A Robust Seven-Gene Signature Associated With Tumor Microenvironment to Predict Survival Outcomes of Patients With Stage III-IV Lung Adenocarcinoma

MiR-1976/NCAPH/P65 axis inhibits the malignant phenotypes of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignancy with an abysmal survival rate. High metastasis is the leading cause of the low survival rate of LUAD. NCAPH, an oncogene, is involved in the carcinogenesis of LUAD. However, the regulation of NCAPH in LUAD remains controversial. In this work, we identified an up-regulation of NCAPH in LUAD tissues. Patients who expressed more NCAPH had shorter overall survival (OS). Furthermore, NCAPH overexpression promoted LUAD cell migration while inhibiting apoptosis. MiR-1976 and miR-133b were predicted to target NCAPH expression by searching TargetScan and linkedomics databases. Following that, we confirmed that miR-1976 suppressed NCAPH by directly targeting a 7-bp region of NCAPH 3' untranslated regions (UTR). In addition, increased expression of miR-1976 decreased the proliferation & migration and promoted apoptosis of LUAD cells, and the re-introduction of NCAPH reversed these influences. Furthermore, the xenograft and metastasis mouse models also confirmed that miR-1976 inhibited tumor growth and metastasis in vivo by targeting NCAPH. Finally, we found that MiR-1976 targeting NCAPH blocked the activation of NF-κB. In conclusion, miR-1976 inhibits NCAPH activity in LUAD and acts as a tumor suppressor. The miR-1976/NCAPH/NF-κB axis may, in the future, represent crucial diagnostic and prognostic biomarkers and promising therapeutic options.

The Dual Role of Chemerin in Lung Diseases

Chemerin is an atypical chemokine first described as a chemoattractant agent for monocytes, natural killer cells, plasmacytoid and myeloid dendritic cells, through interaction with its main receptor, the G protein-coupled receptor chemokine-like receptor 1 (CMKLR1). Chemerin has been studied in various lung disease models, showing both pro- and anti-inflammatory properties. Given the incidence and burden of inflammatory lung diseases from diverse origins (infectious, autoimmune, age-related, etc.), chemerin has emerged as an interesting therapeutical target due to its immunomodulatory role. However, as highlighted by this review, further research efforts to elucidate the mechanisms governing chemerin's dual pro- and anti-inflammatory characteristics are urgently needed. Moreover, although a growing body of evidence suggests chemerin as a potential biomarker for the diagnosis and/or prognosis of inflammatory lung diseases, this review underscores the necessity for standardizing both sampling types and measurement techniques before drawing definitive conclusions.

NSUN6 Regulates NM23-H1 Expression in an m5C Manner to Affect Epithelial-Mesenchymal Transition in Lung Cancer

PURPOSE

The expression and regulatory mechanism of NSUN6 in lung cancer are still unclear. Our study explored whether NSUN6 mediates progression of lung cancer by affecting NM23-H1 expression in an m5C-dependent manner.

METHODS

qRT-PCR, CCK-8, colony formation, transwell, and Western blot analysis were employed to probe the impact of NSUN6 on lung cancer cell proliferation, migration, and epithelial-mesenchymal transition (EMT). RMVar database was utilized to forecast the downstream genes of NSUN6. The mode of interaction between NSUN6 and NM23-H1 was determined by dot blot, luciferase assay, m5C RIP, and cell function assays. The effect of NSUN6 expression on tumor growth was verified in vivo.

RESULTS

Expression of NSUN6 was reduced in lung cancer cells, and over-expression of NSUN6 restricted the proliferation of lung cancer cells, migration, and EMT. NSUN6 regulated NM23-H1 expression by modifying the 3'-UTR of NM23-H1 mRNA through m5C and inhibited lung cancer cell proliferation, migration, and EMT. In vivo experiments also showed that over-expression of NSUN6 inhibited the occurrence of lung cancer.

CONCLUSION

NSUN6 regulates NM23-H1 expression in an m5C-dependent manner to affect EMT in lung cancer. Thus, NSUN6 may be considered as a potential therapeutic target for lung cancer.

A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma

Background

Pyroptosis, a lytic form of programmed cell death initiated by inflammasomes, has been reported to be closely associated with tumor proliferation, invasion and metastasis. However, the roles of pyroptosis genes (PGs) in low-grade glioma (LGG) remain unclear.

Methods

We obtained information for 1,681 samples, including the mRNA expression profiles of LGGs and normal brain tissues and the relevant corresponding clinical information from two public datasets, TCGA and GTEx, and identified 45 differentially expressed pyroptosis genes (DEPGs). Among these DEPGs, nine hub pyroptosis genes (HPGs) were identified and used to construct a genetic risk scoring model. A total of 476 patients, selected as the training group, were divided into low-risk and high-risk groups according to the risk score. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves verified the accuracy of the model, and a nomogram combining the risk score and clinicopathological characteristics was used to predict the overall survival (OS) of LGG patients. In addition, a cohort from the Gene Expression Omnibus (GEO) database was selected as a validation group to verify the stability of the model. qRT-PCR was used to analyze the gene expression levels of nine HPGs in paracancerous and tumor tissues from 10 LGG patients.

Results

Survival analysis showed that, compared with patients in the low-risk group, patients in the high-risk group had a poorer prognosis. A risk score model combining PG expression levels with clinical features was considered an independent risk factor. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched among the DEPGs and that immune activity was increased in the high-risk group.

Conclusion

In summary, we successfully constructed a model to predict the prognosis of LGG patients, which will help to promote individualized treatment and provide potential new targets for immunotherapy.

Clinical features and molecular genetics associated with brain metastasis in suspected early-stage non-small cell lung cancer

Introduction

Regarding whether brain magnetic resonance imaging (MRI) should be routine in patients with suspected early-stage lung cancer, guideline recommendations are inconsistent. Therefore, we performed this study to evaluate the incidence of and risk factors for brain metastasis (BM) in patients with suspected early-stage non-small-cell lung cancer (NSCLC).

Methods

A review of the medical charts of consecutive NSCLC patients diagnosed between January 2006 and May 2020 was performed. We identified 1,382 NSCLC patients with clinical staging of T1/2aN0M0 (excluding BM), and investigated the incidence, clinical predictors, and prognosis of BM in the cohort. We also performed RNA-sequencing differential expression analysis using transcriptome of 8 patients, using DESeq2 package (version 1.32.0) with R (version 4.1.0).

Results

Among 1,382 patients, nine hundred forty-nine patients (68.7%) underwent brain MRI during staging, and 34 patients (3.6%) were shown to have BM. Firth's bias-reduced logistic regression showed that tumor size (OR 1.056; 95% CI 1.009-1.106, p=0.018) was the only predictor of BM, and pathologic type was not a predictor of BM in our cohort (p>0.05). The median overall survival for patients with brain metastasis was 5.5 years, which is better than previously reported in the literature. RNA-sequencing differential expression analysis revealed the top 10 significantly upregulated genes and top 10 significantly downregulated genes. Among the genes involved in BM, Unc-79 homolog, non-selective sodium leak channel (NALCN) channel complex subunit (UNC79) was the most highly expressed gene in the lung adenocarcinoma tissues from the BM group, and an in vitro assay using A549 cells revealed that the NALCN inhibitor suppressed lung cancer cell proliferation and migration.

Conclusions

Given the incidence and favorable outcome of BM in patients with suspected early-stage NSCLC, selective screening with brain MRI may be considered, especially in patients with high-risk features.

Identifying the EMT-related signature to stratify prognosis and evaluate the tumor microenvironment in lung adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is a critical process in tumor invasion and metastasis. EMT has been shown to significantly influence the invasion, metastasis, and poor prognosis in lung adenocarcinoma (LUAD). This study aimed to develop a novel EMT-related prognostic model capable of predicting overall survival (OS) in patients with LUAD.

Methods: A total of 283 LUAD patients from TCGA RNA-seq dataset were assigned to a training cohort for model building, and 310 LUAD patients from GEO RNA-seq dataset were assigned to a validation cohort. EMT genes were acquired from MsigDB database and then prognosis-related EMT genes were identified by univariate Cox regression. Lasso regression was then performed to determine the genes and the corresponding variables to construct a prognosis risk model from the training cohort. Furthermore, characteristics of the tumor microenvironment (TME), mutation status and chemotherapy responses were analyzed to assess the differences between the two risk groups based on the prognostic model. In addition, RT-qPCR was employed to validate the expression patterns of the 6 genes derived from the risk model.

Results: A six-gene EMT signature (PMEPA1, LOXL2, PLOD2, MMP14, SPOCK1 and DCN) was successfully constructed and validated. The signature assigned the LUAD patients into high-risk and low-risk groups. In comparison with the low-risk group, patients in the high-risk group had a significantly lower survival rate. ROC curves and calibration curves for the risk model demonstrated reliable stratification and predictive ability. The risk model was robustly correlated with multiple TME characteristics. Besides, the data showed that patients in the low-risk group had more immune activities, higher stemness scores and cytolytic activity scores and higher TMB. In addition, RT-qPCR results revealed that PMEPA1, LOXL2, PLOD2, MMP14, and SPOCK1 were notably upregulated in LUAD tissues, while DCN was downregulated.

Conclusion: Our study successfully developed a novel EMT-related signature to predict prognosis of LUAD patients and guide treatment strategies. The six genes derived from the prediction signature might play a potential role in antitumor immunity and serve as promising therapeutic targets in LUAD.

A robust CD8+ T cell-related classifier for predicting the prognosis and efficacy of immunotherapy in stage III lung adenocarcinoma

Patients with stage III lung adenocarcinoma (LUAD) have significant survival heterogeneity, meanwhile, CD8⁺ T cell has a remarkable function in immunotherapy. Therefore, developing novel biomarkers based on CD8⁺ T cell can help evaluate the prognosis and guide the strategy of immunotherapy for patients with stage III LUAD. Thus, we abstracted twelve datasets from multiple online databases and grouped the stage III LUAD patients into training and validation sets. We then used WGCNA and CIBERSORT, while univariate Cox analysis, LASSO analysis, and multivariate Cox analysis were performed. Subsequently, a novel CD8⁺ T cell-related classifier including HDFRP3, ARIH1, SMAD2, and UPB1 was developed, which could divide stage III LUAD patients into high- and low-risk groups with distinct survival probability in multiple cohorts (all P < 0.05). Moreover, a robust nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its powerful predictive capacity. Besides, we detected the difference in immune cell subpopulations and evaluated the potential benefits of immunotherapy between the two risk subsets. Finally, we verified the correlation between the gene expression and CD8⁺ T cells included in the model by immunohistochemistry and validated the validity of the model in a real-world cohort. Overall, we constructed a robust CD8⁺ T cell-related risk model originally which could predict the survival rates in stage III LUAD. What’s more, this model suggested that patients in the high-risk group could benefit from immunotherapy, which has significant implications for accurately predicting the effect of immunotherapy and evaluating the prognosis for patients with stage III LUAD.