Microarray data analysis on gene and miRNA expression to identify biomarkers in non-small cell lung cancer

Optimizing cancer diagnosis: A hybrid approach of genetic operators and Sinh Cosh Optimizer for tumor identification and feature gene selection

The identification of tumors through gene analysis in microarray data is a pivotal area of research in artificial intelligence and bioinformatics. This task is challenging due to the large number of genes relative to the limited number of observations, making feature selection a critical step. This paper introduces a novel wrapper feature selection method that leverages a hybrid optimization algorithm combining a genetic operator with a Sinh Cosh Optimizer (SCHO), termed SCHO-GO. The SCHO-GO algorithm is designed to avoid local optima, streamline the search process, and select the most relevant features without compromising classifier performance. Traditional methods often falter with extensive search spaces, necessitating hybrid approaches. Our method aims to reduce the dimensionality and improve the classification accuracy, which is essential in pattern recognition and data analysis. The SCHO-GO algorithm, integrated with a support vector machine (SVM) classifier, significantly enhances cancer classification accuracy. We evaluated the performance of SCHO-GO using the CEC'2022 benchmark function and compared it with seven well-known metaheuristic algorithms. Statistical analyses indicate that SCHO-GO consistently outperforms these algorithms. Experimental tests on eight microarray gene expression datasets, particularly the Gene Expression Cancer RNA-Seq dataset, demonstrate an impressive accuracy of 99.01% with the SCHO-GO-SVM model, highlighting its robustness and precision in handling complex datasets. Furthermore, the SCHO-GO algorithm excels in feature selection and solving mathematical benchmark problems, presenting a promising approach for tumor identification and classification in microarray data analysis.

Circulating cell-free and extracellular vesicles-derived microRNA as prognostic biomarkers in patients with early-stage NSCLC: results from RESTING study

BACKGROUND

Factors to accurately stratify patients with early-stage non-small cell lung cancer (NSCLC) in different prognostic groups are still needed. This study aims to investigate 1) the prognostic potential of circulating cell-free (CF) and extracellular vesicles (EVs)-derived microRNA (miRNAs), and 2) their added value with respect to known prognostic factors (PFs).

METHODS

The RESTING study is a multicentre prospective observational cohort study on resected stage IA-IIIA patients with NSCLC. The primary end-point was disease-free survival (DFS), and the main analyses were carried out separately for CF- and EV-miRNAs. CF- and EV-miRNAs were isolated from plasma, and miRNA-specific libraries were prepared and sequenced. To reach the study aims, three statistical models were specified: one using the miRNA data only (Model 1); one using both miRNAs and known PFs (age, gender, and pathological stage) (Model 2), and one using the PFs alone (Model 3). Five-fold cross-validation (CV) was used to assess the predictive performance of each. Standard Cox regression and elastic net regularized Cox regression were used.

RESULTS

A total of 222 patients were enrolled. The median follow-up time was 26.3 (95% CI 25.4-27.6) months. From Model 1, three CF-miRNAs and 21 EV-miRNAs were associated with DFS. In Model 2, two CF-miRNAs (miR-29c-3p and miR-877-3p) and five EV-miRNAs (miR-181a-2-3p, miR-182-5p, miR-192-5p, miR-532-3p and miR-589-5p) remained associated with DFS. From pathway enrichment analysis, TGF-beta and NOTCH were the most involved pathways.

CONCLUSION

This study identified promising prognostic CF- and EV-miRNAs that could be used as a non-invasive, cost-effective tool to aid clinical decision-making. However, further evaluation of the obtained miRNAs in an external cohort of patients is warranted.

Anti-NSCLC role of SCN4B by negative regulation of the cGMP-PKG pathway: Integrated utilization of bioinformatics analysis and in vitro assay validation

Non-small cell lung cancer (NSCLC) is a malignant tumor with low overall cure and survival rates. Uncovering abnormally expressed genes is significantly important for developing novel targeted therapies in NSCLC. This study aimed to discover new differentially expressed genes (DEGs) of NSCLC. The DEGs of NSCLC were identified in eight data sets from Gene Expression Omnibus (GEO) database. The expression profiles and the prognostic significance of SCN4B in LUAD and LUSC were analyzed using GEPIA database. LinkedOmics was used to identify co-expressed genes with SCN4B, which were further subjected to KEGG pathway enrichment analysis. SCN4B-overexpressing plasmid (pcDNA/SCN4B) was transfected into A549 and NCI-H2170 cells to elevate the expression of SCN4B. MTT and TUNEL assays were performed to evaluate cell viability and apoptosis. Relying on the screened DEGs from GEO database, we identified that SCN4B was significantly downregulated in LUAD and LUSC. We confirmed the downregulation of SCN4B in NSCLC tissues using GEPIA database. SCN4B has a prognostic value in LUAD, but not LUSC. KEGG pathway enrichment analysis of SCN4B-related genes showed that cGMP-PKG signaling pathway might be involved in the role of SCN4B in NSCLC. Overexpression of SCN4B in A549 and NCI-H2170 cells inhibited the cell viability. Besides, SCN4B overexpression induced apoptosis of A549 and NCI-H2170 cells. SCN4B inhibited the expression of PKG1 and p-CREB in NSCLC cells. Moreover, the inhibitory effects of SCN4B on tumor malignancy were attenuated by the activator of PKG. In conclusion, integrated bioinformatical analysis proved that SCN4B was downregulated and had a prognostic significance in NSCLC. In vitro experimental studies demonstrated that SCN4B regulated NSCLC cells viability and apoptosis via inhibiting cGMP-PKG signaling pathway.

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Characterization and verification of MMP family members as potential biomarkers in kidney clear cell renal carcinoma

Renal cell carcinoma can arise from lesions in the renal epithelium. This particular type of cancer is prevalent in the realm of renal cancers and is associated with an unfavorable prognosis. Among these cases, over 70% are classified as kidney renal clear cell carcinoma (KIRC). Since the underlying causes of KIRC haven't been fully understood, there is an urgent need for deeper investigation into its pathogenesis. Various tools, software, and molecular analysis was used, including Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Cytoscape, University of ALabama at Birmingham CANcer data analysis Portal (UALCAN), muTarget, Gene Expression Profiling Interactive Analysis (GEPIA), OncoDB, Human Protein Atlas (HPA), cBioPortal, Kaplan-Meier (KM) plotter, Gene Set Enrichment Analysis (GSEA), Tumor IMmune Estimation Resource (TIMER), Encyclopedia of RNA Interactomes (ENCORI), DrugBank, Encyclopedia of RNA Interactomes (RT-qPCR), targeted bisulfide sequencing (bisulfide-seq), and receiver operating curve (ROC) to matrix metallopeptidase (MMP) gene family constituents, with the precise objective of identifying a small set of hub genes. These hub genes hold the potential to be harnessed as molecular biomarkers for KIRC. By performing STRING and CytoHubba analyses of the 24 MMP gene family members, MMP2 (matrix metallopeptidase 2), MMP9 (matrix metallopeptidase 9), MMP14 (matrix metallopeptidase 14), and MMP16 (matrix metallopeptidase 16) were recognized as hub genes having highest degree scores. After conducting an in-depth expression analysis of MMP2, MMP9, MMP14, and MMP16 using various The Cancer Genome Atlas (TCGA) databases and RT-qPCR techniques, these displayed a significant increase in expression at both the mRNA and protein levels within KIRC samples when compared to control samples. The impact of the over expression of MMP2, MMP9, MMP14, and MMP16 also left a distinct mark on the worst overall survival (OS) rates of KIRC patients. Furthermore, a targeted bisulfide-seq investigation unveiled a correlation between promoter hypomethylation patterns and the up-regulation of these key genes in KIRC patients. Additionally, hub genes were involved in various diverse oncogenic pathways. In conclusion, four MMP gene family members, including MMP2, MMP9, MMP14, and MMP16 may serve as therapeutic target and molecular biomarker in KIRC.

Clinical importance and PI3K/Akt pathway-dependent anti-proliferative role of PALMD and DPT in breast cancer

This study aimed to identify novel differentially expressed genes in breast cancer and to explore the clinical value and the anti-tumor or oncogenic effects of the identified genes using bioinformatics analysis and in vitro experiments. The differentially expressed genes in breast cancer patients were identified using Gene Expression Omnibus (GEO) database with the cut-off criteria p < 0.05 and |logFC| > 1. The expression levels of palmdelphin (PALMD) and dermatopontin (DPT) in normal tissues and breast cancer tissues were evaluated based on GEPIA and UALCAN databases. PALMD and DPT expression levels in clinical subgroups of patients with breast cancer were analyzed to assess the association of PALMD and DPT expression with clinical characteristics. The prognostic and diagnostic values of PALMD and DPT in breast cancer were evaluated from Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves. Pearson's correlation coefficient was performed using LinkedOmics. KEGG pathway enrichment analysis was performed using DAVID. The protein levels were evaluated using western blot analysis. Cell proliferation was assessed using MTT and EdU assays. Two important genes, PALMD and DPT, were identified in breast cancer. The expression levels of PALMD and DPT were significantly lower in breast cancer tissues. The expression levels of PALMD were closely related to age, histological type, and T stage of breast cancer patients. The expression levels of DPT were closely related to age, histological type, T stage, N stage, estrogen receptor status, and progesterone receptor status of breast cancer patients. The K-M survival curves showed that PALMD or DPT was not an independent prognostic factor for breast cancer. The ROC curves showed that both PALMD and DPT had good diagnostic potential for breast cancer. KEGG pathway enrichment results showed that PI3K/Akt pathway was an important overlapping signaling for PALMD and DPT. Further studies proved that overexpression of PALMD and DPT inhibited proliferation in MCF-7 and MDA-MB-231 cells by suppressing the PI3K/Akt pathway. PALMD and DPT knockdown promoted proliferation in MCF-7 and MDA-MB-231 cells by activating the PI3K/Akt pathway. These results collectively suggested that PALMD and DPT might serve as potential diagnostic biomarkers and therapeutic targets for breast cancer.

Identification of novel potential drugs and miRNAs biomarkers in lung cancer based on gene co-expression network analysis

Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DE miRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co-expression network. Next, four modules were selected based on the Zsummary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.

Silencing LCN2 enhances RSL3-induced ferroptosis in T cell acute lymphoblastic leukemia

BACKGROUND

T-cell acute lymphoblastic leukemia (T-ALL) is a life-threatening malignancy and therapeutic toxicity remains a huge challenge for survival rates. A novel iron-dependent form of cell death, ferroptosis, shows potentials in cancer therapy. This study aimed to identify ferroptosis-associated hub genes within a proteinprotein interaction (PPI) network.

METHODS

We screened differential expressed genes (DEGs) in GSE46170 dataset and obtained ferroptosis-related genes from FerrDb database. Through overlapping between DEGs and ferroptosis-related genes, ferroptosis-associated DEGs were identified for further PPI network construction. Molecular complex detection (MCODE) algorithm in Cytoscape was employed to determine tightly connected protein clusters. Chord diagram of Gene Ontology (GO) was generated to reveal the potential biological process of hub genes. Through transfection with siRNA of lipocalin 2 (LCN2) into TALL cells, the regulatory role of LCN2 in ferroptosis was investigated.

RESULTS

Venn diagram identified a total of 37 ferroptosis-associated DEGs between GSE46170 and ferroptosis-associated genes, which were mainly enriched in ferroptosis and necroptosis. Based on PPI network analysis, 5 hub genes (LCN2, LTF, HP, SLC40A1 and TFRC) were found. These hub genes were involved in iron ion transport and could distinguish T-ALL from normal individuals. Further experimental studies demonstrated that LCN2 was highly expressed in T-ALL, while silencing LCN2 promoted RSL3-induced ferroptotic cell death in T-ALL cells.

CONCLUSION

This study identified novel ferroptosis-associated hub genes, which shed new insights into the underlying mechanism of ferroptosis in T-ALL and also provide promising therapeutic targets for T-ALL.

Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients

The molecular mechanism of COVID-19's pathogenic effect on lung cancer patients is yet unknown. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, we also reported that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Also, our findings in concert with published literature suggest that molecular signatures like hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Overall, the scientific results of this research will aid in the formulation of suitable management strategies as well as the development of diagnostic and therapeutic methods for COVID-19-infected lung cancer patients.

Integrated analysis of competitive endogenous RNA networks in elder patients with non-small cell lung cancer

BACKGROUND

Lung cancer is one of the most prevalent cancers and the leading cause of cancer-related deaths worldwide; non-small cell lung cancer (NSCLC) comprises approximately 80% of all lung cancer cases. This study aimed to construct a competing endogenous RNA (ceRNA) network and identify prognostic signatures in elderly patients with NSCLC.

METHODS

We extracted data from elderly patients with NSCLC from The Cancer Genome Atlas and identified differentially expressed (DE) messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the functions of DEmRNAs. The interactions between RNAs were predicted using starBase, TargetScan, miRTarBase, and miRanda. Cytoscape version 3.0 was used to construct and visualize the lncRNA-miRNA-mRNA ceRNA network. The association between the expression levels of DERNAs in the constructed ceRNA network and overall survival was determined using the survival package in R software. Furthermore, another Gene Expression Omnibus cohort was studied to externally validate the ceRNA network.

RESULTS

In total, 2865 DEmRNAs, 62 DEmiRNAs, and 131 DElncRNAs were identified. Dysregulated mRNAs are enriched in cancer-related processes and pathways. A ceRNA network was constructed using 38 miRNAs, 61 lncRNAs, and 164 mRNAs. Of these, 3 lncRNAs, 3 miRNAs, and 16 mRNAs were closely related to overall survival. The MIR99AHG-hsa-miR-31-5p-PRKCE axis has been identified as a potential ceRNA network involved in the development of NSCLC in elderly individuals. External validation of the MIR99AHG-hsa-miR-31-5p-PRKCE axis in the GSE19804 cohort showed that PRKCE was downregulated and that MIR99AHG was upregulated in the tumor tissues of elderly patients with NSCLC compared with normal lung tissues.

CONCLUSIONS

This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential biomarkers for the diagnosis and prognosis of elderly patients with NSCLC.

MicroRNA-9-5p Is Involved in Lipopolysaccharide-Induced Acute Lung Injury Via the Regulation of Macrophage Polarization

MicroRNA (miR)-9-5 p has been shown to affect lung cancer progression and lung fibrosis, but the efficacy of miR-9-5 p in acute lung injury (ALI) remained indefinite. The study was performed to probe the modulating mechanism of miR-9-5 p in ALI via regulating macrophage polarization. The ALI mouse model was established and blood samples of ALI patients were obtained. MiR-9-5 p levels in ALI mice and ALI patients were detected. Mouse pulmonary macrophages were extracted from bronchoalveolar lavage fluid and polarized into M1 and M2 macrophages. Intervention of miR-9-5 p expression was performed to observe the effects on M1 polarization and M2 polarization in lung macrophages, inflammatory factors in BALF, wet/dry weight ratio (W/D) in lung tissues, myeloperoxidase (MPO) activity in lung tissues, and lung tissue lesion condition. MiR-9-5 p levels were elevated in the lung tissues of ALI mice and ALI patients. MiR-9-5 p silencing could repress lung macrophages in ALI mice polarized toward the M1 phenotype and promoted the polarization toward the M2 phenotype, reduced the lung lesions, the lung water content, and the secretion levels of the pro-inflammatory factors TNF-α, IL-6, and IL-1β in BALF, increased the secretion of the anti-inflammatory factor IL-10, as well as impeded the MPO activity in the lung tissues of ALI mice. MiR-9-5 p deletion ameliorates LPS-induced inflammatory infiltration in lung tissues via inhibiting the polarization of mouse lung macrophages to the M1 phenotype and promoting the polarization to the M2 phenotype.

Identification of gene profiles related to the development of oral cancer using a deep learning technique

BACKGROUND

Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning.

METHODS

Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes.

RESULTS

Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes.

CONCLUSIONS

Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Differential expression profile of microRNAs in the lung tissues of coal workers with pneumoconiosis and patients with silicosis

The purpose of this study was to characterize the microRNA (miRNA) profile of the lung tissues from coal workers' pneumoconiosis (CWP) and silicosis and to analyze the changes in downstream genes, biological processes, and signaling pathways based on the differently expressed miRNAs. Lung tissues from three CWP patients, eight silicosis patients, and four healthy controls were collected and analyzed for their miRNA profiles using Affymetrix® GeneChip® miRNA Arrays. Differentially expressed miRNAs (DEMs) were identified between the different groups. The miRanda and TargetScan databases were used to predict the putative target genes, and volcano and heat maps were drawn. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were then performed to screen the DEMs-associated biological process and signaling pathways, respectively. Further identification with a comprehensive literature research involving particle exposure, fibrosis, inflammation and lung cancer were used to further screen DEMs of CWP and silicosis. Microarray data showed that 375 and 88 miRNAs were differentially expressed in CWP and silicosis lung tissues compared with healthy lung tissues, while 34 miRNAs were differentially expressed in CWP compared with silicosis lung tissues. The GO and KEGG pathway analyses showed that, the target genes were mainly enriched in the TGF-β, MAPK, p53 and other signal pathways. These results provided insight into the miRNA-related underlying mechanisms of CWP and silicosis, and they provided new clues for miRNAs as biomarkers for the diagnosis and differential diagnosis of these two diseases.

Recent Developments and Challenges in Molecular-Targeted Therapy of Non-Small-Cell Lung Cancer

Treatment of lung cancer with conventional therapies, which include radiation, surgery, and chemotherapy results in multiple undesirable adverse or side effects. The major clinical challenge in developing new drug therapies for lung cancer is resistance, which involves mutations and disturbance in various signaling pathways. Molecular abnormalities related to epidermal growth factor receptor (EGFR), v-Raf murine sarcoma viral oncogene homolog B1 (B-RAF) Kirsten rat sarcoma virus (KRAS) mutations, translocation of the anaplastic lymphoma kinase (ALK) gene, mesenchymal-epithelial transition factor (MET) amplification have been studied to overcome the resistance and to develop new therapies for non-small cell lung cancer (NSCLC). But, inevitable development of resistance presents limits the clinical benefits of various new drugs. Here, we review current progress in the development of molecularly targeted therapies, concerning six clinical biomarkers: EGFR, ALK, MET, ROS-1, KRAS, and B-RAF for NSCLC treatment.

miR-6742-5p regulates the invasion and migration of lung adenocarcinoma cells via mediating FGF8/ERK12/MMP9/MMP2 signaling pathway

BACKGROUND

microRNAs (miRNAs) are involved in the progression of Lung adenocarcinoma (LUAD), however, the functions of miR-6742-5p in LUAD remains unknown, thereby this study was carried on.

METHODS

The mRNA and miRNA expression data from the LUAD and normal control were obtained from Gene Expression Omnibus (GEO) database, TargetScan and mirDIP were applied to predict the relationship between miR-6742-5p and FGF8.Q-PCR, western blot, dual-luciferase, wound Healing and transwell assays were performed to test the functions of miR-6742-5p in LUAD.

RESULTS

Bioinformatics analysis and dual-luciferase identified FGF8 is the target-gene of miR-6742-5p, which is declined in LUAD of human tissues and cell lines, and miR-6742-5P OE suppressed the progression of LUAD in nude mice. MiR-6742-5p OE and KD suppressed or increased the abilities of LUAD' metastasis tested by wound healing and transwell assays H522 and PC-9 cells, these effects about miR-6742-5p OE were reversed by FGF8; miR-6742-5p OE, KD inhibited and increased the expression of FGF8 as its downstream p-ERK1/2, MMP-2/-9, these results were corrected by ERK1/2 inhibitor: Ro 67-7476; the miR-6742-5p KD increased the migrated and invaded cells and suppressed by MMPs inhibitor: S3304. These results identified the negative correlation of miR-6742-5p with FGF8-ERK1/2 signal pathway in LUAD progression.

CONCLUSIONS

We conclude that miR-6742-5p might be a regulator of LUAD progression by targeting FGF8/ERK1/2/MMPs signaling pathway, which provides a novel therapeutic target for LUAD.

Identification of Autophagy-Related Targets of Berberine against Non-Small Cell Lung Cancer and Their Correlation with Immune Cell Infiltration By Combining Network Pharmacology, Molecular Docking, and Experimental Verification

OBJECTIVE

Non-small cell lung cancer (NSCLC) is the most common lung cancer type with high incidence. This study aimed to reveal the anti-NSCLC mechanisms of berberine and identify novel therapeutic targets.

METHODS

Berberine-related targets were acquired from SuperPred, SwissTargetPrediction, and GeneCards. NSCLC-re-lated targets were collected from GeneCards and DisGeNET. Differentially expressed genes (DEGs) were identified GEO database, UCSC Xena, and limma. GO and KEGG analyses were performed using clusterProfiler. Autophagy-related genes and transcriptional factors were collected from HADb and KnockTF, respectively. STRING and Cytoscape were used for PPI network analysis. Immune cell infiltration in NSCLC was assessed using CIBERSORT, and its correlation with autophagy-related targets was evaluated. Molecular docking was conducted using PyMOL and AutoDock. qRT-PCR and CCK-8 assay was used for in vitro verification.

RESULTS

Thirty intersecting targets of berberine-related targets, NSCLC-related targets, and DEGs were obtained. GO and KEGG analyses revealed that the intersecting targets were mainly implicated in oxidative stress, focal adhesion, and cell-substrate junction, as well as AGE-RAGE, relaxin, FoxO, and estrogen signaling pathways. Significantly, CAPN1, IKBKB, and SIRT2 were identified as the foremost autophagy-related targets, and 21 corresponding transcriptional factors were obtained. PPI network analysis showed that CAPN1, IKBKB, and SIRT2 interacted with 50 other genes. Fifty immune cell types, such as B cells naive, T cells CD8, T cells CD4 naive, T cells follicular helper, and monocytes, were implicated in NSCLC pathogenesis, and CAPN1, IKBKB, and SIRT2 were related to immune cells. Molecular docking revealed the favorable binding activity of berberine with CAPN1, IKBKB, and SIRT2. In vitro assays showed lower CAPN1, IKBKB, and SIRT2 expression in NSCLC cells than that in normal cells. Notably, berberine inhibited the viability and elevated CAPN1, IKBKB, and SIRT2 expression in NSCLC cells.

CONCLUSIONS

Berberine might treat NSCLC mainly by targeting CAPN1, IKBKB, and SIRT2.

A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer

BACKGROUND

Increasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients.

METHODS

This inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training group and an internal validation group. The nomogram was built with the training set, then internally verified with SEER data, and externally validated with two different data sets. Based on the RNA-seq data, ELNs-related DERNAs (DElncRNAs, DEmiRNAs, andDEmRNAs) and immune cells were identified. The LASSO-Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cell prognostic signature in The Cancer Genome Atlas (TCGA) cohort. The OS of subgroups with high- and low-ELN signature was compared using the Kaplan-Meier (K-M) analysis. A nomogram was successfully constructed based on the ELNs signature and other clinical characteristics. The concordance index (C-index), calibration plot, receiver operating characteristic curve, and decision curve analysis (DCA) were all used to evaluate the nomogram model. The meta-analysis, the Gene Expression Profiling Interactive Analysis database, and reverse transcription-quantitative PCR (RT-qPCR) were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal gastric tissues, respectively. Finally, we analyzed the correlations between immune checkpoints, chemotherapy drug sensitivity, and risk score.

RESULTS

The multivariate analysis revealed that the high ELNs improved OS compared with low ELNs (hazard ratio [HR] = 0.659, 95% confidence interval [CI]: 0.626-0.694, p < 0.0001). Using the training set, a nomogram incorporating ELNs was built and proven to have good calibration and discrimination (C-index [95% CI], 0.714 [0.710-0.718]), which was validated in the internal validation set (C-index [95% CI], 0.720 [0.714-0.726]), the TCGA set (C-index [95% CI], 0.693 [0.662-0.724]), and the Chinese set (C-index [95% CI], 0.750 [0.720-0.782]). An ELNs-related signature model based on ELNs group, regulatory T cells (Tregs), neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A, and hsa-miR-135a-5p was constructed by the LASSO-Cox regression analysis. The result showed that OS was remarkably lower in patients with high-ELNs signature compared with those with low-ELN signature (HR = 2.418, 95% CI: 1.804-3.241, p < 0.001). This signature performed well in predicting 1-, 3-, and 5-year survival (AUC [95% CI] = 0.688 [0.612-0.763], 0.744 [0.659-0.830], and 0.778 [0.647-0.909], respectively). The multivariate Cox analysis illustrated that the risk score was an independent predictor of survival for patients with GC. Moreover, the expression of prognostic genes (LINC00643, TMEM236, and hsa-miR-135a-5p) displayed differences between GC tissues and adjacent non-tumor tissues. The C-index of the nomogram that can be used to predict the OS of GC patients was 0.710 (95% CI: 0.663-0.753). Both the calibration plots and DCA showed that the nomogram has good predictive performance. Moreover, the signature was significantly correlated with the N stage and T stage. According to our analysis, GC patients in the low-ELN signature group may have a better immunotherapy response and OS outcome.

CONCLUSIONS

We explored the prognostic role of ELNs in GC and successfully constructed an ELNs signature linked to the GC prognosis in TCGA. The findings manifested that the signature is a powerful predictive indicator for patients with GC. The signature might contain potential biomarkers for treatment response prediction for GC patients. Additionally, we identified a novel and robust nomogram combining the characteristics of ELNs and clinical factors for predicting 1-, 3-, and 5-year OS in GC patients, which will facilitate personalized survival prediction and aid clinical decision-making in GC patients.

Immunotherapy combined with chemotherapy versus chemotherapy alone as the first-line treatment of PD-L1-negative and driver-gene-negative advanced nonsquamous non-small-cell lung cancer: An updated systematic review and meta-analysis

BACKGROUND

This meta-analysis aimed to compare the efficacy of immunotherapy combined with chemotherapy versus chemotherapy alone as the first-line therapy for patients with programmed death ligand-1 (PD-L1)-negative and driver-gene-negative advanced nonsquamous non-small-cell lung cancer (NSCLC).

PATIENTS AND METHODS

Eligible randomized trials were identified following the systematic search of PubMed, Cochrane Library, Embase, Web of Science, Wanfang Data, and China Knowledge Resource Integrated Database from January 2000 to June 2022.

RESULTS

Seven trials involving 1132 patients with PD-L1-negative and driver-gene-negative advanced nonsquamous NSCLC were included. Immunotherapy combined with chemotherapy showed significantly superior objective response rate (ORR) compared with chemotherapy alone (odds ratio 2.81, 95% confidence interval [CI] 1.69-4.65). Immunotherapy combined with chemotherapy also significantly prolonged the progression-free survival (PFS) (hazard ratio [HR] 0.63, 95% CI 0.55-0.74, p < 0.001) and overall survival (OS) (HR 0.68, 95% CI 0.56-0.82, p < 0.001) of patients with PD-L1-negative and driver-gene-negative advanced nonsquamous NSCLC compared to chemotherapy alone. In terms of ≥3 treatment-related adverse events, patients receiving immunotherapy combined with chemotherapy were at higher risk than chemotherapy alone (OR 1.73, 95% CI 1.47-2.05).

CONCLUSIONS

This meta-analysis suggested that immunotherapy combined with chemotherapy yielded a better ORR, PFS, and OS, and a higher incidence of treatment-related adverse events as the first-line therapy for patients with PD-L1-negative and driver-gene-negative nonsquamous advanced NSCLC in comparison to chemotherapy alone. A rational treatment protocol should be selected according to the individual condition of the patients.

The Role of the Selected miRNAs as Diagnostic, Predictive and Prognostic Markers in Non-Small-Cell Lung Cancer

Lung cancer remains a leading cause of cancer-related deaths worldwide, overtaking colon, breast, and prostate cancer-related deaths. Due to the limited diagnostic possibilities, it is often diagnosed after it has reached an advanced stage. The delayed diagnosis significantly worsens the patient’s prognosis. In recent years, we have observed an increased interest in the use of microRNAs (miRNAs) as diagnostic, predictive, and prognostic markers in non-small-cell lung cancer (NSCLC). The abnormal expression levels of the miRNAs could be used to detect NSCLC in its early stages while it is still asymptomatic. This could drastically improve the clinical outcome. Furthermore, some miRNAs could serve as promising predictive and prognostic factors for NSCLC. Some of the currently available studies have shown a correlation between the miRNAs’ levels and the sensitivity of tumour cells to different treatment regimens. Analysing and modulating the miRNAs’ expression could be a way to predict and improve the treatment’s outcome.

Autophagy Induced by BCL2-Related ceRNA Network Participates in the Occurrence of COPD

Purpose

Chronic obstructive pulmonary disease (COPD) is a predominant cause of mortality worldwide. Autophagy, which depends on a lysosomal degradation pathway, plays an essential role in the occurrence of COPD. The aim of our study was to identify the potential function of autophagy and construct a BCL2-related competing endogenous RNA (ceRNA) network that induces autophagy in COPD.

Methods

Blood sample data from GSE31568, GSE24709, and GSE61741 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs in COPD and controls were identified via GEO2R. Transcription factors were obtained from FunRich. DIANA, miRDB, miRTarBase, and TargetScan were used to predict target genes of miRNAs. Autophagy genes were collected from the Human Autophagy Database (HADb). The GSE151052 dataset was used to identify autophagy-related differentially expressed genes in tissues. Functional enrichment and protein–protein interaction (PPI) network analyses were conducted via Metascape and the STRING network. Spearman correlation analysis was used to analyze the relationship between autophagy-related differentially expressed genes and lung function. The BCL2-related ceRNA network was modeled by Cytoscape.

Results

We obtained 41 differentially expressed miRNAs and 10 significantly different transcription factors. We identified 19 autophagy-related differentially expressed genes that were significantly different (P<0.05) in tissue samples. The most significant enrichment in Metascape was an autophagy item, which further confirmed autophagy participation in the occurrence of COPD. PPI network analysis found four genes (BCL2, BECN1, MAPK8, and ITPR1), among which BCL2 was correlated with both FEV1/FVC and FEV1 prediction. Finally, the BCL2-related ceRNA network was constructed to clarify the interaction of RNAs and occurrence of autophagy, including 18 miRNAs and 65 lncRNAs.

Conclusion

We identified 19 autophagy-related differentially expressed genes that participated in COPD; among them, BCL2 was correlated with lung function, and a BCL2-related ceRNA network was constructed, which further revealed the potential mechanism of autophagy involvement in COPD.

Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma

Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here, we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman's correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), and cellular component (CC) as well as to pathways, specific organs, cells, and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene, and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally, methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

LINC00174 Suppresses Non-Small Cell Lung Cancer Progression by Up-Regulating LATS2 via Sponging miR-31-5p

Objective

Dysregulation of long non-coding RNAs (lncRNAs) is associated with the progression of non-small cell lung cancer (NSCLC). This study aimed to investigate the role of long intergenic non-protein coding RNA 174 (LINC00174) in NSCLC.

Materials and Methods

In this experimental study, LINC00174 expression in NSCLC tissues and cell lines was investigated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Besides, cell counting kit-8 (CCK-8), 5-bromo-2'-deoxyuridine (BrdU). Transwell and Flow Cytometry assays were applied to detect the regulatory function of LINC00174 on the growth, migration and apoptosis of NSCLC cells. Bioinformatics analysis, dual luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay predicted and verified the targeting relationship between LINC00174 and miR-31-5p, and between miR-31-5p and the 3´-untranslated region (3´UTR) of large tumor suppressor kinase 2 (LATS2), respectively. Western blotting was performed to detect the regulatory function of LINC00174 and miR-31-5p on LATS2 protein expression.

Results

Compared with that in normal lung tissues, LINC00174 expression in NSCLC tissues and cell lines was reduced. LINC00174 expression was negatively associated with the TNM stage of the patients. Functional experiments showed that LINC00174 overexpression inhibited NSCLC cell multiplication and migration, and induced apoptosis. Furthermore, LINC00174 targeted miR-31-5p and repressed its expression. Additionally, LINC00174 upregulated LATS2 expression through competitively binding to miR-31-5p.

Conclusion

LINC00174, as a competitive endogenous RNA, elevates LATS2 expression by adsorbing miR-31-5p, thereby inhibiting the viability and migration of NSCLC cells, and promoting apoptosis.

Analysis of Genetic Variants and the ceRNA Network of miR-9 in Non-Small Cell Lung Cancer

Objective: To explore the role of single-nucleotide polymorphisms (SNPs) in hsa-miR-9 in non-small cell lung cancer (NSCLC). Methods: Log-rank and Cox regression analyses were conducted to assess the association of four functional SNPs of miR-9 with overall survival (OS) of Chinese patients with NSCLC. A reporter luciferase assay was performed to examine the relationship between the SNPs and transcriptional activity of miR-9. The expression of miR-9 in cells was detected by quantitative real-time PCR assay. Xenograft model was established in nude mice, which were treated with Lv-MiR-9-mimics or Lv-miR-9-inhibitor. A long noncoding RNA (lncRNA)-miR-9-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was established based on bioinformatics analyses. Results: We found that rs1501672 was associated with the prognosis of 1001 Chinese NSCLC patients (A>G, additive model: adjusted hazard ratio = 0.89, 95% confidence interval = 0.79-1.00, p = 0.056). Luciferase reporter assay showed higher luciferase activity with wild A allele than that with mutant G allele in 293T, SPC-A1, and A549 cell lines. The miR-9 level was significantly higher in lung cancer cells than normal lung cells. miR-9 was also over expressed in lung cancer tissue according to The Cancer Genome Atlas and gene expression omnibus databases. Xenograft models based on H1299 cells showed that lv-miR-9-inhibitor significantly decreased tumor growth compared with the lv-miR-9-NC group (p < 0.001). Bioinformatics analysis showed that one target gene leukemia inhibitory factor receptor and two lncRNAs (KIAA0087 and GVINP1) were associated with OS of NSCLC patients. Conclusion: The rs1501672 of miR-9 was associated with the prognosis of NSCLC patients in the Chinese population. The lncRNA-miR-9-mRNA ceRNA network revealed potential molecular biological regulation pathways and prognostic biomarkers for NSCLC.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

Identification of a ZC3H12D-regulated competing endogenous RNA network for prognosis of lung adenocarcinoma at single-cell level

Background

To identify hub genes from the competing endogenous RNA (ceRNA) network of lung adenocarcinoma (LUAD) and to explore their potential functions on prognosis of patients from a single-cell perspective.

Methods

We performed RNA-sequencing of LUAD to construct ceRNA regulatory network, integrating with public databases to identify the vital pathways related to patients’ prognosis and to reveal the expression level of hub genes under different conditions, the functional enrichment of co-expressed genes and their potential immune-related mechanisms.

Results

ZC3H12D-hsa-miR-4443-ENST00000630242 axis was found to be related with LUAD. Lower ZC3H12D expression was significantly associated with shorter overall survival (OS) of patients (HR = 2.007, P < 0.05), and its expression was higher in early-stage patients, including T1 (P < 0.05) and N0 (P < 0.05). Additionally, ZC3H12D expression was higher in immune cells displayed by single-cell RNA-sequencing data, especially in Treg cells of lung cancer and CD8 T cells, B cells and CD4 T cells of LUAD. The functional enrichment analysis showed that the co-expressed genes mainly played a role in lymphocyte activation and cytokine-cytokine receptor interaction. In addition, ZC3H12D was associated with multiple immune cells and immune molecules, including immune checkpoints CTLA4, CD96 and TIGIT.

Conclusion

ZC3H12D-hsa-miR-4443-ENST00000630242 ceRNA network was identified in LUAD. ZC3H12D could affect prognosis of patients by regulating mRNA, miRNA, lncRNA, immune cells and immune molecules. Therefore, it may serve as a vital predictive marker and could be regarded as a potential therapeutic target for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08992-1.

Ultra-sensitive and high efficiency detection of multiple non-small cell lung cancer-related miRNAs on a single test line in catalytic hairpin assembly-based SERS-LFA strip

Accurate quantification of multiple miRNAs biomarkers in body fluid is still a challenge for early screening of cancer. Herein, by catalytic hairpin assembly as a signal amplification strategy, we designed a novel surface-enhanced Raman scattering (SERS)-lateral flow assay (LFA) strip for ultrasensitive detection of miR-21 and miR-196a-5p in non-small cell lung cancer (NSCLC) urine on a single test (T) line. 4-mercaptobenzoic acid or 5,5'-dithiobis-2-nitrobenzoic acid as Raman molecules was labeled and two hairpin DNA sequence was modified gold nanocages (GNCs) were designed as two SERS tags. Through target miRNA-triggered catalytic hairpin assembly (CHA), the double-stranded DNAs (H1-H2 complex) formed by SERS tags and the related hairpin-structured DNA sequence 2 (H2) were immobilized on a single T line of SERS-LFA strip. This generated abundant "hot spots" because of the formation of numerous H1-H2 complex thus facilitated the SERS measurement. Through this method, two kinds of miRNAs were analyzed, resulting in limits of detection of 2.08 pM and 3.31 pM for miR-21 in PBS buffer and human urine, 1.77 pM and 2.18 pM for miR-196a-5p in PBS buffer and human urine. Significantly, the SERS-LFA strip exhibited high specificity and good repeatability toward miRNAs. The whole detection time was only 30 min, which means that the high detection efficiency of the strip. The clinical feasibility of the proposed method was also evaluated by detecting the levels of miR-21 and miR-196a-5p in urine samples from NSCLC patients and healthy subjects. The developed SERS-LFA strip has wide application prospect in biomedical research, drug development and early clinical diagnosis.

The Regulatory Network and Potential Role of LINC00973-miRNA-mRNA ceRNA in the Progression of Non-Small-Cell Lung Cancer

Background

The occurrence and development of cancer could be promoted by abnormally competing endogenous RNAs (ceRNA) network. This article aims to determine the prognostic biomarker of ceRNA for non-small-cell lung cancer (NSCLC) prognosis.

Methods

The expression and clinical significance of LINC00973 in NSCLC tissues were analyzed via the The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), lnCAR, and clinical samples in Taihe Hospital. The biological functions and signaling pathways involved in target genes of ceRNA network were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Survival analysis, univariate and multivariate Cox regression analysis were used for prognostic-related mRNA.

Results

Expression of LINC00973 was increased in NSCLC tissues. High expression of LINC00973 was associated with poor prognosis of NSCLC patients. There were 15 miRNA and 238 differential mRNA in the INC00973-miRNA-mRNA ceRNA network, involving cell migration, endothelial cell proliferation, tumor growth factor (TGF)-β, cellular senescence, phosphatidylinositol 3-hydroxy kinase (PI3K)-Akt, Hippo, Rap1, mitogen-activated protein kinase (MAPK), cell cycle signaling pathway, etc. The expression levels of RTKN2, NFIX, PTX3, BMP2 and LOXL2 were independent risk factors for the poor prognosis of NSCLC patients.

Conclusions

LINC00973-miRNA-mRNA ceRNA network might be the basis for determining pivotal post-translational regulatory mechanisms in the progression of NSCLC. BMP2, LOXL2, NFIX, PTX3 and RTKN2 might be valuable prognostic markers and potential therapeutic targets.

MicroRNA-1: Diverse role of a small player in multiple cancers

The process of cancer initiation and development is a dynamic and complex mechanism involving multiple genetic and non-genetic variations. With the development of high throughput techniques like next-generation sequencing, the field of cancer biology extended beyond the protein-coding genes. It brought the functional role of noncoding RNAs into cancer-associated pathways. MicroRNAs (miRNAs) are one such class of noncoding RNAs regulating different cancer development aspects, including progression and metastasis. MicroRNA-1 (miR-1) is a highly conserved miRNA with a functional role in developing skeletal muscle precursor cells and cardiomyocytes and acts as a consistent tumor suppressor gene. In humans, two discrete genes, MIR-1-1 located on 20q13.333 and MIR-1-2 located on 18q11.2 loci encode for a single mature miR-1. Downregulation of miR-1 has been demonstrated in multiple cancers, including lung, breast, liver, prostate, colorectal, pancreatic, medulloblastoma, and gastric cancer. A vast number of studies have shown that miR-1 affects the hallmarks of cancer like proliferation, invasion and metastasis, apoptosis, angiogenesis, chemosensitization, and immune modulation. The potential therapeutic applications of miR-1 in multiple cancer pathways provide a novel platform for developing anticancer therapies. This review focuses on the different antitumorigenic and therapeutic aspects of miR-1, including how it regulates tumor development and associated immunomodulatory functions.