Correlated low IGF2BP1 and FOXM1 expression predicts a good prognosis in lung adenocarcinoma

Consensus clustering and novel risk score model construction based on m6A methylation regulators to evaluate the prognosis and tumor immune microenvironment of early-stage lung adenocarcinoma

BACKGROUND

The aim of this study was to investigate the correlation between m6A methylation regulators and cell infiltration characteristics in tumor immune microenvironment (TIME), so as to help understand the immune mechanism of early-stage lung adenocarcinoma (LUAD).

METHODS

The expression and consensus cluster analyses of m6A methylation regulators in early-stage LUAD were performed. The clinicopathological features, immune cell infiltration, survival and functional enrichment in different subtypes were analyzed. We also constructed a prognostic model. Clinical tissue samples were used to validate the expression of model genes through real-time polymerase chain reaction (RT-PCR). In addition, cell scratch assay and Transwell assay were also performed.

RESULTS

Expression of m6A methylation regulators was abnormal in early-stage LUAD. According to the consensus clustering of m6A methylation regulators, patients with early-stage LUAD were divided into two subtypes. Two subtypes showed different infiltration levels of immune cell and survival time. A prognostic model consisting of HNRNPC, IGF2BP1 and IGF2BP3 could be used to predict the survival of early-stage LUAD. RT-PCR results showed that HNRNPC, IGF2BP1 and IGF2BP3 were significantly up-regulated in early-stage LUAD tissues. The results of cell scratch assay and Transwell assay showed that overexpression of HNRNPC promotes the migration and invasion of NCI-H1299 cells, while knockdown HNRNPC inhibits the migration and invasion of NCI-H1299 cells.

CONCLUSIONS

This work reveals that m6A methylation regulators may be potential biomarkers for prognosis in patients with early-stage LUAD. Our prognostic model may be of great value in predicting the prognosis of early-stage LUAD.

TFAP4 Activates IGF2BP1 and Promotes Progression of Non-Small Cell Lung Cancer by Stabilizing TK1 Expression through m6A Modification

Non-small cell lung cancer (NSCLC) is a well-known global health concern. TFAP4 has been reported to function as an oncogene. This study sought to investigate the molecular mechanism of TFAP4 in NSCLC development. Significantly highly-expressed gene IGF2BP1 was screened on online databases and its downstream gene TK1 was predicted. IGF2BP1 promoter sequence was identified. The binding site of TFAP4 and IGF2BP1 was predicted. The expression correlations among TFAP4, IGF2BP1, and TK1 were confirmed. The correlations between TFAP4, IGF2BP1, TK1, and NSCLC prognosis were predicted. NSCLC and paracancerous tissues were collected. The expressions of TFAP4, IGF2BP1, and TK1 were detected. NSCLC cell proliferation, migration, invasion, and apoptosis were detected. The binding of TFAP4 to the IGF2BP1 promoter was verified. m6A modification of TK1 mRNA was detected. The correlation between IGF2BP1 and TK1 was confirmed. A subcutaneous tumor xenograft model was established to validate the effect of TFAP4 in vivo. IGF2BP1 was highly expressed in NSCLC tissues and cells. IGF2BP1 knockdown repressed NSCLC cell proliferation, migration, and invasion and facilitated apoptosis. Mechanically, TFAP4 transcriptionally activated IGF2BP1. IGF2BP1 stabilized TK1 expression via m6A modification and promoted NSCLC cell proliferation, migration, and invasion. In vivo experiments confirmed that TFAP4 knockdown suppressed tumor growth by downregulating IGF2BP1/TK1.

IMPLICATIONS

Our findings revealed that TFAP4 activated IGF2BP1 and facilitated NSCLC progression by stabilizing TK1 expression via m6A modification, which offered new insights into the diagnosis and treatment of NSCLC.

RNA m6 A methylation in cancer

N⁶ -methyladenosine (m⁶ A) is one of the most abundant internal modifications in eukaryotic messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). It is a reversible and dynamic RNA modification that has been observed in both internal coding segments and untranslated regions. Studies indicate that m⁶ A modifications play important roles in translation, RNA splicing, export, degradation and ncRNA processing control. In this review, we focus on the profiles and biological functions of RNA m⁶ A methylation on both mRNAs and ncRNAs. The dynamic modification of m⁶ A and its potential roles in cancer development are discussed. Moreover, we discuss the possibility of m⁶ A modifications serving as potential biomarkers for cancer diagnosis and targets for therapy.

Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer

Background: Studies have reported that RNA-binding proteins (RBPs) are dysregulated in multiple cancers and are correlated with the progression and prognosis of disease. However, the functions of RBPs in non-small cell lung cancer (NSCLC) remain unclear. The present study aimed to explore the function of RBPs in NSCLC and their prognostic and therapeutic value.

Methods: The mRNA expression profiles, DNA methylation data, gene mutation data, copy number variation data, and corresponding clinical information on NSCLC were downloaded from The Cancer Genome Atlas, Gene Expression Omnibus, and the University of California Santa Cruz Xena databases. The differentially expressed RBPs were identified between tumor and control tissues, and the expression and prognostic value of these RBPs were systemically investigated by bioinformatics analysis. A quantitative polymerase chain reaction (qPCR) was performed to validate the dysregulated genes in the prognostic signature.

Results: A prognostic RBP-related signature was successfully constructed based on eight RBPs represented as a risk score using least absolute shrinkage and selection operator (LASSO) regression analysis. The high-risk group had a worse overall survival (OS) probability than the low-risk group (p < 0.001) with 1-, 3-, and 5-year area under the receiver operator characteristic curve values of 0.671, 0.638, and 0.637, respectively. The risk score was associated with the stage of disease (p < 0.05) and was an independent prognostic factor for NSCLC when adjusted for age and UICC stage (p < 0.001, hazard ratio (HR): 1.888). The constructed nomogram showed a good predictive value. The P53, focal adhesion, and NOD-like receptor signaling pathways were the primary pathways in the high-risk group (adjusted p value <0.05). The high-risk group was correlated with increased immune infiltration (p < 0.05), upregulated relative expression levels of programmed cell death 1 (PD1) (p = 0.015), cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (p = 0.042), higher gene mutation frequency, higher tumor mutational burden (p = 0.034), and better chemotherapy response (p < 0.001). The signature was successfully validated using the GSE26939, GSE31210, GSE30219, and GSE157009 datasets. Dysregulation of these genes in patients with NSCLC was confirmed using the qPCR in an independent cohort (p < 0.05).

Conclusion: An RBP-related signature was successfully constructed to predict prognosis in NSCLC, functioning as a reference for individualized therapy, including immunotherapy and chemotherapy.

RNA-Binding Protein IGF2BP1 Associated With Prognosis and Immunotherapy Response in Lung Adenocarcinoma

N6-methyladenosine (m⁶A) is the most common modification in eukaryotic RNAs and plays a vital role in the tumorigenesis and metastasis of various cancers. However, a comprehensive study of m⁶A methylation regulators in lung adenocarcinoma (LUAD) is still lacking. The present study aimed to systematically explore the role of m⁶A methylation regulators in LUAD. RNA sequencing data of 20 m⁶A methylation regulators and clinical data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. The prognosis value of m⁶A methylation regulators in LUAD was evaluated using the Gene Expression Profiling Interactive Analysis (GEPIA) and PrognoScan database. The correlation between IGF2BP1 and immune infiltrates in LUAD was investigated via CIBERSORT and Tumor Immune Estimation Resource (TIMER). A total of 15 m⁶A modification regulators were significantly abnormally expressed in LUAD tissues. Survival analysis revealed that four genes (HNRNPC, HNRNPA2B1, IGF2BP1, and IGF2BP3) were significantly associated with poor prognosis in LUAD. Multivariate Cox regression analysis showed that only IGF2BP1 was an independent predictor of LUAD after adjusting common clinical parameters. The mutation rates of m⁶A modification regulators in LUAD were less than 10%. Further analysis revealed that IGF2BP1 expression was significantly correlated with immune infiltration, the expression of immune checkpoints, and tumor mutational burden (TMB) in LUAD. Our findings suggest that IGF2BP1 is an independent predictor and related to immunotherapy response in LUAD, which maybe a potential novel biomarker for LUAD prognosis and the status of tumor immunity.

Involvement of m6A regulatory factor IGF2BP1 in malignant transformation of human bronchial epithelial Beas-2B cells induced by tobacco carcinogen NNK

Nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a Group 1 human carcinogen, as classified by the International Agency for Research of Cancer (IARC), and plays a significant role in lung carcinogenesis. However, its carcinogenic mechanism has not yet been fully elucidated. In this study, we performed colony formation assays, soft-agar assays, and tumor growth in nude mice to show that 100 mg/L NNK facilitates the malignant transformation of human bronchial epithelial Beas-2B cells. Transcriptome sequencing showed that insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1), a post-transcriptional regulator, was differentially expressed in NNK-induced malignant transformed Beas-2B cells (2B-NNK cells). Small interfering RNA (SiRNA) was used to downregulate the expression of the IGF2BP1 gene. The reduction in protein expression, cell proliferation rate, and colony-forming ability and the increase in the apoptosis rate of Beas-2B cells transfected with the SiRNA indicated a role for IGF2BP1 in NNK-induced malignant transformation. IGF2BP1 is an N6-methyladenosine (m6A) regulatory factor, but it is not known whether its association with m6A mediates the malignant transformation of cells. Therefore, we measured the overall levels of m6A in Beas-2B cells. We found that the overall m6A level was lower in 2B-NNK cells, and knocking down IGF2BP1, the overall level of m6A was restored. Hence, we concluded that IGF2BP1 is involved in the NNK-induced malignant transformation of Beas-2B cells, possibly via m6A modification. This study therefore contributes novel insights into the environmental pathogenesis of lung cancer and the gene regulatory mechanisms of chemical carcinogenesis.

HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells

BACKGROUND

Little is known about the relationship between N6-methyladenosine (m6A)-related genes and tumor immune microenvironment (TIME) in non-small cell lung cancer (NSCLC). It is unclear which m6A regulators are essential for NSCLC progression. The aim of this work was to excavate the role of m6A-related genes in the TIME and progression of NSCLC.

METHODS

Based on bioinformatics analysis, heterogeneous nuclear ribonucleoprotein C (HNRNPC) was considered as the most influential m6A regulator. Further study was investigated using patient samples, stable cell lines, and xenograft mice models.

RESULTS

The differentially expressed profiles of m6A-related genes were established in NSCLC, and the NSCLC samples were clustered into two subtypes with different immune infiltration and survival time. Next, we found that the risk score (RS) based on m6A-related genes was a predictor of prognosis and immunotherapy response for NSCLC, in which HNRNPC was considered as the most influential m6A regulator. In NSCLC patients, we confirmed that HNRNPC predicted poor prognosis and correlated with tumor invasion and lymph node metastasis. RNA-seq data revealed that HNRNPC was involved in cell growth, cell migration, extracellular matrix organization and angiogenesis. In vitro, we verified that HNRNPC knockdown attenuated the cell proliferation, clonogenicity, invasion and migration. In vivo, HNRNPC knockdown inhibited the tumor growth and lung metastasis. Additionally, HNRNPC knockdown was associated with high CD8 + T cell infiltration, along with elevated CD4 + T cell infiltration, collagen production and angiogenesis.

CONCLUSIONS

M6A regulator HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells.

Genomics and Prognosis Analysis of N6-Methyladenosine Regulators in Lung Adenocarcinoma

Objective: N⁶-methyladenosine (m⁶A) modification is involved in modulating various biological processes in human cancers. But the implication of m⁶A modification in lung adenocarcinoma (LUAD) is still unclear. Hence, this study conducted a comprehensive analysis of the expression and clinical implication of m⁶A regulators in LUAD.

Methods: Consensus clustering analysis of 502 LUAD samples in the TCGA dataset was presented based on the expression profiles of 20 m⁶A regulators using ConsensusClusterPlus package. Overall survival (OS), activation of signaling pathways and tumor immunity (immune/stromal score, tumor purity, expression of HLA and immune checkpoints, and immune cell infiltration) were compared between m⁶A modification patterns. The m⁶A-related genes between patterns were identified and prognostic m⁶A-related genes were imported into LASSO-cox regression analysis. The m⁶A risk score was developed and its prognostic implication was evaluated and externally verified in the GSE30219 and GSE72094 dataset. Furthermore, a nomogram that contained independent prognostic indicators was established, followed by external verification.

Results: Two m⁶A modification patterns were clustered across LUAD based on the expression similarity of the m⁶A regulators via consensus clustering analysis, with distinct OS, activation of signaling pathways and tumor immunity. Totally, 213 m⁶A-related genes that were identified by comparing two patterns were significantly related to LUAD prognosis. By LASSO method, we constructed the m⁶A risk score that was a reliable and independent prognostic factor for LUAD. Patients with low m⁶A risk score displayed a prominent survival advantage. After incorporating independent clinical features, we developed the prognostic nomogram that exhibited high predictive accuracy and the best clinical net benefit for OS.

Conclusion: Collectively, our study may provide a clinically useful tool for precise prognostic management and optimization of immunotherapeutic strategies for LUAD patients.

Development of a novel embryonic germline gene-related prognostic model of lung adenocarcinoma

Background

Emerging evidence implicates the correlation of embryonic germline genes with the tumor progress and patient's outcome. However, the prognostic value of these genes in lung adenocarcinoma (LUAD) has not been fully studied. Here we systematically evaluated this issue, and constructed a novel signature and a nomogram associated with embryonic germline genes for predicting the outcomes of lung adenocarcinoma.

Methods

The LUAD cohorts retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used as training set and testing set, respectively. The embryonic germline genes were downloaded from the website https://venn.lodder.dev. Then, the differentially expressed embryonic germline genes (DEGGs) between the tumor and normal samples were identified by limma package. The functional enrichment and pathway analyses were also performed by clusterProfiler package. The prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO)-Cox regression method. Survival and Receiver Operating Characteristic (ROC) analyses were performed to validate the model using training set and four testing GEO datasets. Finally, a prognostic nomogram based on the signature genes was constructed using multivariate regression method.

Results

Among the identified 269 DEGGs, 249 were up-regulated and 20 were down-regulated. GO and KEGG analyses revealed that these DEGGs were mainly enriched in the process of cell proliferation and DNA damage repair. Then, 103 DEGGs with prognostic value were identified by univariate Cox regression and further filtered by LASSO method. The resulting sixteen DEGGs were included in step multivariate Cox regression and an eleven embryonic germline gene related signature (EGRS) was constructed. The model could robustly stratify the LUAD patients into high-risk and low-risk groups in both training and testing sets, and low-risk patients had much better outcomes. The multi-ROC analysis also showed that the EGRS model had the best predictive efficacy compared with other common clinicopathological factors. The EGRS model also showed robust predictive ability in four independent external datasets, and the area under curve (AUC) was 0.726 (GSE30219), 0.764 (GSE50081), 0.657 (GSE37745) and 0.668 (GSE72094). More importantly, the expression level of some genes in EGRS has a significant correlation with the progression of LUAD clinicopathology, suggesting these genes might play an important role in the progression of LUAD. Finally, based on EGRS genes, we built and calibrated a nomogram for conveniently evaluating patients' outcomes.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

BACKGROUND

This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD).

METHODS

We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients.

RESULTS

CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments.

CONCLUSION

A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

GTSE1 Facilitates the Malignant Phenotype of Lung Cancer Cells via Activating AKT/mTOR Signaling

The expression of G2 and S phase-expressed-1 (GTSE1) was upregulated in human cancer. However, its expression and roles in lung cancer have not been identified yet. In our study, we reported that GTSE1 expression was statistically higher in lung tissues than in the adjacent noncancerous tissues which might be a consequence of hypomethylation of the GTSE1 promoter. The upregulated expression of GTSE1 mRNA predicted the poorer survival of the lung patients. Ectopic expression of GTSE1 in lung cancer cells significantly increased while knockdown of GTSE1 decreased cell proliferation, cell migration, and cell invasion in H460 and A549 cells. Furthermore, knockdown of GTSE1 regulated the cell cycle and promoted cell apoptosis in H460 and A549 cells. Finally, we presented that GTSE1 was able to activate AKT/mTOR signaling in H460 and A549 cells. In conclusion, these results indicated that the overexpressed GTSE1 was involved in the progress of lung cancer by promoting proliferation migration and invasion and inhibiting apoptosis of lung cancer cells via activating AKT/mTOR signaling.

Identification of pathology-specific regulators of m6A RNA modification to optimize lung cancer management in the context of predictive, preventive, and personalized medicine

Relevance

Lung cancer is the most common malignant tumor with high morbidity (11.6% of the total diagnosed cancer cases) and mortality (18.4% of the total cancer deaths), and its 5-year survival rate is very low (20%). Clarification of any molecular events and the discovery of effective biomarkers will offer increasing promise for lung canner management. N⁶-methyladenosine (m⁶A) modification is one of the important RNA modifications that are closely associated with lung cancer, and are tightly regulated by m⁶A regulators. Elucidation of pathology-specific m⁶A regulators will directly contribute to lung cancer medical services in the context of predictive, preventive, and personalized medicine (PPPM).

Purpose

To investigate pathology-specific regulators of m⁶A RNA modifications in lung cancer and further inspect the m⁶A regulator gene signature as useful tools for PPPM in lung cancers.

Methods

The gene expression data of 19 m⁶A regulators (m⁶A-methyltransferases-ZC3H13, KIAA1429, RBM15/15B, WTAP, and METTL3/14; demethylases-FTO and ALKBH5; and m⁶A-binding proteins-HNRNPC, YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1) and clinical data of 1013 lung cancer patients [511 lung adenocarcinoma (LUAD) and 502 lung squamous carcinoma (LUSC)] and 109 controls (Con) were obtained from the TCGA database. Quantitative real-time PCR (qRT-PCR) was used to verify m⁶A regulators in lung cancer cell lines. Protein-protein interaction (PPI), gene co-expression, survival analysis, and heatmap were used to analyze these m⁶A regulators in this set of lung cancer clinical data. Lasso regression was used to optimize the pathology-specific m⁶A regulator gene signature. Gene set enrichment analysis (GSEA) was used to reveal the functional characteristics of m⁶A regulators.

Results

Those 19 m⁶A regulator profiling was significantly differentially expressed in lung cancer tissues relative to control tissues, which was also verified in lung cancer cell lines. Those m⁶A regulators interacted mutually, and those regulator-based sample clusters were correlated with clinical traits, including survival status, gender, tobacco smoking history, primary disease, and pathologic stage. Further, lasso regression based on the 19 m⁶A regulators optimized and identified a three-m⁶A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as independent prognostic factor, which classified 1013 lung cancer patients into high-risk and low-risk groups according to median value (0.84) of the lasso regression risk scores. This three-m⁶A-regulator signature profiling was significantly related to lung cancer overall survival, cancer status, and the above-described clinical traits. Further, GSEA revealed that KIAA1429, METTL3, and IGF2BP1 were significantly related to multiple biological behaviors, including proliferation, apoptosis, metastasis, energy metabolism, drug resistance, and recurrence, and that KIAA1429 and IGF2BP1 had potential target genes, including E2F3, WTAP, CCND1, CDK4, EGR2, YBX1, and TLX, which were associated with cancers.

Conclusion

This study provided the first view of the pathology-specific regulators of m⁶A RNA modification in lung cancers and identified the three-m⁶A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as an independent prognostic model to classify lung cancers into high- and low-risk groups for patient stratification, prognostic assessment, and personalized treatment toward PPPM in lung cancers.