell lines H358-IRR and A549-IRR were used for in vitro analysis. Real-time quantitative PCR (qPCR), western blot, cell counting kit-8 (CCK-8), colony formation and transmission electron microscopy analyses were performed to explore the relationship between Cav1 and immunity-related GTPase family M protein (IRGM)-regulated autophagy in the radiation resistance of lung cancers.

Results

Cav1 was significantly overexpressed in H358-IRR and A549-IRR cells compared to their parental counterparts. Knockdown of Cav1 significantly decreased the proliferation of IR-resistant NSCLC cells. Combinational treatment of IR and siRNA of Cav1 showed enhanced inhibition of the cell viability and colony formation of IR-resistant NSCLC cells. In addition, Cav1 overexpression could upregulate the autophagic proteins microtubule associated protein 1 light chain 3 II (LC3 II), Beclin-1 and Sequestosome 1 (SQSTM1/p62) in parental NSCLC cells, while Cav1 downregulation by siRNA inhibited the expression of LC3 II, Beclin-1 and p62 and the formation of autophagosomes in IR-resistant NSCLC cells. Furthermore, we observed that IRGM was downregulated after knockdown of Cav1 in IR-resistant NSCLC cells. Thus, Cav1 was observed to promote autophagy and increase IR-resistant cell survival by targeting IRGM.

Conclusions

The results of our study showed that Cav1 is involved in the development of IR resistance in NSCLC through IRGM-regulated autophagy and can be considered as a potential therapeutic target for improving the radiosensitivity of NSCLC.

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Background

Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.

Method

RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).

Results

Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.

Conclusion

In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer

Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis (p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival (p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient.

In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.

A two-gene-based prognostic signature for pancreatic cancer

The purpose of this study was to identify a vital gene signature that has prognostic value for pancreatic cancer based on gene expression datasets from the Cancer Genome Atlas and Gene Expression Omnibus. A total of 34 genes were obtained by the univariate analysis, which were significantly associated with the overall survival of PC patients. After further analysis, Anillin (ANLN) and Histone H1c (HIST1H1C) were identified and considered to be the most significant prognostic genes among the 34 genes. A prognostic model based on these two genes was constructed, and successfully distinguished pancreatic cancer survival into high-risk and low-risk groups in the training set and testing set. Subsequently, independent predictive factors, including the age, margin condition and risk score, were then employed to construct the nomogram model. The area under curve for the nomogram model was 0.826 at 0.5 years and 0.726 at 1 year, and the C-index of the nomogram model was 0.664 higher than the others variables alone. These findings have indicated that high expression of ANLN and HIST1H1C predicted poor outcomes for patients with pancreatic cancer. The nomogram model based on the expression of two genes could be valuable for the guidance of clinical treatment.

Roles of Proteoglycans and Glycosaminoglycans in Cancer Development and Progression

The extracellular matrix (ECM) spatiotemporally controls cell fate; however, dysregulation of ECM remodeling can lead to tumorigenesis and cancer development by providing favorable conditions for tumor cells. Proteoglycans (PGs) and glycosaminoglycans (GAGs) are the major macromolecules composing ECM. They influence both cell behavior and matrix properties through direct and indirect interactions with various cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes, and glycoproteins within the ECM. The classical features of PGs/GAGs play well-known roles in cancer angiogenesis, proliferation, invasion, and metastasis. Several lines of evidence suggest that PGs/GAGs critically affect broader aspects in cancer initiation and the progression process, including regulation of cell metabolism, serving as a sensor of ECM's mechanical properties, affecting immune supervision, and participating in therapeutic resistance to various forms of treatment. These functions may be implemented through the characteristics of PGs/GAGs as molecular bridges linking ECM and cells in cell-specific and context-specific manners within the tumor microenvironment (TME). In this review, we intend to present a comprehensive illustration of the ways in which PGs/GAGs participate in and regulate several aspects of tumorigenesis; we put forward a perspective regarding their effects as biomarkers or targets for diagnoses and therapeutic interventions.

Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

BACKGROUND In recent years, the morbidity and mortality rates of lung adenocarcinoma in non-smoking females have been increasing dramatically. Although much research has been done with some progress, the molecular mechanism remains unclear. In this study we aimed to estimate hub genes and infiltrating immune cells in non-smoking females with lung adenocarcinoma. MATERIAL AND METHODS Firstly, we obtained differentially expressed genes (DEGs) by GEO2R analysis based on 3 independent mRNA microarray datasets of GSE10072, GSE31547, and GSE32863. The DAVID database was utilized for functional enrichment analysis of DEGs. Moreover, we identified hub genes with prognostic value by STRING, Cytoscape, and Kaplan Meier plotter. Subsequently, these genes were further analyzed by Gene Expression Profiling Interactive Analysis, Oncomine, Tumor Immune Estimation Resource, and Human Protein Atlas. Finally, the immune infiltration analysis was performed by CIBERSORT and The Cancer Genome Atlas with R packages. RESULTS We found 315 DEGs enriching in the extracellular matrix organization, cell adhesion, integrin binding, angiogenesis, and hypoxic response. And among these DEGs, we identified 10 hub genes (SPP1, ENG, ATF3, TOP2A, COL1A1, PAICS, CAV1, CAT, TGFBR2, and ANGPT1) of significant prognostic value. Simultaneously, we illustrated the distribution and differential expressions of 22 immune cell subtypes. and dendritic cells resting and macrophages M1 were identified with prognostic significance. CONCLUSIONS The results indicated that 10 hub genes and 2 immune cell subtypes might be promising biomarkers for lung adenocarcinoma in non-smoking females. This finding needs to be further evaluated.

Inhibition of lncRNA PART1 Chemosensitizes Wild Type but Not KRAS Mutant NSCLC Cells

Background

Lung cancer has the highest incidence among solid tumors in men and is the third most common cancer in women. Despite improved understanding of genomic and mutational landscape in non-small cell lung cancer (NSCLC), the five-year survival in these patients has remained stagnant at a dismal 15%. The first line of treatment commonly adapted for NSCLC patients with somatic mutation in EGFR is tyrosine kinase inhibitor gefitinib or erlotinib. EGFR mutant cells seem to be intrinsically sensitive to tyrosine kinase inhibitors; however, the remaining 20-30% patients are resistant to tyrosine kinase inhibitor.

Materials and Methods

Here we show, using in vitro normal and NSCLS cell lines, that the lncRNA Prostate androgen-regulated transcript 1 (PART1) is expressed at higher levels in NSCLC cells compared to normal lung epithelial cell line, corroborating two earlier studies.

Results

We additionally show that these cells are resistant to erlotinib which is reversed in some, but not all, cell lines following suppression of PART1 expression. The differential response to erlotinib following siRNA-mediated knockdown of PART1 was found to be related to the mutational status of KRAS. Only in cells with wild-type KRAS suppression of PART1 sensitized them to erlotinib. Knockdown of mutant KRAS did not sensitize those cell lines to erlotinib. But knockdown of mutant KRAS along with suppression of PART1 sensitized the cells to treatment with erlotinib. The results from the study reveal a yet undefined and important role of lncRNA PART1 in defining sensitivity to erlotinib. This action is mediated by mutation status of KRAS.

Conclusion

Even though preliminary, our results indicate PART1 might be a potential candidate for targeted therapy or used as a predictor of chemosensitivity in patients with NSCLC.

Identification of gene expression and DNA methylation of SERPINA5 and TIMP1 as novel prognostic markers in lower-grade gliomas

Background

Lower-grade gliomas (LGGs) is characteristic with great difference in prognosis. Due to limited prognostic biomarkers, it is urgent to identify more molecular markers to provide a more objective and accurate tumor classification system for LGGs.

Methods

In the current study, we performed an integrated analysis of gene expression data and genome-wide methylation data to determine novel prognostic genes and methylation sites in LGGs.

Results

To determine genes that differentially expressed between 44 short-term survivors (<2 years) and 48 long-term survivors (≥2 years), we searched LGGs TCGA RNA-seq dataset and identified 106 differentially expressed genes. SERPINA5 and TIMP1 were selected for further study. Kaplan-Meier plots showed that SERPINA5 and TIMP1 expression were significantly correlated with overall survival (OS) and relapse-free survival (RFS) in TCGA LGGs patients. We next validated the correlation between the candidate genes expression and clinical outcome in CGGA LGGs patients. Multivariate analysis showed that TIMP1 mRNA expression had a significant prognostic value independent of other variables (HR = 4.825, 95% CI = 1.370-17.000, P = 0.014). Then, differential methylation sites were identified from differentially candidate gene expression groups, and all four methylation sites were significantly negatively correlated with gene expression (spearman r <  - 0.5, P < 0.0001). Moreover, hyper-methylation of four methylation sites indicated better OS (P < 0.05), and three of them also shown statistical significantly association with better RFS, except for SERPINA5 cg15509705 (P = 0.0762).

Conclusion

Taken together, these findings indicated that the gene expression and methylation of SERPINA5 and TIMP1 may serve as prognostic predictors in LGGs and may help to precise the current histology-based tumors classification system and to provide better stratification for future clinical trials.

Conjoint analysis of lncRNA and mRNA expression in rotator cuff tendinopathy

Background

Rotator cuff tendinopathy (RCT) is a common musculoskeletal disorder in the shoulder, whose underlying mechanism is unknown. Long non-coding RNAs (lncRNAs) are involved in the development of various diseases, but little is known about their potential roles in RCT.

Methods

In this study, we profiled lncRNAs and mRNAs involved in RCT in comparison with the normal tendon (NT) by RNA sequencing (RNA-Seq), to identify potential therapeutic targets. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway, competing endogenous RNA (ceRNA), and co-expression network construction were used to identify the potential functions of these RNAs. Three lncRNAs and three mRNAs were validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

Results

In total, 419 lncRNAs and 1,541 mRNAs were differentially expressed between the RCT and NT groups with a fold change of >2 and P of <0.01. The GO and KEGG pathway analyses showed that the differentially expressed mRNAs were mainly enriched in complement activation and involved in the citrate cycle. The ceRNA network showed the interaction of differentially expressed RNAs, comprising 139 lncRNAs, 126 mRNAs, and 35 miRNAs. NONHSAT209114.1, ENST00000577806, NONHSAT168464.1, PLK2, TMEM214, and IGF2 were validated by PCR. We constructed a co-expressed network of these validated RNAs.

Conclusions

We preliminarily analyzed the profile of lncRNAs and mRNAs in RCT. The bioinformatic analysis revealed several potential therapeutic targets for RCT.

Clinicopathological characteristics and prediction of cancer-specific survival in large cell lung cancer: a population-based study

Background

To describe the demographic and clinical characteristics of large cell lung cancer (LCLC) with a population-based database and to find the prognosis factors of cancer-specific survival (CSS) for these patients; also, to develop a nomogram to independently validate and predict the CSS for LCLC based on the identified prognosis factors.

Methods

We extracted the LCLC patient’s information from the Surveillance, Epidemiology, and End Results (SEER) database [2005–2014] and summarized the characteristics of the extracted factors. We used Cox proportional hazards regression to find the prognosis factors for LCLC patients and to develop the nomogram based on these in a split train cohort from the extracted data. The validation of the developed nomograms was performed in an independent validation cohort from the extracted data, in which the C-index and the average of the time-dependent area under the receiver operating characteristic curve (time-dependent AUC) for CSS in 1-year, 3-year, and 5-year CSS was calculated. The calibration curves were drawn to visualize the performance of the established nomogram.

Results

As a result, 4,936 patients with LCLC were identified from the SEER database. Nearly half of LCLC patients were diagnosed with stage IV; only approximately 20% of patients underwent surgery. The prognosis factors that influenced the LCLC patients included age, sex, American Joint Committee on Cancer (AJCC) stage, race, surgery, tumor size, and marital status. The calculated C-index was 0.701±0.01, and the mean time-dependent AUC for in 1-year, 3-year, and 5-year CSS was 0.88. The calibrated curve showed that the gap between the predicted and observed values for 1-year, 3-year, and 5-year CSS was small.

Conclusions

Sex, age, race, marital status, AJCC stage, surgery, and tumor size were shown to all be the independent prognostic factors of CSS in LCLC. The established nomogram can provide more precise evaluation for the survival of LCLC patients and help the clinicians in the individual management of patients.

CPS1 expression and its prognostic significance in lung adenocarcinoma

Background

Studies have increasingly shown that carbamoyl phosphate synthetase 1 (CPS1) plays a vital role in the occurrence and development of human malignant disease. Unfortunately, the detailed function of CPS1 in the development and prognosis of lung cancer, especially lung adenocarcinoma (LADC), is still not fully understood. In this research, we performed a comprehensive bioinformatics analysis with respect to the function of CPS1 in human LADC.

Methods

Several biological databases including UALCAN, GEPIA and Oncomine were used to analyze the expression of CPS1 in LADC. Meanwhile, TCGA and GEO databases were utilized to analyze relevant clinical data. In addition, databases including Methsurv, etc., were used to analyze CPS1 methylation levels in LADC.

Results

The Oncomine platform, UALCAN and gene expression profiling interactive analysis (GEPIA) were used and revealed that the expression levels of CPS1 were significantly increased in LADC tissues. Furthermore, we analyzed the methylation level of CPS1 in LADC and found that cases with high levels of CPS1 showed hypomethylated CPS1. The clinical data from the Wanderer database, which is linked to The Cancer Genome Atlas (TCGA) database, demonstrated that the expression and methylation values of CPS1 were both significantly related to the clinical characteristics and prognosis of LADC. Through analysis of the dataset from the Gene Expression Omnibus (GEO) database, we found that the expression level of CPS1 was markedly downregulated in human A549 lung cancer cells treated with the chemotherapeutic drug motexafin gadolinium (MGd) in a time-dependent manner.

Conclusions

Our work indicated that CPS1 is upregulated in LADC samples and that CPS1 might be used as a potential biomarker for the diagnostic and prognostic evaluation of LADC. Determining the detailed biological function of CPS1 in LADC tissues will provide promising and insightful information for our further study.

Identification of a long non-coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high- and low-risk groups based on median risk scores calculated using a prognosis-related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan-Meier (K-M) survival analysis of the high- and low-risk cohorts. Potential biomarkers associated with survival status were then identified using K-M survival analysis and potential biomarker functions were predicted using enrichment analysis of co-expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3- and 5-year survival with the model were 0.836 and 0.818, respectively, of a time-dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K-M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.

Role of downregulated ADARB1 in lung squamous cell carcinoma

Non-small cell lung cancer (NSCLC) is prevalent worldwide. Lung squamous cell carcinoma (LUSC) is one of the main subtypes of NSCLC yet, currently, few biomarkers are available for the diagnosis of LUSC. The present study aimed to investigate the expression and role of adenosine deaminase RNA specific B1 (ADARB1) in lung squamous cell carcinoma (LUSC). Integrative bioinformatics analysis was used to identify the effects of ADARB1 expression on the occurrence and prognosis of LUSC. The expression of ADARB1 was further examined by immunohistochemistry (IHC). Bioinformatics analysis suggested that ADARB1 was downregulated in LUSC, serving as a potential tumor suppressor, and these results were verified by IHC performed on a lung cancer tissue array. Clinical studies suggested that ADARB1 expression and methylation levels were significantly associated with patient characteristics in LUSC. Moreover, ADARB1 global methylation levels were upregulated in LUSC tissues compared with normal lung tissues. Higher methylation levels of cg24063645 were associated with shorter overall survival time of patients with LUSC. A negative correlation was identified between ADARB1 and epidermal growth factor receptor (EGFR) expression in LUSC. Using the Gene Expression Omnibus database, it was suggested that the expression of ADARB1 in LUSC was significantly different compared with that in lung adenocarcinoma. Furthermore, protein-protein interactions were studied and a biological process annotation analysis was conducted. The present study suggested that ADARB1 was downregulated in LUSC; therefore, ADARB1 may serve as a specific biomarker and a potential therapeutic target for LUSC.

Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Background

Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD.

Methods

Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model.

Results

We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors.

Conclusion

Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

Function of low ADARB1 expression in lung adenocarcinoma

Adenosine deaminase RNA-specific B1 (ADARB1), an adenosine-to-inosine (A-to-I) RNA-editing enzyme, has been found to play an essential role in the development of cancer. However, the specific function of ADARB1 in lung cancer, especially in lung adenocarcinoma (LUAD), is still not fully understood and requires further study. In our study, integrative bioinformatics were used to analyze the detailed function of ADARB1 in LUAD. By conducting bioinformatics analyses of several public databases, such as Gene Expression Profiling Interactive Analysis (GEPIA), GE-mini, and Oncomine, we found significantly decreased ADARB1 expression in LUAD cells and tissues. Moreover, RT-PCR and Western blot showed lower ADARB1 expression in H358 and A549 LUAD cells compared to human bronchial epithelial Beas-2B cells. Wound Healing Assay indicated that knockdown ADARB1 could promote LUAD cell metastasis. By using the Kaplan-Meier Plotter tool, we found that downregulation of ADARB1 was related to shorter first progression (FP), overall survival time (OS) and post-progression survival time (PPS). The relevant clinical data acquired from the Wanderer database indicated that the expression and methylation values of ADARB1 were significantly associated with the clinical characteristics of LUAD. Using DNA methylation inhibitor, we found DNMT inhibitor 5-aza-2-deoxycytidine (5-azaD) could promote the expression of ADARB1 and reverse the inhibition effect of ADARB1 in migration. In addition, functional enrichment analysis of ADARB1-associated coexpression genes was further conducted. Our investigation demonstrated that low levels of ADARB1 were specifically found in LUAD, and this gene might be a potential target in the diagnostic and prognostic evaluation of LUAD patients.

A tropomyosin receptor kinase family protein, NTRK2 is a potential predictive biomarker for lung adenocarcinoma

Neurotrophic receptor tyrosine kinase 2 (NTRK2) is a member of the tropomyosin receptor kinase family associated with the tumor development. However, the detailed function of NTRK2 in lung cancer, especially in lung adenocarcinoma (LUAD), is still not fully understood. Here, we investigated the effects of NTRK2 on LUAD biology. Through analyzing bioinformatics data derived from several databases, such as Oncomine, Gene Expression Profiling Interactive Analysis and UALCAN, we found that NTRK2 expression was significantly decreased in LUAD tissues. Clinical data acquired from Wanderer database, which is linked to The Cancer Genome Atlas database, demonstrated that the expression and methylation site of NTRK2 were significantly related to the clinical characteristics and prognosis of LUAD. Furthermore, NTRK2 expression was increased remarkably after treatment with the protein kinase B (AKT) inhibitor MK2206 and the anticancer agent actinomycin D. Functional enrichment analysis of NTRK2-associated coexpression genes was further conducted. Together, our results suggested that downregulated NTRK2 might be used in the diagnostic and prognostic evaluation of LUAD patients, or as a potential therapeutic target for the treatment of LUAD.