Systematic analysis identifies three-lncRNA signature as a potentially prognostic biomarker for lung squamous cell carcinoma using bioinformatics strategy

Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma

Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.

Lung proteomics combined with metabolomics reveals molecular characteristics of inflammation-related lung tumorigenesis induced by B(a)P and LPS

Inflammatory microenvironment may take a promoting role in lung tumorigenesis. However, the molecular characteristics underlying inflammation-related lung cancer remains unknown. In this work, the inflammation-related lung tumorigenesis mouse model was established by treated with B(a)P (1 mg/mouse, once a week for 4 weeks), followed by LPS (2.5 μg/mouse, once every 3 weeks for five times), the mice were sacrificed 30 weeks after exposure. TMT-labeled quantitative proteomics and untargeted metabolomics were used to interrogate differentially expressed proteins and metabolites in different mouse cancer tissues, followed by integrated crosstalk between proteomics and metabolomics through Spearman's correlation analysis. The result showed that compared with the control group, 103 proteins and 37 metabolites in B(a)P/LPS group were identified as significantly altered. By searching KEGG pathway database, proteomics pathways such as Leishmaniasis, Asthma and Intestinal immune network for IgA production, metabolomics pathways such as Vascular smooth muscle contraction, Linoleic acid metabolism and cGMP-PKG signaling pathway were enriched. A total of 22 pathways were enriched after conjoint analysis of the proteomic and metabolomics, and purine metabolism pathway, the unique metabolism-related pathway, which included significantly altered protein (adenylate cyclase 4, ADCY4) and metabolites (L-Glutamine, guanosine monophosphate (GMP), adenosine and guanosine) was found. Results suggested purine metabolism may contribute to the inflammation-related lung tumorigenesis, which may provide novel clues for the therapeutic strategies of inflammation-related lung cancer.

LncRNA POU6F2-AS2 contributes to malignant phenotypes and paclitaxel resistance by promoting SKP2 expression in stomach adenocarcinoma

This study aimed to investigate the role and mechanism of POU6F2-AS2 in the development of gastric cancer. POU6F2-AS2 expression was considerably higher in clinical stomach adenocarcinoma (STAD) tissues and gastric cancer cell lines (MKN-28 and MGC-803) than in neighbouring normal tissues and gastric mucosa epithelial cells (GES-1). POU6F2-AS2 overexpression resulted in a low overall survival probability, progression-free survival probability and post progression survival probability, as well as increased cell viability, migration and invasion of gastric cancer cells, thereby inhibiting apoptosis. Based on RNA pull-down, cycloheximide and MG132 incubation experiments, POU6F2-AS2 promoted SKP2 by stabilizing NONO expression. In addition, in vivo silencing of POU6F2-AS2 in gastric cancer cells can inhibit tumour progression and produce a synergistic antitumour effect when combined with paclitaxel. POU6F2-AS2 is overexpressed in STAD, which is attributed to a bad prognosis. In vitro and in vivo experiments have confirmed that the POU6F2-AS2/NONO/SKP2 axis promotes STAD progression, and that the silencing of POU6F2-AS2 plays a synergistic antitumour effect when combined with paclitaxel. Therefore, POU6F2-AS2 may be potentially developed as a target to inhibit STAD and reduce chemoresistance.

Non-coding RNAs in lung cancer: molecular mechanisms and clinical applications

Lung cancer (LC) is a heterogeneous disease with high malignant degree, rapid growth, and early metastasis. The clinical outcomes of LC patients are generally poor due to the insufficient elucidation of pathological mechanisms, low efficiency of detection and assessment methods, and lack of individualized therapeutic strategies. Non-coding RNAs (ncRNAs), including microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA), are endogenous regulators that are widely involved in the modulation of almost all aspects of life activities, from organogenesis and aging to immunity and cancer. They commonly play vital roles in various biological processes by regulating gene expression via their interactions with DNA, RNA, or protein. An increasing amount of studies have demonstrated that ncRNAs are closely correlated with the initiation and development of LC. Their dysregulation promotes the progression of LC via distinct mechanisms, such as influencing protein activity, activating oncogenic signaling pathways, or altering specific gene expression. Furthermore, some ncRNAs present certain clinical values as biomarker candidates and therapeutic targets for LC patients. A complete understanding of their mechanisms in LC progression may be highly beneficial to developing ncRNA-based therapeutics for LC patients. This review mainly focuses on the intricate mechanisms of miRNA, lncRNA, and circRNA involved in LC progression and discuss their underlying applications in LC treatment.

Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell carcinoma, accounting for about 30% of all lung cancers. Yet, the evaluation of prognostic outcome and therapy response of patients with LUSC remains to be resolved. This study aimed to explore the prognostic value of cell death pathways and develop a cell death-associated signature for predicting prognosis and guiding treatment in LUSC.

METHODS

Transcriptome profiles and corresponding clinical information of LUSC patients were gathered from The Cancer Genome Atlas (TCGA-LUSC, n = 493) and Gene Expression Omnibus database (GSE74777, n = 107). The cell death-related genes including autophagy (n = 348), apoptosis (n = 163), and necrosis (n = 166) were retrieved from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. In the training cohort (TCGA-LUSC), LASSO Cox regression was used to construct four prognostic signatures of respective autophagy, apoptosis, and necrosis pathway and genes of three pathways. After comparing the four signatures, the cell death index (CDI), the signature of combined genes, was further validated in the GSE74777 dataset. We also investigated the clinical significance of the CDI signature in predicting the immunotherapeutic response of LUSC patients.

RESULTS

The CDI signature was significantly associated with the overall survival of LUSC patients in the training cohort (HR, 2.13; 95% CI, 1.62‒2.82; P < 0.001) and in the validation cohort (HR, 1.94; 95% CI, 1.01‒3.72; P = 0.04). The differentially expressed genes between the high- and low-risk groups contained cell death-associated cytokines and were enriched in immune-associated pathways. We also found a higher infiltration of naive CD4⁺ T cells, monocytes, activated dendritic cells, neutrophils, and lower infiltration of plasma cells and resting memory CD4⁺ T cells in the high-risk group. Tumor stemness indices, mRNAsi and mDNAsi, were both negatively correlated with the risk score of the CDI. Moreover, LUSC patients in the low-risk group are more likely to respond to immunotherapy than those in the high-risk group (P = 0.002).

CONCLUSIONS

This study revealed a reliable cell death-associated signature (CDI) that closely correlated with prognosis and the tumor microenvironment in LUSC, which may assist in predicting the prognosis and response to immunotherapy for patients with LUSC.

Immune-Related Long Non-Coding RNA Signatures for Tongue Squamous Cell Carcinoma

BACKGROUND

Tongue squamous cell carcinoma (TSCC) represents one of the major subsets of head and neck cancer, which is characterized by unfavorable prognosis, frequent lymph node metastasis, and high mortality rate. The molecular events regulating tongue tumorigenesis remain elusive. In this study, we aimed to identify and evaluate immune-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in TSCC.

METHODS

The lncRNA expression data for TSCC were obtained from The Cancer Genome Atlas (TCGA) and the immune-related genes were downloaded from the Immunology Database and Analysis Portal (ImmPort). Pearson correlation analysis was performed to identify immune-related lncRNAs. The TCGA TSCC patient cohort was randomly divided into training and testing cohorts. In the training cohort, univariate and multivariate Cox regression analyses were used to determining key immune-related lncRNAs, which were then validated through Cox regression analysis, principal component analysis (PCA), and receiver operating characteristic (ROC) analysis in the testing cohort.

RESULTS

Six immune-related signature lncRNAs (MIR4713HG, AC104088.1, LINC00534, NAALADL2-AS2, AC083967.1, FNDC1-IT1) were found to have prognostic value in TSCC. Multivariate and univariate cox regression analyses showed that the risk score based on our six-lncRNA model, when compared to other clinicopathological factors (age, gender, stage, N, T), was an important indicator of survival rate. In addition, Kaplan-Meier survival analysis demonstrated significantly higher overall survival in the low-risk patient group than the high-risk patient group within both training and testing cohorts. The ROC analysis indicated that the AUCs for 5-year overall survival were 0.790, 0.691, and 0.721, respectively, for training, testing, and entire cohorts. Finally, PCA analysis demonstrated that the high-risk and low-risk patient groups presented significant deviation regarding their immune status.

CONCLUSIONS

A prognostic model based on six immune-related signature lncRNAs was established. This six-lncRNA prognostic model has clinical significance and may be helpful in the development of personalized immunotherapy strategies.

Development and validation of a novel ferroptosis‑related lncRNA prognostic signature for pancreatic adenocarcinoma

Long non‑coding RNAs (lncRNAs) serve a pivotal role in the regulation of cancer cell ferroptosis. However, the prognostic value of ferroptosis‑related lncRNAs in pancreatic adenocarcinoma (PAAD) largely remains unclear. We aimed at constructing a lncRNA‑based signature to improve the prognosis prediction of PAAD. In the present study, the transcriptome profiling data and clinical information of patients with PAAD were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Gene Consortium (ICGC) databases. Univariate Cox regression analysis of the TCGA cohort demonstrated that 26 ferroptosis‑related lncRNAs had significant prognostic value for PAAD (all P<0.01). Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazards regression analyses were performed to construct a prognostic ferroptosis‑related lncRNA signature (FRLS) comprising nine ferroptosis‑related lncRNAs. The efficacy of this FRLS was verified in the training (TCGA) and validation (ICGC) cohorts. Based on the risk model, high risk scores were significantly correlated with poor overall survival (OS) (hazard ratio, 1.314; 95% confidence interval, 1.218‑1.418; P<0.001). The receiver operating characteristic curves and principal component analysis further demonstrated the robust prognostic ability of the FRLS. Furthermore, a nomogram with favorable predictive efficacy for the prediction of OS was constructed based on the FRLS and clinical features. Gene set enrichment analysis demonstrated that the genes in the FRLS participated in a number of cancer‑associated immunoregulatory pathways. Importantly, it was demonstrated that immune infiltration and response to cancer immunotherapy differed significantly between the high and low‑risk groups according to the FRLS. In conclusion, the risk signature based on the FRLS has potential for the clinical prediction of prognosis and immunotherapy response in patients with PAAD.

Pan-Cancer Analysis Identifies MNX1 and Associated Antisense Transcripts as Biomarkers for Cancer

The identification of diagnostic and prognostic biomarkers is a major objective in improving clinical outcomes in cancer, which has been facilitated by the availability of high-throughput gene expression data. A growing interest in non-coding genomic regions has identified dysregulation of long non-coding RNAs (lncRNAs) in several malignancies, suggesting a potential use as biomarkers. In this study, we leveraged data from large-scale sequencing projects to uncover the expression patterns of the MNX1 gene and its associated lncRNAs MNX1-AS1 and MNX1-AS2 in solid tumours. Despite many reports describing MNX1 overexpression in several cancers, limited studies exist on MNX1-AS1 and MNX1-AS2 and their potential as biomarkers. By employing clustering methods to visualise multi-gene relationships, we identified a discriminative power of the three genes in distinguishing tumour vs. normal samples in several cancers of the gastrointestinal tract and reproductive systems, as well as in discerning oesophageal and testicular cancer histological subtypes. Notably, the expressions of MNX1 and its antisenses also correlated with clinical features and endpoints, uncovering previously unreported associations. This work highlights the advantages of using combinatory expression patterns of non-coding transcripts of differentially expressed genes as clinical evaluators and identifies MNX1, MNX1-AS1, and MNX1-AS2 expressions as robust candidate biomarkers for clinical applications.

Systematic analysis competing endogenous RNA coexpression network as a potentially prediction prognostic biomarker for colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common types of colon cancer, represents a major public health issue due to its high incidence and mortality. Competing endogenous RNAs (ceRNAs) hypothesis has generated a great interest in the study of molecular biological mechanisms of cancer progression. The aim of this study was to identify potential prediction prognostic biomarker associated with progression of COAD and illuminate regulatory mechanisms. Two RNA sequencing datasets downloaded from the Genotype-Tissue Expression and TCGA. The differentially expressed RNAs were analyzed. Weighted correlation network analysis was used to analyze the similarity of genes model with a trait in the network. Interactions between lncRNAs, miRNAs, and target mRNAs were predicted by MiRcode, starBase, miRTarBase, miRDB, and TargetScan, and the risk score of mRNAs was established. Based on the identified prognostic signature and independent clinical factors, then the nomogram survival model was built. Totally, we identified 3537 differentially expressed mRNAs, 2379 lncRNAs, and 449 microRNAs. Based on the 8 prognosis-associated mRNAs (CCNA2 + CEBPA + NEBL + SOX9 + DLG4 + RIMKLB + TCF7L1 + TUB), the risk score was proposed. After the independent clinical prognostic factors were identified, the nomogram survival model was built. LncRNA-miRNA-mRNA ceRNA network was built by 68 lncRNAs, 4 miRNAs, and 6 mRNAs, which might serve as prognostic biomarkers of COAD. These findings suggest several genes in ceRNA network might be novel important prognostic biomarkers and potential targets for COAD. CeRNA networks could provide further insight into the mRNA-related regulatory mechanism and COAD prognosis.

Identification of pyroptosis-related long non-coding RNAs with prognosis and therapy in lung squamous cell carcinoma

Pyroptosis is a type of programmed cell death with an intense inflammatory response. Previous studies have shown that pyroptosis plays an important role in the pathogenesis and progression of lung cancer. However, the prognostic value and drug targets of pyroptosis-related lncRNAs in lung squamous cell carcinoma (LSCC) have never been studied. In the present study, we identified 1468 pyroptosis-related lncRNAs in LSCC by performing Pearson correlation analysis between the pyroptosis-related genes and the lncRNAs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The whole set was divided into a training and a test set with a 1:1 ratio. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to establish an 11 multilncRNA signature in the three sets. The signature divided LSCC patients into the low-risk and the high-risk groups. Kaplan–Meier analysis and receiver operating characteristic (ROC) indicated that the prognostic signature had a promising predictive capability for LSCC patients. Besides, the association of microenvironment and immunotherapy response with signature was also analyzed. Moreover, 28 potential compounds targeting signature were screened as possible drugs to treat LSCC. Finally, a nomogram model was constructed to offer the quantitative prediction and net benefit for the prognosis of LSCC patients. In conclusion, the 11 pyroptosis-related lncRNAs and their signature may be promising prognostic factors and therapeutic targets for patients with LSCC.

Clinicopathological implications of lncRNAs, immunotherapy and DNA methylation in lung squamous cell carcinoma: a narrative review

Objective

To explore the clinicopathological impact of lncRNAs, immunotherapy, and DNA methylation in lung squamous cell carcinoma (LUSC), emphasizing their exact roles in carcinogenesis and modes of action.

Background

LUSC is the second most prevalent form, accounting for around 30% of non-small cell lung cancer (NSCLC). To date, molecular-targeted treatments have significantly improved overall survival in lung adenocarcinoma patients but have had little effect on LUSC therapy. As a result, there is an urgent need to discover new treatments for LUSC that are based on existing genomic methods.

Methods

In this review, we summarized and analyzed recent research on the biological activities and processes of lncRNA, immunotherapy, and DNA methylation in the formation of LUSC. The relevant studies were retrieved using a thorough search of Pubmed, Web of Science, Science Direct, Google Scholar, and the university's online library, among other sources.

Conclusions

LncRNAs are the primary components of the mammalian transcriptome and are emerging as master regulators of a number of cellular processes, including the cell cycle, differentiation, apoptosis, and growth, and are implicated in the pathogenesis of a variety of cancers, including LUSC. Understanding their role in LUSC in detail may help develop innovative treatment methods and tactics for LUSC. Meanwhile, immunotherapy has transformed the LUSC treatment and is now considered the new standard of care. To get a better knowledge of LUSC biology, it is critical to develop superior modeling systems. Preclinical models, particularly those that resemble human illness by preserving the tumor immune environment, are essential for studying cancer progression and evaluating novel treatment targets. DNA methylation, similarly, is a component of epigenetic alterations that regulate cellular function and contribute to cancer development. By methylating the promoter regions of tumor suppressor genes, abnormal DNA methylation silences their expression. DNA methylation indicators are critical in the early detection of lung cancer, predicting therapy efficacy, and tracking treatment resistance. As such, this review seeks to explore the clinicopathological impact of lncRNAs, immunotherapy, and DNA methylation in LUSC, emphasizing their exact roles in carcinogenesis and modes of action.

Identification of LTF as a Prognostic Biomarker for Osteosarcoma

Osteosarcoma remains a major health problem in teenagers. However, its pathogenesis mechanism remains not fully elucidated. This study aims to identify the prognostic biomarkers for osteosarcoma. In this study, we selected genes with a median absolute deviation (MAD) value of the top 5000 in the GSE32981 dataset for subsequent analysis. Weighted correlation network analysis (WGCNA) was used to construct a coexpression network. WGCNA showed that the tan module and midnight blue module were highly correlated with origin and metastases of osteosarcoma, respectively. Enrichment analysis was conducted using genes in the tan module and midnight blue module. A gene coexpression network was constructed by calculating the Spearman correlation coefficients. Four key genes (LTF, C10orf107, HIST1H2AK, and NEXN) were identified to be correlated with the prognosis of osteosarcoma patients. LTF has the highest AUC value, and its effect on osteosarcoma cells was then evaluated. The effect of LTF overexpression on proliferation, migration, and invasion of MG63 and 143B cells was detected by the CCK-8 assay, transwell cell migration assay, and transwell invasion assay, respectively. The overexpression of LTF promoted the proliferation, migration, and invasion of MG63 and 143B cells. In conclusion, LTF may serve as a prognostic biomarker for osteosarcoma.

WT1-AS/IGF2BP2 Axis Is a Potential Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma According to ceRNA Network Comprehensive Analysis Combined with Experiments

Lung adenocarcinoma (LUAD) is one of the most common malignancies, and there is still a lack of effective biomarkers for early detection and prognostic prediction. Here, we comprehensively analyze the characteristics of. an RNA sequencing data set of LUAD samples. In total, 395 long non-coding RNAs (lncRNAs), 89 microRNAs (miRNAs), and 872 mRNAs associated with c-Myc were identified, which were differentially expressed between tumor and normal tissues. The most relevant pathway was found to be WT1-AS–miR-200a-3p–IGF2BP2 according to the rules of competitive endogenous RNA (ceRNA) regulation. WT1-AS and IGF2BP2 expression were positively correlated and increased in LUAD samples, while miR-200a-3p had relatively low expression. The high expression of WT1-AS and IGF2BP2 was associated with poor prognosis in LUAD patients, while low expression of miR-200a-3p predicted reduced survival (p < 0.05). The analysis of the multi-gene regulation model indicated that the WT1-AS (downregulation)–miR-200a-3p (upregulation)–IGF2BP2 (downregulation) pattern significantly improved the survival of LUAD patients. Finally, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting were detected in LUAD cells, and the results are consistent with the bioinformatics analysis. In summary, the WT1-AS/IGF2BP2 axis is a potential prognostic biomarker in LUAD and is expected to become an effective target for diagnosis and treatment.

DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Low Expression of ADCY4 Predicts Worse Survival of Lung Squamous Cell Carcinoma Based on Integrated Analysis and Immunohistochemical Verification

Purpose

The molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC.

Methods

A microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings.

Results

A list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell-cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels.

Conclusions

This analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982-1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921-1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920-1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

The tumor environment immune phenotype of LUSC by genome-wide analysis

PURPOSE

To compare the landscape of tumor microenvironment (TME) of lung squamous carcinoma (LUSC) in different immune pattern and explore potential factors on immune therapy and prognosis.

METHOD AND MATERIALS

We have obtained the LUSC data from TCGA, GEO, and our department and classified them into 2 TME clusters by random forest model based on the infiltration pattern of 24 immune cell populations. We systemically compared the genomic significance, clinical characteristics, and immune infiltration pattern in 2 TME clusters.

RESULTS

Samples were divided into 2 TME clusters based on the relative abundance of 24 immune cells, and a random forest classifier model was constructed. TME cluster B was a higher immune infiltration group with lower mutation load, richer co-infiltrate immune cells, upregulated immune-related cytokines, immune checkpoint molecules, and higher active immune cells. TME cluster was also an independent predictor in prognosis (B vs. A, p < 0.05) in patients from TCGA, GEO, and our department.

CONCLUSIONS

Our study has described the microenvironment landscape of LUSC in different immune infiltration patterns and systemically analyzed genomic and clinical characteristics with distinct immunophenotypes, thus partly revealed the interaction between tumors and the immune microenvironment, which may guide a more precise and personalized immune therapeutic strategy for LUSC patients.

Prognostic significance of eight immune-related genes on survival in patients with lung squamous cell carcinoma

Aim: To build a valid prognostic model based on immune-related genes for lung squamous cell carcinoma (LUSC). Materials & methods: Differential expression of immune-related genes between LUSC and normal specimens from TCGA dataset and underlying molecular mechanisms were systematically analyzed. Constructing and validating the high-risk and low-risk groups for LUSC survival. Results: The immune-related gene-based prognostic index (IRGPI) could predict the overall survival in patients with different clinicopathological characteristics. Functional enrichment analysis of differential expression of immune-related gene signature indicated distinctive molecular pathways between high-risk and low-risk groups. Conclusion: Analysis of IRGs in LUSC enable us to stratify patients into distinct risk groups, which may help to screen LUSC patients at risk and decision making on follow-up therapeutic intervention.

Integrating Multi–Omics Data for Gene-Environment Interactions

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA-miRNA-mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network was highly enriched in some cancer-associated signaling pathways. Next, lasso-Cox model was run 1,000 times to recognize the potential survival-related combinations of the candidate lncRNAs in the ceRNA network, followed by the "best subset selection" to further optimize these lncRNA-based combinations, and a seven-lncRNA prognostic signature with the best performance was determined. Based on the median risk score, LUAD patients could be well distinguished into high-/low-risk subgroups. The Kaplan-Meier survival curve showed that LUAD patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (log-rank test P = 4.52 × 10^-9). The ROC curve indicated that the clinical genomic model including both the TNM staging system and the signature had a superior performance in predicting the patients' overall survival compared to the clinical model with the TNM staging system only. Further stratification analysis suggested that the signature could work well in the different strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.

Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

Lung adenocarcinoma (LUAD), which accounts for 60% of non-small-cell lung cancers, is poorly diagnosed and has a low average 5-year survival rate (approximately 20%). It remains the leading cause of cancer-related deaths worldwide. Studies on long noncoding RNAs (lncRNAs) in LUAD-related competing endogenous RNA (ceRNA) networks are limited. We aimed to identify novel prognostic biomarkers for LUAD using bioinformatic tools and data analysis. We systemically integrated differentially expressed genes and clinically significant modules using weighted correlation network analysis. We performed a functional analysis of the collected candidate genes and explored three LUAD-related genes (VWF, PECAM1, and COL1A1) associated with the overall survival rates of patients with LUAD. Based on Cox proportional hazards analysis of candidate mRNAs and lncRNAs together with differentially expressed microRNAs, we constructed ceRNA networks, obtained 12 lncRNAs in the ceRNA networks, and revealed seven novel lncRNAs AC021016.2, AC079630.1, AC116407.1, AC125807.2, AF131215.5, LINC01936, and RHOXF1-AS1. These lncRNAs were found to be associated with overall survival rates and are suitable for the prediction of prognosis by Kaplan-Meier survival and receiver operating characteristic curve analyses. In particular, three lncRNAs—AF131215.5, AC125807.2, and LINC01936—showed an independent prognostic value of overall survival for patients with LUAD. We evaluated the diagnostic capabilities of seven lncRNAs for patients with LUAD using principal component analysis and the Gene Set Variation Analysis index. lncRNAs and crucial genes could be effectively used for distinguishing LUAD tumors from normal tissues in the Gene Expression Omnibus profile. In particular, AC021016.2 showed a significant prognostic value in the validation dataset. Our findings reveal the significance of exploring lncRNAs in cancer-related ceRNAs using bioinformatic strategies.

A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction.

METHODS

The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature.

RESULTS

Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8⁺ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature.

CONCLUSION

Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

Long non-coding RNA nuclear paraspeckle assembly transcript 1 protects human lens epithelial cells against H2O2 stimuli through the nuclear factor kappa b/p65 and p38/mitogen-activated protein kinase axis

Background

Long non-coding RNA (lncRNA) nuclear paraspeckle assembly transcript 1 (NEAT1) plays a regulatory role in many biological processes; however, its role in cataracts has yet to be illuminated. This study aimed to investigate the protective role of NEAT1 in hydrogen peroxide (H₂O₂)-treated human lens epithelial cells (HLECs) and its underlying molecular mechanism.

Methods

HLECs (SRA01/04) were treated with 300 µM H₂O₂ to mimic cataract in vitro. Cell viability was detected by performing an MTT assay and EdU staining. Flow cytometry was carried out to detect apoptosis of HLECs. DNA damage was examined using γ-H2A histone family member X staining. and reactive oxygen species (ROS) production was measured using 2’,7’dichlorofluorescin diacetate staining. The expression levels of lncRNA and proteins were detected with quantitative real-time polymerase chain reaction and western blot, respectively.

Results

The expression of NEAT1 was observed to be increased in H₂O₂-treated HLECs and age-related cataract (ARC) tissues. Knockdown NEAT1 strongly protected against H₂O₂-induced cell death and also regulated the expression of cleaved caspase-3, B-cell lymphoma 2, and Bcl-2-associated X protein. Further, knockdown NEAT1 also significantly suppressed H₂O₂-induced intracellular ROS production and malondialdehyde (MDA) content, but elevated the glutathione (GSH) activity of H₂O₂-treated cells. Also, it is demonstrated that si-NEAT1 greatly inhibited H₂O₂-induced phosphorylation of NF-кB p65 and p38 MAPK.

Conclusions

This study confirmed that knockdown NEAT1 attenuated H₂O₂-induced damage in HLECs, and inhibited the oxidative stress and apoptosis of HLECs via regulating nuclear factor-kappa B (NF-κB) p65 and p38 MAPK signaling. It may provide a potential target for clinical treatment of cataracts.

LINC01235-TWIST2 feedback loop facilitates epithelial-mesenchymal transition in gastric cancer by inhibiting THBS2

Although the anomalous expression of long non-coding RNAs (lncRNAs) has been extensively investigated in numerous carcinomas including gastric cancer (GC), their function remains unclear. The aim of our study was to explore the role of LINC01235 in GC. We used real-time quantitative PCR (RT-qPCR) to measure the expression of LINC01235 and twist family bHLH transcription factor 2 (TWIST2) in GC tissues. Scratch and transwell assays were performed to evaluate cellular capacity for migration and invasion. Gene relationships were explored by Weighted Gene Co-Expression Network Analysis (WGCNA). We measured TWIST2, thrombospondin 2 (THBS2) and epithelial-mesenchymal transition (EMT)-related proteins with western blot. We also used Pearson correlation analysis and the Kaplan-Meier method to detect associations among genes and overall survival. We found that LINC01235 was upregulated in GC tissues and cells. LINC01235 down-regulation restricted migration and invasion. Interestingly, we found the LINC01235-TWIST2-THBS2 axis induced EMT. Additionally, TWIST2 upregulated LINC01235 transcription in luciferase and chromatin immunoprecipitation (ChIP) assays. Bioinformatics analysis showed that microRNA (miR)-6852-5p might be a key gene involved in the regulation of TWIST2 by LINC01235. The LINC01235-TWIST2 positive feedback loop mainly affected migration and invasion of GC cells, which suggests it may serve as a potential therapeutic target in gastric cancer.

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

BACKGROUND

Gastric cancer (GC) is a digestive system cancer with a high mortality rate globally. Previous experiences and studies have provided clinicians with ample evidence to diagnose and treat patients with reasonable therapeutic options. However, there remains a need for sensitive biomarkers that can provide clues for early diagnosis and prognosis assessment.

RESULTS

We found 610 independent prognosis-related 5'-cytosine-phosphate-guanine-3' (CpG) sites (P < 0.05) among 21,121 sites in the training samples. We divided the GC samples into seven clusters based on the selected 610 sites. Cluster 6 had relatively higher methylation levels and high survival rates than the other six clusters. A prognostic risk model was constructed using the significantly altered CpG sites in cluster 6 (P < 0.05). This model could distinguish high-risk GC patients from low-risk groups efficiently with the area under the receiver operating characteristic curve of 0.92. Risk assessment showed that the high-risk patients had poorer prognosis than the low-risk patients. The methylation levels of the selected sites in the established model decreased as the risk scores increased. This model had been validated in testing group and its effectiveness was confirmed. Corresponding genes of the independent prognosis-associated CpGs were identified, they were enriched in several pathways such as pathways in cancer and gastric cancer. Among all of the genes, the transcript level of transforming growth factor β2 (TGFβ2) was changed in different tumor stages, T categories, grades, and patients' survival states, and up-regulated in patients with GC compared with the normal. It was included in the pathways as pathways in cancer, hepatocellular carcinoma or gastric cancer. The methylation site located on the promoter of TGFβ2 was cg11976166.

CONCLUSIONS

This is the first study to separate GC into different molecular subtypes based on the CpG sites using a large number of samples. We constructed an effective prognosis risk model that can identify high-risk GC patients. The key CpGs sites or their corresponding genes such as TGFβ2 identified in this research can provide new clues that will enable gastroenterologists to make diagnosis or personalized prognosis assessments and better understand this disease.

Long non-coding RNA TRPM2-AS sponges microRNA-138-5p to activate epidermal growth factor receptor and PI3K/AKT signaling in non-small cell lung cancer

Background

Long non-coding RNAs (lncRNAs) can play pivotal roles in tumor progression by acting as microRNA (miRNA) sponges. This study aimed to investigate the association of a novel lncRNA, TRPM2-AS, with the miR-138-5p/EGFR axis in the development of non-small cell lung cancer (NSCLC).

Methods

Sixty NSCLC tissues and paired adjacent non-tumor tissues were analyzed. The relative expression levels of TRPM2-AS, miR138-5p, and epidermal growth factor receptor (EGFR) and the interactions between them were analyzed. The NSCLC cell lines NCI-H1299 and A549 were transfected with TRPM2-AS shRNA/pcDNA, and miR-138-5p mimics. Cell proliferation, migration, invasion, and apoptosis were examined in response to different transfection conditions. Dual-luciferase reporter assay was performed to identify the target interactions between TRPM2-AS, miR-138-5p, and EGFR. A549 cells stably transfected with shRNA were injected into BALB/c null nude mice to establish a tumor xenograft model.

Results

TRPM2-AS was up-regulated in NSCLC tumors and cell lines. Cell proliferation, migration, and invasion were inhibited in NSCLC cells treated with sh-TRPM2-AS, while apoptosis was induced. The targeting of TRPM2-AS by miR138-5p and miR138-5p by EGFR were validated with dual-luciferase reporter assay. TRPM2-AS was found to be negatively correlated with miR138-5p but positively correlated with EGFR. PI3K/AKT/mTOR was activated by pcDNA-EGFR but inactivated by miR-138-5p mimics. In the tumor xenograft mouse model, sh-TRPM2-AS suppressed tumor formation, reduced the expression of EGFR and Ki67, and promoted tumor cell apoptosis.

Conclusions

Our results suggested that TRPM2-AS can increase the levels of EGFR via sponging miR-138-3p; this promoted NSCLC cell proliferation, migration, and invasion in vitro, and exacerbated tumors in vivo. These findings highlight TRPM2-AS/miR-138-5p as a potential target for reducing drug resistance in patients with NSCLC.

13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common type of cancer with a high mortality rate and is usually detected at the middle or late stage, missing the optimal treatment period. The current study aims to identify potential long non-coding RNA (lncRNAs) biomarkers that contribute to the diagnosis and prognosis of HCC.

METHODS

The differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate, and multivariate Cox regression. The models for predicting diagnosis and prognosis were established, respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis.

RESULTS

We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set, and entire cohort, the low-risk group had longer survival than the high-risk group (median OS: 3124 vs. 649 days, 2456 vs. 770 days and 3124 vs. 755 days). It performed well in 1-, 3-, and 5-year survival prediction. 13-lncRNAs-based risk score, age, and race were good predictors of prognosis. The AUC of diagnosis was 0.9487, 0.9265, and 0.9376, respectively. Meanwhile, the 13-lncRNAs were involved in important pathways, including the cell cycle and multiple metabolic pathways.

CONCLUSION

In our study, the 13-lncRNAs signature may be a potential marker for the prognosis of HCC and improve the diagnosis.

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

The magnitude of the immune response is closely associated with clinical outcome in patients with cancer. However, finding potential therapeutic targets for lung cancer in the immune system remains challenging. Here, we constructed a vital immune‐prognosis genes (VIPGs) based cluster of lung adenocarcinoma (LUAD) from IMMPORT databases and The Cancer Genome Atlas. A transcription factor regulatory network for the VIPGs was also established. The tumor microenvironment of LUAD was analyzed using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and single‐sample Gene Set Enrichment Analysis. The immune checkpoints and genomic alterations were explored in the different immune clusters. We identified 15 VIPGs for patients with LUAD and clustered the patients into low‐immunity and high‐immunity subtypes. The immune score, stromal score and ESTIMATE score were significantly higher in the high‐immunity subtype, whereas tumor purity was higher in the low‐immunity subtype. In addition, the immune checkpoints cytotoxic T lymphocyte associate protein‐4(CTLA4), programmed cell death protein‐1 and programmed death‐ligand were elevated in the low‐immunity subtype. The genomic results also showed that the tumor mutation burden was higher in the high‐immunity subtype. Finally, Gene Set Enrichment Analysis showed that several immune‐related gene sets, including interleukin‐2/STAT5 signaling, inflammatory response, interleukin‐6/Janus kinase(JAK)/signal transducer and activator of transcription 3 (STAT3) signaling, interferon‐gamma response and allograft rejection, were elevated in the high‐immunity subtype. Finally, high‐immunity patients exhibited greater overall and disease‐specific survival outcome compared with low‐immunity patients (log rank P = 0.013 and P = 0.0097). Altogether, here we have identified 15 immune‐prognosis genes and a potential immune subtype for patients with LUAD, which may provide new insights into the prognosis and treatment of LUAD.