Switched alternative splicing events as attractive features in lung squamous cell carcinoma.

BACKGROUND: Alternative splicing (AS) plays important roles in transcriptome and proteome diversity. Its dysregulation has a close affiliation with oncogenic processes. This study aimed to evaluate AS-based biomarkers by machine learning algorithms for lung squamous cell carcinoma (LUSC) patients. METHOD: The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database were utilized. After data composition balancing, Boruta feature selection and Spearman correlation analysis were used for differentially expressed AS events. Random forests and a nested fivefold cross-validation were applied for lymph node metastasis (LNM) classifier building. Random survival forest combined with Cox regression model was performed for a prognostic model, based on which a nomogram was developed. Functional enrichment analysis and Spearman correlation analysis were also conducted to explore underlying mechanisms. The expression of some switch-involved AS events along with parent genes was verified by qRT-PCR with 20 pairs of normal and LUSC tissues. RESULTS: We found 16 pairs of splicing events from same parent genes which were strongly related to the splicing switch (intrapair correlation coefficient = - 1). Next, we built a reliable LNM classifier based on 13 AS events as well as a nice prognostic model, in which switched AS events behaved prominently. The qRT-PCR presented consistent results with previous bioinformatics analysis, and some AS events like ITIH5-10715-AT and QKI-78404-AT showed remarkable detection efficiency for LUSC. CONCLUSION: AS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC.

Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma.

Exploring early detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression.

hsa_circ_0000519 promotes the progression of lung adenocarcinoma through the hsa-miR-1296-5p/DARS axis.

Emerging research indicates that circRNAs serve a crucial role in occurrence and development of cancers. This study aimed to uncover the biological role of hsa_circ_0000519 in the progression of LUAD (lung adenocarcinoma). hsa_circ_0000519 was identified by bioinformatic analysis, and its differential expression was validated in LUAD tissues and cell lines. CCK8, colony formation, wound healing, transwell assays, and xenograft tumor models were used to observe the biological functions of hsa_circ_0000519. FISH, RIP, dual luciferase reporter assays, and recovery experiments were implemented to explore the underlying mechanisms of hsa_circ_0000519. hsa_circ_0000519 was significantly upregulated in LUAD tissues and cell lines. The expression of hsa_circ_0000519 was positively correlated with T grade and TNM stage in patients with LUAD. Downregulation of hsa_circ_0000519 remarkably reduced cell proliferation, migration, invasion in vitro, and tumor growth in vivo. Mechanistic investigation demonstrated that hsa_circ_0000519 directly sponged hsa-miR-1296-5p to reduce its repressive impact on DARS as well as activate the PI3K/AKT/mTOR signaling pathway. The malignant phenotypes of LUAD cells induced by upregulation of hsa_circ_0000519 could be rescued by hsa-miR-1296-5p overexpression or knockdown of DARS. In conclusion, hsa_circ_0000519 promotes LUAD progression through the hsa-miR-1296-5p/DARS axis and may be expected as a novel biomarker and therapeutic for LUAD.

The NLRP11 Protein Bridges the Histone Lysine Acetyltransferase KAT7 to Acetylate Vimentin in the Early Stage of Lung Adenocarcinoma.

Accumulation of vimentin is the core event in epithelial-mesenchymal transition (EMT). Post-translational modifications have been widely reported to play crucial roles in imparting different properties and functions to vimentin. Here, a novel modification of vimentin, acetylated at Lys104 (vimentin-K104Ac) is identified, which is stable in lung adenocarcinoma (LUAD) cells. Mechanistically, NACHT, LRR, and PYD domain-containing protein 11 (NLRP11), a regulator of the inflammatory response, bind to vimentin and promote vimentin-K104Ac expression, which is highly expressed in the early stages of LUAD and frequently appears in vimentin-positive LUAD tissues. In addition, it is observed that an acetyltransferase, lysine acetyltransferase 7 (KAT7), which binds to NLRP11 and vimentin, directly mediates the acetylation of vimentin at Lys104 and that the cytoplasmic localization of KAT7 can be induced by NLRP11. Malignant promotion mediated by transfection with vimentin-K104Q is noticeably greater than that mediated by transfection with vimentin-WT. Further, suppressing the effects of NLRP11 and KAT7 on vimentin noticeably inhibited the malignant behavior of vimentin-positive LUAD in vivo and in vitro. In summary, these findings have established a relationship between inflammation and EMT, which is reflected via KAT7-mediated acetylation of vimentin at Lys104 dependent on NLRP11.

A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD.

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (<1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens <1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.

Overexpression of LINC00551 promotes autophagy-dependent ferroptosis of lung adenocarcinoma via upregulating DDIT4 by sponging miR-4328.

According to mounting evidence, long noncoding RNAs (lncRNAs) play a vital role in regulated cell death (RCD). A potential strategy for cancer therapy involves triggering ferroptosis, a novel form of RCD. Although it is thought to be an autophagy-dependent process, it is still unclear how the two processes interact. This study characterized a long intergenic noncoding RNA, LINC00551, expressed at a low level in lung adenocarcinoma (LUAD) and some other cancers. Overexpression of LINC00551 suppresses cell viability while promoting autophagy and RSL-3-induced ferroptosis in LUAD cells. LINC00551 acts as a competing endogenous RNA (ceRNA) and binds with miR-4328 which up-regulates the target DNA damage-inducible transcript 4 (DDIT4). DDIT4 inhibits the activity of mTOR, promotes LUAD autophagy, and then promotes the ferroptosis of LUAD cells in an autophagy-dependent manner. This study provided an insight into the molecular mechanism regulating ferroptosis and highlighted LINC00551 as a potential therapeutic target for LUAD.

Decreased IL-6 and NK Cells in Early-Stage Lung Adenocarcinoma Presenting as Ground-Glass Opacity.

BACKGROUND: Lung ground-glass opacities (GGOs) are an early manifestation of lung adenocarcinoma. It is of great value to study the changes in the immune microenvironment of GGO to elucidate the occurrence and evolution of early lung adenocarcinoma. Although the changes of IL-6 and NK cells in lung adenocarcinoma have caught global attention, we have little appreciation for how IL-6 and NK cells in the lung GGO affect the progression of early lung adenocarcinoma. METHODS: We analyzed the RNA sequencing data of surgical specimens from 21 patients with GGO-featured primary lung adenocarcinoma and verified the changes in the expression of IL-6 and other important immune molecules in the TCGA and GEO databases. Next, we used flow cytometry to detect the protein expression levels of important Th1/Th2 cytokines in GGO and normal lung tissues and the changes in the composition ratio of tumor infiltrating lymphocytes (TILs). Then, we analyzed the effect of IL-6 on NK cells through organoid culture and immunofluorescence. Finally, we explored the changes of related molecules and pathway might be involved. RESULTS: IL-6 may play an important role in the tumor microenvironment of early lung adenocarcinoma. Further research confirmed that the decrease of IL-6 in GGO tissue is consistent with the changes in NK cells, and there seems to be a correlation between these two phenomena. CONCLUSION: The IL-6 expression status and NK cell levels of early lung adenocarcinoma as GGO are significantly reduced, and the stimulation of IL-6 can up-regulate or activate NK cells in GGO, providing new insights into the diagnosis and pathogenesis of early lung cancer.

A Novel Model Based on Genomic Instability-Associated Long Non-Coding RNAs for Predicting Prognosis and Response to Immunotherapy in Patients With Lung Adenocarcinoma.

Background: Emerging scientific evidence has shown that long non-coding RNAs (lncRNAs) exert critical roles in genomic instability (GI), which is considered a hallmark of cancer. To date, the prognostic value of GI-associated lncRNAs (GI-lncRNAs) remains largely unexplored in lung adenocarcinoma (LUAC). The aims of this study were to identify GI-lncRNAs associated with the survival of LUAC patients, and to develop a novel GI-lncRNA-based prognostic model (GI-lncRNA model) for LUAC. Methods: Clinicopathological data of LUAC patients, and their expression profiles of lncRNAs and somatic mutations were obtained from The Cancer Genome Atlas database. Pearson correlation analysis was conducted to identify the co-expressed mRNAs of GI-lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to determine the main biological function and molecular pathways of the differentially expressed GI-lncRNAs. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify GI-lncRNAs significantly related to overall survival (OS) for construction of the GI-lncRNA model. Kaplan-Meier survival analysis and receiver operating characteristic curve analysis were performed to evaluate the predictive accuracy. The performance of the newly developed GI-lncRNA model was compared with the recently published lncRNA-based prognostic index models. Results: A total of 19 GI-lncRNAs were found to be significantly associated with OS, of which 9 were identified by multivariate analysis to construct the GI-lncRNA model. Notably, the GI-lncRNA model showed a prognostic value independent of key clinical characteristics. Further performance evaluation indicated that the area under the curve (AUC) of the GI-lncRNA model was 0.771, which was greater than that of the TP53 mutation status and three existing lncRNA-based models in predicting the prognosis of patients with LUAC. In addition, the GI-lncRNA model was highly correlated with programed death ligand 1 (PD-L1) expression and tumor mutational burden in immunotherapy for LUAC. Conclusion: The GI-lncRNA model was established and its performance was found to be superior to existing lncRNA-based models. As such, the GI-lncRNA model holds promise as a more accurate prognostic tool for the prediction of prognosis and response to immunotherapy in patients with LUAC.

Construction and validation of a 15-gene ferroptosis signature in lung adenocarcinoma.

BACKGROUND: Ferroptosis is a novel form of programmed cell death characterized by the excessive accumulation of intracellular iron and an increase in reactive oxygen species. Emerging studies have shown that ferroptosis plays a vital role in the progression of lung adenocarcinoma, but the effect of ferroptosis-related genes on prognosis has been poorly studied. The purpose of this study was to explore the prognostic value of ferroptosis-related genes. METHODS: Lung adenocarcinoma samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to establish a predictive signature for risk stratification. Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the signature. We further explored the potential correlation between the risk score model and tumor immune status. RESULTS: A 15-gene ferroptosis signature was constructed to classify patients into different risk groups. The overall survival (OS) of patients in the high-risk group was significantly shorter than that of patients in the low-risk group. The signature could predict OS independent of other risk factors. Single-sample gene set enrichment analysis (ssGSEA) identified the difference in immune status between the two groups. Patients in the high-risk group had stronger immune suppression, especially in the antigen presentation process. CONCLUSIONS: The 15-gene ferroptosis signature identified in this study could be a potential biomarker for prognosis prediction in lung adenocarcinoma. Targeting ferroptosis might be a promising therapeutic alternative for lung adenocarcinoma.

N6-Methyladenosine RNA Methylation Regulator-Related Alternative Splicing (AS) Gene Signature Predicts Non-Small Cell Lung Cancer Prognosis.

Aberrant N6-methyladenosine (m6A) RNA methylation regulatory genes and related gene alternative splicing (AS) could be used to predict the prognosis of non-small cell lung carcinoma. This study focused on 13 m6A regulatory genes (METTL3, METTL14, WTAP, KIAA1429, RBM15, ZC3H13, YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC, FTO, and ALKBH5) and expression profiles in TCGA-LUAD (n = 504) and TCGA-LUSC (n = 479) datasets from the Cancer Genome Atlas database. The data were downloaded and bioinformatically and statistically analyzed, including the gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. There were 43,948 mRNA splicing events in lung adenocarcinoma (LUAD) and 46,020 in lung squamous cell carcinoma (LUSC), and the data suggested that m6A regulators could regulate mRNA splicing. Differential HNRNPC and RBM15 expression was associated with overall survival (OS) of LUAD and HNRNPC and METTL3 expression with the OS of LUSC patients. Furthermore, the non-small cell lung cancer prognosis-related AS events signature was constructed and divided patients into high- vs. low-risk groups using seven and 14 AS genes in LUAD and LUSC, respectively. The LUAD risk signature was associated with gender and T, N, and TNM stages, but the LUSC risk signature was not associated with any clinical features. In addition, the risk signature and TNM stage were independent prognostic predictors in LUAD and the risk signature and T stage were independent prognostic predictors in LUSC after the multivariate Cox regression and receiver operating characteristic analyses. In conclusion, this study revealed the AS prognostic signature in the prediction of LUAD and LUSC prognosis.

DNA methylome and transcriptome analysis established a model of four differentially methylated positions (DMPs) as a diagnostic marker in esophageal adenocarcinoma early detection.

BACKGROUND: Esophageal carcinogenesis involves in alterations of DNA methylation and gene transcription. This study profiled genomic DNA methylome vs. gene expression using transcriptome data on esophageal adenocarcinoma (EAC) tissues from the online databases in order to identify methylation biomarkers in EAC early diagnosis. MATERIALS AND METHODS: The DNA methylome and transcriptome data were downloaded from the UCSC Xena, Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases and then bioinformatically analyzed for the differentially methylated positions (DMPs) vs. gene expression between EAC and normal tissues. The highly methylated DMPs vs. reduced gene expression in EAC were selected and then stratified with those of the corresponding normal blood samples and other common human cancers to construct an EAC-specific diagnostic model. The usefulness of this model was further verified in other three GEO datasets of EAC tissues. RESULT: A total of 841 DMPs were associated with expression of 320 genes, some of which were aberrantly methylated in EAC tissues. Further analysis showed that four (cg07589773, cg10474350, cg13011388 and cg15208375 mapped to gene IKZF1, HOXA7, EFS and TSHZ3, respectively) of these 841 DMPs could form and establish a diagnostic model after stratified them with the corresponding normal blood samples and other common human cancers. The data were further validated in other three GEO datasets on EAC tissues in early EAC diagnosis. CONCLUSION: This study revealed a diagnostic model of four genes methylation to diagnose EAC early. Further study will confirm the usefulness of this model in a prospective EAC cases.