Distinct expression and prognostic value of members of SMAD family in non-small cell lung cancer

The role of inflammatory and remodelling biomarkers in patients with non-small cell lung cancer

Introduction

Biomarkers play a crucial role in evaluating the prognosis, diagnosis, and monitoring of non-small cell lung cancer (NSCLC). The aim of this study was to compare the levels of inflammatory and remodelling biomarkers among patients with NSCLC and healthy controls (HCs) and to investigate the correlation between these biomarkers.

Material and methods

Blood samples were taken from 93 NSCLC and 84 HCs. Each sample was analysed for the inflammatory biomarkers transforming growth factor β1 (TGF-β1), mothers against decapentaplegic homolog 2 (SMAD2) and the remodelling biomarkers Wingless-related integration site (Wnt3a) and α-catenin (CTNN-β1).

Results

The patients with NSCLC had significantly higher levels of all the measured biomarkers. In the NSCLC patients, TGF-β1 correlated significantly with SMAD2 (r = 0.34, p = 0.0008), Wnt3a (r = 0.328, p = 0.0013), and CTNN-β1 levels (r = 0.30, p = 0.004). SMAD2 correlated significantly with CTNN-β1 (r = 0.546, p = 0.0001) and Wnt3a (r = 0.598, p = 0.0001). CTNN-β1 level also correlated with the level of Wnt3a (r = 0.61, p = 0.0001). No correlation was found between biomarkers and symptom scores.

Discussion

In this study, patients with NSCLC had higher inflammatory and remodelling biomarker levels than HCs. In the NSCLC, there were significant associations between inflammatory and remodelling biomarkers. This indicates that measuring biomarkers could be valuable in the workup of NSCLC patients.

Conclusions

Our investigation showed that inflammatory and remodelling biomarkers might play a role in future immunologic response and pharmacologically targeted NSCLC therapy.

Regulation and therapeutic potentials of microRNAs to non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for 80%-85% of total cases and leading to millions of deaths worldwide. Drug resistance is the primary cause of treatment failure in NSCLC, which urges scientists to develop advanced approaches for NSCLC treatment. Among novel approaches, the miRNA-based method has emerged as a potential approach as it allows researchers to modulate target gene expression. Subsequently, cell behaviors are altered, which leads to the death and the depletion of cancer cells. It has been reported that miRNAs possess the capacity to regulate multiple genes that are involved in various signaling pathways, including the phosphoinositide 3-kinase, receptor tyrosine kinase/rat sarcoma virus/mitogen-activated protein kinase, wingless/integrated, retinoblastoma, p53, transforming growth factor β, and nuclear factor-kappa B pathways. Dysregulation of these signaling pathways in NSCLC results in abnormal cell proliferation, tissue invasion, and drug resistance while inhibiting apoptosis. Thus, understanding the roles of miRNAs in regulating these signaling pathways may enable the development of novel NSCLC treatment therapies. However, a comprehensive review of potential miRNAs in NSCLC treatment has been lacking. Therefore, this review aims to fill the gap by summarizing the up-to-date information on miRNAs regarding their targets, impact on cancer-associated pathways, and prospective outcomes in treating NSCLC. We also discuss current technologies for delivering miRNAs to the target cells, including virus-based, non-viral, and emerging extracellular vesicle-based delivery systems. This knowledge will support future studies to develop an innovative miRNA-based therapy and select a suitable carrier to treat NSCLC effectively.

Integrative analysis of GWAS and transcriptomics data reveal key genes for non-small lung cancer

Lung cancer is one of the world's most common and deadly cancers. The two main types of lung cancer are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). More than 85% of lung cancers are NSCLC. Genetic factors play a significant role in the risk of NSCLC. Growing studies focus on studying risk factors at the molecular level. The aim of the study is to build a pipeline to integrate Genome-wide association analysis (GWAS) and transcriptomics data with machine learning to effectively identify genetic risk factors of NSCLC. GWAS datasets and GWAS summary data were downloaded from GWAS catalog, which include lung carcinoma genetic variants among the European population. Then, with the GWAS summary, data functional analysis of significant SNPs was performed using a webserver called FUMAGWAS. The transcriptomics data of NSCLC and non-NSCLC people were used to build a machine learning model to identify the key genes that help predict the NSCLC. The top up-regulation and down-regulation genes were identified by the BART cancer webserver, and the mechanistic roles of the genes were validated by literature review. By performing integrative analysis of GWAS and transcriptomics analysis using machine learning, we identified multiple SNPs and genes that related to NSCLC. The computational pipeline may facilitate the biomarker discovery for NSCLC and other diseases.

Study of MDM2 as Prognostic Biomarker in Brain-LGG Cancer and Bioactive Phytochemicals Inhibit the p53-MDM2 Pathway: A Computational Drug Development Approach

An evaluation of the expression and predictive significance of the MDM2 gene in brain lower-grade glioma (LGG) cancer was carried out using onco-informatics pipelines. Several transcriptome servers were used to measure the differential expression of the targeted MDM2 gene and search mutations and copy number variations. GENT2, Gene Expression Profiling Interactive Analysis, Onco-Lnc, and PrognoScan were used to figure out the survival rate of LGG cancer patients. The protein–protein interaction networks between MDM2 gene and its co-expressed genes were constructed by Gene-MANIA tool. Identified bioactive phytochemicals were evaluated through molecular docking using Schrödinger Suite Software, with the MDM2 (PDB ID: 1RV1) target. Protein–ligand interactions were observed with key residues of the macromolecular target. A molecular dynamics simulation of the novel bioactive compounds with the targeted protein was performed. Phytochemicals targeting MDM2 protein, such as Taxifolin and (-)-Epicatechin, have been shown with more highly stable results as compared to the control drug, and hence, concluded that phytochemicals with bioactive potential might be alternative therapeutic options for the management of LGG patients. Our once informatics-based designed pipeline has indicated that the MDM2 gene may have been a predictive biomarker for LGG cancer and selected phytochemicals possessed outstanding interaction results within the macromolecular target’s active site after utilizing in silico approaches. In vitro and in vivo experiments are recommended to confirm these outcomes.

miR-370 impacts the biological behavior of lung cancer cells by targeting the SMAD1 signaling pathway

BACKGROUND

MicroRNAs (miRNAs) have been identified to play a role in the development and progression of lung cancer (LC). As of now, the expression and function of miR-370 in LC are still under investigation. Accordingly, this study explores the role and mechanism of miR-370 in LC.

METHODS

MiR-370 mimics or inhibitors were used to transfect A549 and NCI-H460 cells to overexpress or inhibit miR-370. The colony formation test and Cell Counting Kit-8 were conducted to detect the cell proliferation activity, and transwell test and wound healing test were conducted to evaluate the cell invasion and migration activities. In addition, the downstream target genes of miR-370 in LC were verified by dual luciferase reporter assay and western blot.

RESULTS

Compared to normal tissues and cell lineslines, the miR-370 expression in LC tissues and cells was decreased greatly. Compared to the negative control group, the up-regulation of miR-370 greatly intensified the apoptosis of NCI-H460 cells and weakened the migration, proliferation, and invasion of the cells. However, compared to the inhibitor-negative control group, the downregulation of miR-370 caused the opposite results. Additionally, SMAD family member 1 (SMAD1) was identified as a direct target of miR-370 in LC and could be inhibited by miR-370. Its overexpression restored the impact of miR-370 mimics on LC cells.

CONCLUSION

With low expression in LC tissues and cell lineslines, miR-370 is a tumor suppressor that weakens the growth, invasion as well as migration of LC cells by inhibiting SMAD1 expression. Our results may provide novel insights for the biological treatment of LC.

Establishing a Macrophage Phenotypic Switch-Associated Signature-Based Risk Model for Predicting the Prognoses of Lung Adenocarcinoma

Background

Tumor-associated macrophages are important components of the tumor microenvironment, and the macrophage phenotypic switch has been shown to correlate with tumor development. However, the use of a macrophage phenotypic switch-related gene (MRG)-based prognosis signature for lung adenocarcinoma (LADC) has not yet been investigated.

Methods

In total, 1,114 LADC cases from two different databases were collected. The samples from TCGA were used as the training set (N = 490), whereas two independent datasets (GSE31210 and GSE72094) from the GEO database were used as the validation sets (N = 624). A robust MRG signature that predicted clinical outcomes of LADC patients was identified through multivariate COX and Lasso regression analysis. Gene set enrichment analysis was applied to analyze molecular pathways associated with the MRG signature. Moreover, the fractions of 22 immune cells were estimated using CIBERSORT algorithm.

Results

An eight MRG-based signature comprising CTSL, ECT2, HCFC2, HNRNPK, LRIG1, OSBPL5, P4HA1, and TUBA4A was used to estimate the LADC patients’ overall survival. The MRG model was capable of distinguishing high-risk patients from low-risk patients and accurately predict survival in both the training and validation cohorts. Subsequently, the eight MRG-based signature and other features were used to construct a nomogram to better predict the survival of LADC patients. Calibration plots and decision curve analysis exhibited good consistency between the nomogram predictions and actual observation. ROC curves displayed that the signature had good robustness to predict LADC patients’ prognostic outcome.

Conclusions

We identified a phenotypic switch-related signature for predicting the survival of patients with LADC.

Prognostic Significance of TLR2, SMAD3 and Localization-dependent SATB1 in Stage I and II Non-Small-Cell Lung Cancer Patients

This study aimed to explore the prognostic value of SATB1, SMAD3, and TLR2 expression in non-small-cell lung carcinoma patients with clinical stages I-II. To investigate, we evaluated immunohistochemical staining to each of these markers using tissue sections from 69 patients from our cohort and gene expression data for The Cancer Genome Atlas (TCGA) cohort. We found that, in our cohort, high expression levels of nuclear SATB1ⁿ and SMAD3 were independent prognostic markers for better overall survival (OS) in NSCLC patients. Interestingly, expression of cytoplasmic SATB1^c exhibited a significant but inverse association with survival rate, and it was an independent predictor of unfavorable prognosis. Likewise, TLR2 was a negative outcome biomarker for NSCLC even when adjusting for covariates. Importantly, stratification of NSCLCs with respect to combined expression of the three biomarkers allowed us to identify subgroups of patients with the greatest difference in duration of survival. Specifically, expression profile of SATB1^n-high/SMAD3^high/TLR2^low was associated with the best OS, and it was superior to each single protein alone in predicting patient prognosis. Furthermore, based on the TCGA dataset, we found that overexpression of SATB1 mRNA was significantly associated with better OS, whereas high mRNA levels of SMAD3 and TLR2 with poor OS. In conclusion, the present study identified a set of proteins that may play a significant role in predicting prognosis of NSCLC patients with clinical stages I-II.

Analysis of SYK Gene as a Prognostic Biomarker and Suggested Potential Bioactive Phytochemicals as an Alternative Therapeutic Option for Colorectal Cancer: An In-Silico Pharmaco-Informatics Investigation

BACKGROUND

SYK gene regulates the expression of SYK kinase (Spleen tyrosine kinase), an important non-receptor protein-tyrosine kinase for immunological receptor-mediated signaling, which is also considered a tumor growth metastasis initiator. An onco-informatics analysis was adopted to evaluate the expression and prognostic value of the SYK gene in colorectal cancer (CRC), the third most fatal cancer type; of late, it may be a biomarker as another targeted site for CRC. In addition, identify the potential phytochemicals that may inhibit the overexpression of the SYK kinase protein and minimize the human CRC.

MATERIALS & METHODS

The differential expression of the SYK gene was analyzed using several transcriptomic databases, including Oncomine, UALCAN, GENT2, and GEPIA2. The server cBioPortal was used to analyze the mutations and copy number alterations, whereas GENT2, Gene Expression Profiling Interactive Analysis (GEPIA), Onco-Lnc, and PrognoScan were used to examine the survival rate. The protein-protein interaction network of SYK kinase and its co-expressed genes was conducted via Gene-MANIA. Considering the SYK kinase may be the targeted site, the selected phytochemicals were assessed by molecular docking using PyRx 0.8 packages. Molecular interactions were also observed by following the Ligplot+ version 2.2. YASARA molecular dynamics simulator was applied for the post-validation of the selected phytochemicals.

RESULTS

Our result reveals an increased level of mRNA expression of the SYK gene in colorectal adenocarcinoma (COAD) samples compared to those in normal tissues. A significant methylation level and various genetic alterations recurrence of the SYK gene were analyzed where the fluctuation of the SYK alteration frequency was detected across different CRC studies. As a result, a lower level of SYK expression was related to higher chances of survival. This was evidenced by multiple bioinformatics platforms and web resources, which demonstrated that the SYK gene can be a potential biomarker for CRC. In this study, aromatic phytochemicals, such as kaempferol and glabridin that target the macromolecule (SYK kinase), showed higher stability than the controls, and we have estimated that these bioactive potential phytochemicals might be a useful option for CRC patients after the clinical trial.

CONCLUSIONS

Our onco-informatics investigation suggests that the SYK gene can be a potential prognostic biomarker of CRC. On the contrary, SYK kinase would be a major target, and all selected compounds were validated against the protein using in-silico drug design approaches. Here, more in vitro and in vivo analysis is required for targeting SYK protein in CRC.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

Development and validation of a prognostic nomogram for patients with lung adenocarcinoma based on a novel 6-DNA repair-related gene signature

DNA repair-related genes (DRGs) have attracted much attention in the field of oncology. However, the prognostic role of DRGs and their biological function in lung adenocarcinoma (LUAD) remains rudimentary and inconclusive. In this study, 716 LUAD cases from two different cohorts were collected. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and data from Gene Expression Omnibus (GEO) datasets were used for validation. Using multivariate Cox analysis and LASSO regression, we constructed a DRG signature and used it, together with clinical indices, to develop a nomogram to predict 1-, 3-, and 5-year survival rates. We identified a six-DRG signature to estimate the survival of LUAD patients, which distinguished high-risk from low-risk patients with LUAD in both the training and validation cohorts. We also observed elevated levels of infiltrating CD4 memory activated T cells, resting NK cells, M0 and M1 macrophages, and activated mast cells in the high-risk group. Finally, a nomogram incorporating the signature and clinical parameters was superior to the American Joint Committee on Cancer (AJCC) staging system in predicting the survival of LUAD patients. The DRG prognostic signature and integrated nomogram could be a useful tool to predict prognosis in patients with LUAD.

Systematic construction and validation of an epithelial-mesenchymal transition risk model to predict prognosis of lung adenocarcinoma

Epithelial–mesenchymal transition (EMT) has been shown to be linked to a poor prognosis, particularly in patients with non-small-cell lung cancer. Nevertheless, little is known regarding the existence of EMT-related gene signatures and their prognostic values in lung adenocarcinoma (LUAD). In the current study, we systematically profiled the mRNA expression data of patients with LUAD in The Cancer Genome Atlas and Gene Expression Omnibus databases using a total of 1,184 EMT-related genes. The prognostic values of the EMT-related genes used to develop risk score models for overall survival were determined using LASSO and Cox regression analyses. A prognostic signature that consisted of nine unique EMT-related genes was generated using a training set. A nomogram, incorporating this EMT-related gene signature and clinical features of patients with LUAD, was constructed for potential clinical use. Calibration plots, decision-making curves, and receiver operating characteristic curve analysis showed that this model had a good ability to predict the survival of patients with LUAD. The EMT-associated gene signature and prognostic nomogram established in this study were reliable in predicting the survival of patients with LUAD. Thus, we first identified a novel EMT-related gene signature and developed a nomogram for predicting the prognosis of patients with LUAD.

Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma

Epigenetic factors play crucial roles in carcinogenesis by modifying chromatin architecture. Here, we established an epigenetic biosignature-based model for examining survival in patients with lung adenocarcinoma (LUAD). We retrieved gene-expression profiles and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus and clustered the data into training (n = 490) and Validation (n = 226) datasets, respectively. To establish an epigenetic model, we identified prognostic epigenetic regulation-related genes by LASSO and Cox regression analyses, and established a novel 11-gene signature, including EPC1, GADD45A, HCFC2, RCOR1, SMARCAL1, TLE2, TRIM28, and ZNF516, for predicting LUAD overall survival (OS). The biosignature performed optimally in both the training and validation sets according to receiver operating characteristic and calibration plots. Moreover, the biosignature classified patients into high- and low-risk clusters with distinct survival times, with Cox regression analysis revealing the biosignature as an independent LUAD prognostic index. Furthermore, the generated nomogram integrating the prognostic gene biosignature and clinical indices predicted LUAD OS with high efficiency and outperformed tumor-node-metastasis staging in LUAD survival prediction. These results demonstrated the efficacy of the epigenetic signature prognostic nomogram for reliably predicting LUAD OS and its potential application for informing clinical decision making and individualized treatment.

Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

Background

The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.

Methods

The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.

Results

A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.

Conclusions

Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.