Differentiated regulation of immune-response related genes between LUAD and LUSC subtypes of lung cancers

Identification of a methyltransferase-related long noncoding RNA signature as a novel prognosis biomarker for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) accounts for a high proportion of tumor deaths globally, while methyltransferase-related lncRNAs in LUAD were poorly studied.

METHODS

In our study, we focused on two distinct cohorts, TCGA-LUAD and GSE3021, to establish a signature of methyltransferase-related long non-coding RNAs (MeRlncRNAs) in LUAD. We employed univariate Cox and LASSO regression analyses as our main analytical tools. The GSE30219 cohort served as the validation cohort for our findings. Furthermore, to explore the differential pathway enrichments between groups stratified by risk, we utilized Gene Set Enrichment Analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was conducted to assess the immune infiltration landscape within each sample. Reverse transcription quantitative PCR (RT-qPCR) was also performed to verify the expression of prognostic lncRNAs in both clinically normal and LUAD samples.

RESULTS

In LUAD, we identified a set of 32 MeRlncRNAs. We further narrowed our focus to six prognostic lncRNAs to develop gene signatures. The TCGA-LUAD cohort and GSE30219 were utilized to validate the risk score model derived from these signatures. Our analysis showed that the risk score served as an independent prognostic factor, linked to immune-related pathways. Additionally, the analysis of immune infiltration revealed that the immune landscape in high-risk groups was suppressed, which could contribute to poorer prognoses. We also constructed a regulatory network comprising 6 prognostic lncRNAs, 19 miRNAs, and 21 mRNAs. Confirmatory RT-qPCR results aligned with public database findings, verifying the expression of these prognostic lncRNAs in the samples.

CONCLUSION

The prognostic gene signature of LUAD associated with MeRlncRNAs that we provided, may offer us a comprehensive picture of the prognosis prediction for LUAD patients.

Data mining-based identification of epigenetic signatures with discrimination potential of lung adenocarcinoma and squamous cell carcinoma

BACKGROUND

Mounting evidence suggests that lung adenocarcinoma (LAC) and lung squamous cell carcinoma (LSC) have different biological behaviors and therapeutic regimens in clinical practice. However, limited improvements in molecular differential diagnosis of the two entities have been achieved in recent decades. We aimed to find novel markers that could define non-small cell lung cancer (NSCLC) subtypes.

METHODS

We first explored publically available databases to search for DNA methylation signatures that enable a precise discrimination of LAC and LSC. Next-generation sequencing (NGS) was then used to analyze the methylation status and sites of candidate genes in LAC/LSC tissue samples, and a quantitative methylation-sensitive PCR (qMS-PCR) assay was conducted to test the performance of the selected maker in tissue samples and bronchoalveolar lavage fluid (BALF) specimens.

RESULTS

We screened 19 top-ranked methylation loci that are differentially methylated between LAC and LSC. Among these hits, 6 methylation sites are enriched within the PREX1 gene promoter, thus becoming our focus. NGS analysis confirmed markedly higher PREX1 methylation levels in LAC than in LSC and revealed the right sites for detection of PREX1 methylation. Furthermore, PREX1 methylation analysis in lung cancer tissue samples defined 9 of 11 pathologically proven LACs, as well as 12 of 14 LSCs. In addition, ~ 80% LAC BALF samples showed methylated PREX1 compared to substantially lower test positivity (0-9%) of it in LSC and other lung conditions (P < 0.01).

CONCLUSION

Our pilot study identified a unique epigenetic signature that could effectively distinguish LAC from LSC in various lung samples. It may enhance our in-depth understanding of the biology of lung cancer and pave the way for better accurate diagnosis and treatment stratification in the future.

Integrative bioinformatics analysis reveals ECM and nicotine-related genes in both LUAD and LUSC, but different lung fibrosis-related genes are involved in LUAD and LUSC

There are several bioinformatics studies related to lung cancer, but most of them have mainly focused on either microarray data or RNA-Seq data alone. In this study, we have combined both types of data to identify differentially expressed genes (DEGs) specific to lung cancer subtypes. We obtained six microarray datasets from the GEO and also the expression matrix of LUSC and LUAD from TCGA, which were analyzed by GEO2R tool and GEPIA2, respectively. Enrichment analyses of DEGs were performed using the Enrichr database. Protein module identification was done by MCODE plugin in cytoscape software. We identified 30 LUAD-specific, 17 LUSC-specific, and 17 DEGs shared between LUAD and LUSC. Enrichment analyses revealed that LUSC-specific DEGs are involved in lung fibrosis. In addition, DEGs shared between LUAD and LUSC are involved in extracellular matrix (ECM), nicotine metabolism, and lung fibrosis. We identified lung fibrosis-related genes, including SPP1, MMP9, and CXCL2, involved in both LUAD and LUSC, but SERPINA1 and PLAU genes involved only in LUSC. We also found an important module separately for LUAD-specific, LUSC-specific, and shared DEGs between LUSC and LUAD. S100P, GOLM, AGR2, AK1, TMEM125, SLC2A1, COL1A1, and GHR genes were significantly associated with survival. Our findings suggest that different lung fibrosis-related genes may play roles in LUSC and LUAD. Additionally, nicotine metabolism and ECM remodeling were found to be associated with both LUSC and LUAD, regardless of subtype, emphasizing the role of smoking in the development of lung cancer and ECM in the high aggressiveness and mortality of lung cancer.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.

Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features

Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n). Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly connected important features by involving the known biological network when constructing the forest. The algorithm can identify effective features that form highly connected sub-graphs and achieve equivalent classification accuracy to RF. To evaluate the capability of our proposed method, we conducted simulation experiments and applied the method to two real datasets-non-small cell lung cancer RNA-seq data from The Cancer Genome Atlas, and human embryonic stem cell RNA-seq dataset (GSE93593). The resulting high classification accuracy, connectivity of selected sub-graphs, and interpretable feature selection results suggest the method is a helpful addition to graph-based classification models and feature selection procedures.

Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.

Penalized estimation of a class of single-index varying-coefficient models for integrative genomic analysis

Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.

Comprehensive analysis of the significance of METTL7A gene in the prognosis of lung adenocarcinoma

Background

The most common subtype of lung cancer, called lung adenocarcinoma (LUAD), is also the largest cause of cancer death in the world. The aim of this study was to determine the importance of the METTL7A gene in the prognosis of patients with LUAD.

Methods

This particular study used a total of four different LUAD datasets, namely TCGA-LUAD, GSE32863, GSE31210 and GSE13213. Using RT-qPCR, we were able to determine METTL7A expression levels in clinical samples. Univariate and multivariate Cox regression analyses were used to identify factors with independent effects on prognosis in patients with LUAD, and nomograms were designed to predict survival in these patients. Using gene set variation analysis (GSVA), we investigated differences in enriched pathways between METTL7A high and low expression groups. Microenvironmental cell population counter (MCP-counter) and single-sample gene set enrichment analysis (ssGSEA) methods were used to study immune infiltration in LUAD samples. Using the ESTIMATE technique, we were able to determine the immune score, stromal score, and estimated score for each LUAD patient. A competing endogenous RNA network, also known as ceRNA, was established with the help of the Cytoscape program.

Results

We detected that METTL7A was down-regulated in pan-cancer, including LUAD. The survival study indicates that METTL7A was a protective factor in the prognosis of LUAD. The univariate and multivariate Cox regression analyses revealed that METTL7A was a robust independent prognostic indicator in survival prediction. Through the use of GSVA, several immune-related pathways were shown to be enriched in both the high-expression and low-expression groups of METTL7A. Analysis of the tumor microenvironment revealed that the immune microenvironment of the group with low expression was suppressed, which may be connected to the poor prognosis. To explore the ceRNA regulatory mechanism of METTL7A, we finally constructed a regulatory network containing 1 mRNA, 2 miRNAs, and 5 long non-coding RNAs (lncRNAs).

Conclusion

In conclusion, we presented METTL7A as a potential and promising prognostic indicator of LUAD. This biomarker has the potential to offer us with a comprehensive perspective of the prediction of prognosis and treatment for LUAD patients.

In silico prediction of COVID-19 cytokine storm in lung cancer types

Lung cancer is one of the most frequently diagnosed malignant tumors and the leading cause of cancer-related death worldwide. Mainly, Non-small-cell lung cancer (NSCLC), which accounts for more than eighty-five percent of all lung cancers, consists of two major subtypes: lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Novel coronavirus disease (COVID-19) affected millions of people caused by acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) around the globe. Lung cancer patients and COVID-19 present unique and unfortunate lethal combinations because the lungs are the primary target organ of SARS-CoV-2 infection. Clinical studies have demonstrated that an over-activated inflammatory response associated with severe COVID-19 cases is characterized by excessive auto-amplifying cytokine release, which is defined as a “cytokine storm.” ACE2 and TMPRSS2 receptors play an essential role in SARS-CoV-2 infection; therefore, using in silico analysis, we did correlation analysis with immune infiltration markers in LUAD and LUSC patient groups. Our study identified a promising correlation between immune-modulators and receptor proteins (ACE-2 and TMPRSS2), creating a domain that requires further laboratory studies for clinical authentication.

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Correlation between immune-modulators and virus receptor proteins certainly using computational approach. Focusing on differential vulnerability and complications toward SARS-CoV-2 infection in lung cancer types.

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Immunomodulatory role of ACE2 and TMPRSS2 genes by analysing various cytokines.

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Potential immunomodulatory therapies to overpower the deregulated pro-inflammatory action in COVID-19.

An efficient five-lncRNA signature for lung adenocarcinoma prognosis, with AL606489.1 showing sexual dimorphism

Background: Lung adenocarcinoma (LUAD) is a sex-biased and easily metastatic malignant disease. A signature based on 5 long non-coding RNAs (lncRNAs) has been established to promote the overall survival (OS) prediction effect on LUAD.

Methods: The RNA expression profiles of LUAD patients were obtained from The Cancer Genome Atlas. OS-associated lncRNAs were identified based on the differential expression analysis between LUAD and normal samples followed by survival analysis, univariate and multivariate Cox proportional hazards regression analyses. OS-associated lncRNA with sex dimorphism was determined based on the analysis of expression between males and females. Functional enrichment analysis of the Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed to explore the possible mechanisms of 5-lncRNA signatures.

Results: A 5-lncRNA signature (composed of AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1) was found to be effective in predicting high-risk LUAD patients as well as applicable to female and male subgroups and &lt;65-year and ≥65-year age subgroups. The forecasted effect of the 5-lncRNA signature was more efficient and stable than the TNM stage and other clinical risk factors (such as sex and age). Functional enrichment analysis revealed that the mRNA co-expressed with these five OS-related lncRNAs was associated with RNA regulation within the nucleus. AL606489.1 demonstrated a sexual dimorphism that may be associated with microtubule activity.

Conclusion: Our 5-lncRNA signature could efficaciously predict the OS of LUAD patients. AL606489.1 demonstrated gender dimorphism, which provides a new direction for mechanistic studies on sexual dimorphism.

Network module function enrichment analysis of lung squamous cell carcinoma and lung adenocarcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small cell lung cancer that pose a serious threat to human health. However, both subtypes currently lack effective indicators for early diagnosis.

METHODS

To identify tumor-specific indicators and predict cancer-related signaling pathways, LUSC and LUAD gene weighted co-expression networks were constructed. Combined with clinical data, core genes in LUSC and LUAD modules were then screened using protein-protein interaction networks and their functions and pathways were analyzed. Finally, the effect of core genes on survival of LUSC and LUAD patients was evaluated.

RESULTS

We identified 12 network modules in LUSC and LUAD, respectively. LUSC modules "purple" and "green" and LUAD modules "brown" and "pink" are significantly associated with overall survival and clinical traits of tumor node metastasis, respectively. Eleven genes from LUSC and eight genes from LUAD were identified as candidate core genes, respectively. Survival analysis showed that high expression of SLIT3, ABI3BP, MYOCD, PGM5, TNXB, and DNAH9 are associated with decreased survival in LUSC patients. Furthermore, high expression of BUB1, BUB1B, TTK, and UBE2C are associated with lower patient survival.

CONCLUSIONS

We found biomarker genes and biological pathways for LUSC and LUAD. These network hub genes are associated with clinical characteristics and patient outcomes and they may play important roles in LUSC and LUAD.

Shared network pattern of lung squamous carcinoma and adenocarcinoma illuminates therapeutic targets for non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is a malignant tumor with high mortality. Lung squamous carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the common subtypes of NSCLC. However, how LUSC and LUAD are compatible remains to be elucidated.

Methods

We used a network approach to find highly interconnected genes shared with LUSC and LUAD, and we then built modules to assess the degree of preservation between them. To quantify this result, Z-scores were used to summarize the interrelationships between LUSC and LUAD. Furthermore, we correlated network hub genes with patient survival time to identify risk factors.

Results

Our findings provided a look at the regulatory pattern for LUSC and LUAD. For LUSC, several genes, such as AKR1C1, AKR1C2, and AKR1C3, play key roles in regulating network modules of cell growth pathways. In addition, CCL19, CCR7, CCL21, and LY9 are enriched in LUAD network modules of T lymphocyte-related pathways. LUSC and LUAD have similar expressed gene expression patterns. Their networks share 46 hub genes with connectivity greater than 0.9. These genes are correlated with patient survival time. Among them, the expression level of COL5A2 in LUSC and LUAD is higher than that in normal tissues, which is closely related to the poor prognosis of LUSC and LUAD patients.

Conclusion

LUSC and LUAD share a network pattern. COL5A2 may be a risk factor in poor prognosis in LUSC and LUAD. The common landscape of LUSC and LUAD will help better define the regulation of NSCLC candidate genes and achieve the goals of precision medicine.

Current treatments for non-small cell lung cancer

Despite improved methods of diagnosis and the development of different treatments, mortality from lung cancer remains surprisingly high. Non-small cell lung cancer (NSCLC) accounts for the large majority of lung cancer cases. Therefore, it is important to review current methods of diagnosis and treatments of NSCLC in the clinic and preclinic. In this review, we describe, as a guide for clinicians, current diagnostic methods and therapies (such as chemotherapy, chemoradiotherapy, targeted therapy, antiangiogenic therapy, immunotherapy, and combination therapy) for NSCLC.

PLK1 Is a Potential Prognostic Factor Associated with the Tumor Microenvironment in Lung Adenocarcinoma

More than 40% of lung cancers are lung adenocarcinoma (LUAD) worldwide. However, the prognosis of LUAD is poor for the lack of effective treatment methods. Our study identified PLK1 as a novel prognosis biomarker and treatment target for LUAD. Based on the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) from 551 LUAD cases were analyzed for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. To explore the biological pathways and the tumor-infiltrating immune cells (TICs) using gene set variation analysis (GSVA) and the CIBERSORT, as well as to analyze DEGs, a protein-protein interaction (PPI) network and Cox regression analysis were performed. Validation of DEGs was achieved through quantitative real-time PCR (qPCR) and immunoblotting. DEGs associated with the cell cycle were sorted out. Cell cycle scores were positively correlated with age, clinical stages, and metastasis and negatively correlated with overall survival of LUAD patients. PPI and Cox analyses showed that PLK1 could be a prognostic factor for LUAD patients. CIBERSORT analysis revealed a positive correlation between the transcription level of PLK1 and the function of CD8+ and activated memory CD4+ T cells, as well as a negative correlation with activated natural killer cells. Furthermore, PLK1 overexpression increased immune cytotoxicity, as measured by the cytolytic activity score, IFN- score, and IFN- level. There is a strong correlation between PLK1 and key features of TICs, indicating its potential as a promising prognostic biomarker for LUAD.

RAGE is a potential biomarker implicated in immune infiltrates and cellular senescence in lung adenocarcinoma

Background

Receptor for Advanced Glycation End‐products (RAGE) is an oncogene abnormally expressed in various cancers. However, the clinical value of RAGE and the biological role of RAGE in lung cancer have not been fully investigated.

Methods

We compared the RAGE expression using several public databases. The relationship between RAGE expression and clinicopathological variables was assessed. The R software package was used to carry out enrichment analyses of RAGE co‐expression and gene set enrichment analysis (GSEA). Additionally, we used the TIMER database to assess the association between immune infiltration and RAGE expression. The correlation between RAGE expression and senescence biomarkers in lung adenocarcinoma was analyzed using the TCGA database.

Results

Our findings indicated that the expression of RAGE was downregulated in lung adenocarcinoma, and down‐regulation of RAGE was related to poor overall survival and disease‐free survival. Functional enrichment analysis indicated that RAGE co‐expression genes were mainly associated with neutrophil activation involved in immune response, neutrophil degranulation, and regulation of leukocyte‐mediated immunity. Correlation analysis revealed that RAGE expression was closely related to the purity of the tumor and immune infiltration. GSEA indicated that the RAGE‐related differential genes were mainly enriched in senescence‐related pathways. Besides, the RAGE expression was significantly associated with senescence‐related genes.

Conclusion

Down‐regulation of RAGE expression was associated with poor prognosis, as well as defective immune infiltration and cellular senescence in lung adenocarcinoma.

Novel cancer subtyping method based on patient-specific gene regulatory network

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Alternative Energy: Breaking Down the Diverse Metabolic Features of Lung Cancers

Metabolic reprogramming is a hallmark of cancer initiation, progression, and relapse. From the initial observation that cancer cells preferentially ferment glucose to lactate, termed the Warburg effect, to emerging evidence indicating that metabolic heterogeneity and mitochondrial metabolism are also important for tumor growth, the complex mechanisms driving cancer metabolism remain vastly unknown. These unique shifts in metabolism must be further investigated in order to identify unique therapeutic targets for individuals afflicted by this aggressive disease. Although novel therapies have been developed to target metabolic vulnerabilities in a variety of cancer models, only limited efficacy has been achieved. In particular, lung cancer metabolism has remained relatively understudied and underutilized for the advancement of therapeutic strategies, however recent evidence suggests that lung cancers have unique metabolic preferences of their own. This review aims to provide an overview of essential metabolic mechanisms and potential therapeutic agents in order to increase evidence of targeted metabolic inhibition for the treatment of lung cancer, where novel therapeutics are desperately needed.

miRNA-218/FANCI is associated with metastasis and poor prognosis in lung adenocarcinoma: a bioinformatics analysis

Background

In this study, tumor microarray analysis was used to screen the key messenger RNAs (mRNAs) and microRNAs related to the progression of lung adenocarcinoma (LUAD), in order to provide a theoretical basis for early diagnosis, therapeutic targets, and prognosis evaluation of patients with LUAD.

Methods

The mRNA and miRNA expression datasets came from the Gene Expression Omnibus (GEO) project database. Differentially expressed genes (DEGs) and microRNAs (DEMs) between LUAD tissues and adjacent lung tissue were obtained using GEO2R. The Search Tool for the Retrieval of Interacting Genes website was also employed to construct and visualize the interactions of overlapped DEGs. The overall survival of DEMs was investigated using the Kaplan-Meier plotter. The TargetScan website (http://www.targetscan.org/) was used to verify the relationship between FA Complementation Group I (FANCI) and the expression of miRNA-218 (miR-218). The expression of FANCI was verified using the GEO and Human Protein Atlas databases, as well as Real Time Quantitative PCR using our own samples. Next, we analyzed the relationship between the expression of FANCI and the clinicopathological characteristics as well as the prognosis of patients with LUAD. We also explored whether the FANCI was related to immune cell infiltration in LUAD.

Results

FANCI was identified as a hub gene and associated with poor OS. We found that miR-218 negatively regulates FANCI mRNA expression. At the mRNA expression and protein level, FANCI was more highly expressed in LUAD tissues. The expression of FANCI in LUAD was related to tumor size (χ²=13.96, P<0.001), lymphatic metastasis (χ²=3.88, P<0.05), distant metastasis (χ²=45.39, P<0.001), and stage (χ²=11.03, P<0.05). In addition, the Cox regression model found that FANCI mRNA expression was an independent predictive factor of patient survival (P<0.05). FANCI expression was both weakly related to B cells and neutrophil infiltration in LUAD.

Conclusions

miR-218 may negatively regulate FANCI, and FANCI could promote metastasis via extracellular matrix (ECM) receptor interaction, leading to poor prognosis of LUAD. FANCI may be a key gene to the determine metastasis and poor prognosis in patients with LUAD. Changes in the immune microenvironment may be the mechanism through which FANCI leads to poor prognosis of LUAD.

Moving pan-cancer studies from basic research toward the clinic

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Identification of Prognostic miRNAs Associated With Immune Cell Tumor Infiltration Predictive of Clinical Outcomes in Patients With Non-Small Cell Lung Cancer

Background

A detailed means of prognostic stratification in patients with non-small cell lung cancer (NSCLC) is urgently needed to support individualized treatment plans. Recently, microRNAs (miRNAs) have been used as biomarkers due to their previously reported prognostic roles in cancer. This study aimed to construct an immune-related miRNA signature that effectively predicts NSCLC patient prognosis.

Methods

The miRNAs and mRNA expression and mutation data of NSCLC was obtained from The Cancer Genome Atlas (TCGA). Immune-associated miRNAs were identified using immune scores calculated by the ESTIMATE algorithm. LASSO-penalized multivariate survival models were using for development of a tumor immune-related miRNA signature (TIM-Sig), which was evaluated in several public cohorts from the Gene Expression Omnibus (GEO) and the CellMiner database. The miRTarBase was used for constructing the miRNA-target interactions.

Results

The TIM-Sig, including 10 immune-related miRNAs, was constructed and successfully predicted overall survival (OS) in the validation cohorts. TIM-Sig score negatively correlated with CD8+ T cell infiltration, IFN-γ expression, CYT activity, and tumor mutation burden. The correlation between TIM-Sig score and genomic mutation and cancer chemotherapeutics was also evaluated. A miRNA-target network of 10 miRNAs in TIM-Sig was constructed. Further analysis revealed that these target genes showed prognostic value in both lung squamous cell carcinoma and adenocarcinoma.

Conclusions

We concluded that the immune-related miRNAs demonstrated a potential value in clinical prognosis.

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

Hepcidin Upregulation in Lung Cancer: A Potential Therapeutic Target Associated With Immune Infiltration

Lung cancer has the highest death rate among cancers globally. Hepcidin is a fascinating regulator of iron metabolism; however, the prognostic value of hepcidin and its correlation with immune cell infiltration in lung cancer remain unclear. Here, we comprehensively clarified the prognostic value and potential function of hepcidin in lung cancer. Hepcidin expression was significantly increased in lung cancer. High hepcidin expression was associated with sex, age, metastasis, and pathological stage and significantly predicted an unfavorable prognosis in lung cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) results suggested that hepcidin is involved in the immune response. Furthermore, hepcidin expression was positively correlated with the infiltration levels of immune cells and the expression of diverse immune cell marker sets. Importantly, hepcidin may affect prognosis partially by regulating immune infiltration in lung cancer patients. Hepcidin may serve as a candidate prognostic biomarker for determining prognosis associated with immune infiltration in lung cancer.

Notch Signaling Pathway in Cancer-Review with Bioinformatic Analysis

Simple Summary

The Notch signaling pathway, which controls multiple cell differentiation processes during the embryonic stage and adult life, is associated with carcinogenesis and disease progression. The aim of the present study was to highlight cancer heterogeneity with respect to the Notch pathway. Our analysis concerns the effects of the Notch signaling at different levels, including core components and downstream target genes. We also demonstrate overall and disease-free survival results, pointing out the characteristics of particular Notch components. Depending on tissue context, Notch members can be either oncogenic or suppressive. We observed different expression profile core components and target genes that could be associated with distinct survival of patients. Advances in our understanding of the Notch signaling in cancer are very promising for the development of new treatment strategies for the benefit of patients.

Abstract

Notch signaling is an evolutionarily conserved pathway regulating normal embryonic development and homeostasis in a wide variety of tissues. It is also critically involved in carcinogenesis, as well as cancer progression. Activation of the Notch pathway members can be either oncogenic or suppressive, depending on tissue context. The present study is a comprehensive overview, extended with a bioinformatics analysis of TCGA cohorts, including breast, bladder, cervical, colon, kidney, lung, ovary, prostate and rectum carcinomas. We performed global expression profiling of the Notch pathway core components and downstream targets. For this purpose, we implemented the Uniform Manifold Approximation and Projection algorithm to reduce the dimensions. Furthermore, we determined the optimal cutpoint using Evaluate Cutpoint software to established disease-free and overall survival with respect to particular Notch members. Our results demonstrated separation between tumors and their corresponding normal tissue, as well as between tumors in general. The differentiation of the Notch pathway, at its various stages, in terms of expression and survival resulted in distinct profiles of biological processes such as proliferation, adhesion, apoptosis and epithelial to mesenchymal transition. In conclusion, whether oncogenic or suppressive, Notch signaling is proven to be associated with various types of malignancies, and thus may be of interest as a potential therapeutic target.

Isorhamnetin Enhances the Radiosensitivity of A549 Cells Through Interleukin-13 and the NF-κB Signaling Pathway

Isorhamnetin (ISO), a naturally occurring plant flavonoid, is widely used as a phytomedicine. The major treatment modality for non-small-cell lung carcinoma (NSCLC) is radiotherapy. However, radiotherapy can induce radioresistance in cancer cells, thereby resulting in a poor response rate. Our results demonstrated that pretreatment with ISO induced radiosensitizing effect in A549 cells using colony formation, micronucleus, and γH2AX foci assays. In addition, ISO pretreatment significantly enhanced the radiation-induced incidence of apoptosis, the collapse of mitochondrial membrane potential, and the expressions of proteins associated with cellular apoptosis and suppressed the upregulation of NF-κBp65 induced by irradiation in A549 cells. Interestingly, the expression of interleukin-13 (IL-13), an anti-inflammatory cytokine, was positively correlated with the ISO-mediated radiosensitization of A549 cells. The knockdown of IL-13 expression by RNA interference decreased the IL-13 level and thus reduced ISO-mediated radiosensitivity in cells. We also found that the IR-induced NF-κB signaling activation was inhibited by ISO pretreatment, and it was abrogated in IL-13 silenced cells. We speculated that ISO may confer radiosensitivity on A549 cells via increasing the expression of IL-13 and inhibiting the activation of NF-κB. To our knowledge, this is the first report demonstrating the effects of ISO treatment on the responsiveness of lung cancer cells to irradiation through IL-13 and the NF-κB signaling pathway. In summary, ISO is a naturally occurring radiosensitizer with a potential application in adjuvant radiotherapy.

Long non-coding RNA LINC01194 promotes the proliferation, migration and invasion of lung adenocarcinoma cells by targeting miR-641/SETD7 axis

Background

It is increasingly evidenced that long non-coding RNAs (lncRNAs) play an important role in various diseases. LncRNA LINC01194 acts as an oncogene in several cancer types. Nevertheless, the role of LINC01194 in lung adenocarcinoma (LUAD) has not yet been revealed.

Methods

qRT-PCR was used to detect the expression of LINC01194, miR-641 and SETD7 mRNA, while western blot was exploited to examine SETD7 protein level. Cell proliferation was detected by colony formation and EdU assays. Transwell assays detected cell migration and invasion. TUNEL assay and flow cytometry analysis were used to detect cell apoptosis. RIP, RNA pull down and luciferase reporter assays detected the binding among LINC01194, miR-641 and SETD7.

Results

LINC01194 was significantly upregulated in LUAD tissues and cell lines. Knockdown of LINC01194 resulted in decreased cell proliferation, migration and invasion, and increased apoptosis. Mechanistic experiments unveiled that LINC01194 augmented SETD7 expression in LUAD cells by competitively interacting with miR-641. Rescue experiments showed that miR-641 inhibition and SETD7 overexpression rescued the repressing impacts on LUAD cell proliferation, migration and invasion caused by LINC01194 knockdown.

Conclusion

LINC01194 promotes the progression of LUAD by enhancing miR-641-targeted SETD7. The LINC01194/miR-641/SETD7 axis might provide new molecular targets for treating LUAD.

Functional genomics of AP-2α and AP-2γ in cancers: in silico study

BACKGROUND

Among all causes of death, cancer is the most prevalent and is only outpaced by cardiovascular diseases. Molecular theory of carcinogenesis states that apoptosis and proliferation are regulated by groups of tumor suppressors or oncogenes. Transcription factors are example of proteins comprising representatives of both cancer-related groups. Exemplary family of transcription factors which exhibits dualism of function is Activating enhancer-binding Protein 2 (AP-2). Scientific reports concerning their function in carcinogenesis depend on particular family member and/or tumor type which proves the issue to be unsolved. Therefore, the present study examines role of the best-described AP-2 representatives, AP-2α and AP-2γ, through ontological analysis of their target genes and investigation what processes are differentially regulated in 21 cancers using samples deposited in Genomic Data Analysis Center (GDAC) Firehose.

METHODS

Expression data with clinical annotation was collected from TCGA-dedicated repository GDAC Firehose. Transcription factor targets were obtained from Gene Transcription Regulation Database (GTRD), TRANScription FACtor database (TRANSFAC) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Monocle3 R package was used for global samples profiling while Protein ANalysis THrough Evolutionary Relationships (PANTHER) tool was used to perform gene ontology analysis.

RESULTS

With RNA-seq data and Monocle3 or PANTHER tools we outlined differences in many processes and signaling pathways, separating tumor from normal tissues or tumors from each other. Unexpectedly, a number of alterations in basal-like breast cancer were identified that distinguished it from other subtypes, which could bring future clinical benefits.

CONCLUSIONS

Our findings indicate that while the AP-2α/γ role remains ambiguous, their activity is based on processes that underlie the cancer hallmarks and their expression could have potential in diagnosis of selected tumors.

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Background

Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs).

Methods

In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.

Findings

For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06–2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04–4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15–2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways.

Interpretation

CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

Funding

National Institue of Health and US Department of Defense.

A Systematic Analysis of Dysregulated Long Non-Coding RNAs/microRNAs/mRNAs in Lung Squamous Cell Carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) accounts up for approximately 30% of all lung cancers with a high mortality. The study was aimed at finding genes critical in the diagnosis and prognosis of LUSC.

MATERIALS AND METHODS

The differentially expressed (DE) genes (DEGs) and DE lncRNAs (DELs) from 501 LUSC and 49 normal lung tissues, and DE miRNAs (DEMs) from 478 LUSC and 45 normal lung tissues were respectively obtained via the TCGA database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and co-expression network analyses were performed. Survival analysis and receiver operating characteristic curve of hub mRNAs were also analyzed. Competitive endogenous RNA networks of lncRNAs, miRNAs and mRNAs were constructed.

RESULTS

A total of 5747 DEGs, 378 DEMs and 3141 DELs in LUSC were identified in LUSC. The DEGs including AUARK, CDK1, KIF11 and EXO1 were proven to be significant metastatic indicators in LUSC, and 2 DEGs were significantly associated with the survival in LUSC patients. Some genes might have connections with many other gene nodes through a co-expression network. Four lncRNAs, 2 mRNAs and 2 miRNAs were identified as the candidates for the competitive miRNA-mRNA-lncRNA network and might serve as prognostic markers in LUSC.

CONCLUSIONS

We identified the differentially expressed lncRNAs, miRNAs and mRNAs in LUSC, providing further insights into the molecular mechanism of LUSC tumorigenesis and the potential prognostic biomarkers or therapeutic targets for LUSC.

NCAPH is negatively associated with Mcl‑1 in non‑small cell lung cancer

Lung cancer has a high mortality rate worldwide. Non‑SMC condensin I complex subunit H (NCAPH) has been identified to be one of the regulatory subunits of the condensin I complex, which is essential for the correct packaging and segregation of chromosomes in eukaryotes. NCAPH is abnormally overexpressed in various types of cancer. A pro‑survival member of the Bcl‑2 family, myeloid cell leukemia sequence 1 (Mcl‑1) is also frequently overexpressed in multiple cancers and is associated with poorer clinical outcomes for patients. The association of NCAPH and Mcl‑1 proteins with the clinical and pathological features of non‑small cell lung cancer (NSCLC) remains to be elucidated. In the current study, the positive percentage of NCAPH in the non‑cancerous lung tissues was revealed to be higher compared with that in NSCLC. However, the positive percentage of Mcl‑1 in the non‑cancerous lung tissues was lower compared with NSCLC. In addition, NCAPH high‑expression patients had a higher overall survival rate compared with patients exhibiting low expression, whereas the Mcl‑1 high‑expression group had a lower survival rate. Pairwise association in 260 cases of NSCLC revealed that overexpression of the NCAPH protein was negatively associated with Mcl‑1 expression and vice versa. The results of multivariate Cox proportional hazard regression analysis also indicated that NCAPH and Mcl‑1 demonstrated potential as distinct prognostic factors that may be used in NSCLC. The expression of NCAPH and Mcl‑1 may be associated with, and act as distinct molecular marks for the prediction of a poor prognosis in patients with NSCLC.

Establishment of the prognostic index of lung squamous cell carcinoma based on immunogenomic landscape analysis

Background

The incidence of lung squamous cell carcinoma (LUSC) increased substantially in recent years. Systematical investigation of the immunogenomic pattern is critical to improve the prognosis of LUSC.

Methods

Based on the TCGA and GEO dataset, we integrated the immune-related genes (IRGs) expression profile and the overall survival (OS) of 502 patients with LUSC. The survival-related and differentially-expressed IRGs in LUSC patients were evaluated by univariate cox regression and LASSO regression analysis. By applying multivariate cox analysis, a new prognostic indicator based on IRGs was established. We also used CIBERSORT algorithms and TIMER database to analyze immune infiltration of LUSC. Both gene set enrichment analysis (GSEA) and principal component analysis (PCA) was carried out for functional annotation. With the assist of computational biology, we also investigated the latent properties and molecular mechanisms of these LUSC-specific IRGs. We analyzed the correlation between immune checkpoints and risk score.

Results

A novel prognostic model was established based on 11 IRGS, including CXCL5, MMP12, PLAU, ELN, JUN, RNASE7, JAG1, SPP1, AGTR2, FGFR4, and TNFRSF18. This model performed well in the prognostic forecast, and was also related to the infiltration of immune cells. Besides, the high-risk groups and the low-risk groups exhibited distinct layout modes in PCA analysis, and GSEA results showed that different immune status among these groups.

Conclusions

In summary, our researches screened out clinically significant IRGs and proved the significance of IRG-based, individualized immune-related biomarkers in monitoring, prognosis, and discern of LUSC.

lncRNA LEF1-AS1 Promotes Proliferation and Induces Apoptosis of Non-Small-Cell Lung Cancer Cells by Regulating miR-221/PTEN Signaling

Introduction

LEF1-AS1 is a characterized oncogenic lncRNA in oral cancer. Analysis of TCGA dataset revealed the upregulation of LEF1-AS1 in non-small-cell lung cancer (NSCLC). This study was therefore carried out to investigate the involvement of LEF1-AS1 in NSCLC.

Methods

A total of 62 NSCLC patients were included to collect paired cancer and non-tumor tissues. RT-qPCR was performed to measure levels of LEF1-AS1 and miR-221 expression. Transient transfections were performed to explore the interactions between LEF1-AS1, miR-221 and PTEN. Cell proliferation and apoptosis were analyzed by cell proliferation assay and cell apoptosis assay, respectively.

Results

We found that LEF1-AS1 was upregulated in NSCLC patients. In addition, expression of LEF1-AS1 was negatively correlated with the expression of PTEN but positively correlated with the expression of miR-221 in NSCLC tissue samples. In NSCLC cells, overexpression of LEF1-AS1 led to downregulated expression of PTEN but upregulated expression of miR-221, which can directly target PTEN. Overexpression of LEF1-AS1 and miR-221 promoted cancer cell proliferation and inhibited apoptosis. PTEN played an opposite role and reduced the effects of overexpressing LEF1-AS1 and miR-221.

Conclusion

LEF1-AS1 may promote the proliferation and induce apoptosis of NSCLC cells by regulating miR-221/PTEN signaling.

Leveraging methylation to identify the potential causal genes associated with survival in lung adenocarcinoma and lung squamous cell carcinoma

Understanding the different genetic landscape between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is important for understanding the underlying molecular mechanism, which may facilitate the development of effective and precise treatments. Although previous studies have identified a number of differentially expressed genes (DEGs) responsible for lung cancer, it is unknown which of these genes are causal. The present study integrated DNA methylation, RNA sequencing, clinical characteristics and survival outcomes of patients with LUAD and LUSC from The Cancer Genome Atlas. DEGs were first identified using edgeR by comparing tumor and normal tissue, and differentially methylated probes (DMPs) were assessed using ChAMP. Candidate genes for further time-to-event instrumental variable analysis were selected as the intersecting genes between DEGs and the genes including DMP CpG sites within the transcription start site (TSS1500), with DMPs in TSS1500 region being the instrumental variables. Extensive sensitivity analyses were conducted to assess the robustness of the results. The present study identified 906 DEGs for LUAD, among which 538 also had DMPs in the TSS1500 region. In addition, 1,543 DEGs were identified for LUSC, among which 1,053 also had DMPs in the TSS1500 region. Time-to-event instrumental variable analysis detected eight potential causal genes for LUAD survival, including aryl hydrocarbon receptor nuclear translocator like 2, semaphorin 3G, serum deprivation-response protein, chloride intracellular channel protein 5, LIM zinc finger domain containing 2, epithelial membrane protein 2, carbonic anhydrase 7 and LOC116437. The results also identified that phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2 may be a potential causal gene for LUSC. Therefore, the results of the present study suggested that there was molecular heterogeneity between these two lung cancer subtypes. Such analysis framework can be extended to other cancer genomics research.

Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) is a frequently diagnosed histologic subtype with increasing morbidity and mortality. RalGDS-Like 4 (RGL4) has not been reported to be associated with cancer risk, prognosis, immunotherapy or any other treatments. We perform a bioinformatics analysis on data downloaded from the Cancer Genome Atlas (TCGA)-LUAD, and we find that low expression of RGL4 is accompanied by worse outcomes and prognosis in LUAD patients. As a promising predictor, the potential influence and mechanisms of RGL4 on overall survival are worth exploring. Moreover, RGL4 expression is significantly associated with a variety of tumor-infiltrating immune cells (TIICs), particularly memory B cells, CD8⁺T cells and neutrophils. Subsequently, we evaluated the most notable KEGG pathways, including glycolysis gluconeogenesis, the P53 signaling pathway, RNA degradation, and the B cell receptor signaling pathway, among others. Our findings provide evidence that the decreased expression of RGL4 is significantly associated with poor prognosis and immune cell infiltration in patients with LUAD and highlight the use of RGL4 as a novel predictive biomarker for the prognosis of LUAD and other cancers. RGL4 may also be used in combination with immune checkpoints to identify the benefits of immunotherapy. Subjects: Bioinformatics, Genomics, Oncology, Thoracic surgery.

Role of downregulated ADARB1 in lung squamous cell carcinoma

Non-small cell lung cancer (NSCLC) is prevalent worldwide. Lung squamous cell carcinoma (LUSC) is one of the main subtypes of NSCLC yet, currently, few biomarkers are available for the diagnosis of LUSC. The present study aimed to investigate the expression and role of adenosine deaminase RNA specific B1 (ADARB1) in lung squamous cell carcinoma (LUSC). Integrative bioinformatics analysis was used to identify the effects of ADARB1 expression on the occurrence and prognosis of LUSC. The expression of ADARB1 was further examined by immunohistochemistry (IHC). Bioinformatics analysis suggested that ADARB1 was downregulated in LUSC, serving as a potential tumor suppressor, and these results were verified by IHC performed on a lung cancer tissue array. Clinical studies suggested that ADARB1 expression and methylation levels were significantly associated with patient characteristics in LUSC. Moreover, ADARB1 global methylation levels were upregulated in LUSC tissues compared with normal lung tissues. Higher methylation levels of cg24063645 were associated with shorter overall survival time of patients with LUSC. A negative correlation was identified between ADARB1 and epidermal growth factor receptor (EGFR) expression in LUSC. Using the Gene Expression Omnibus database, it was suggested that the expression of ADARB1 in LUSC was significantly different compared with that in lung adenocarcinoma. Furthermore, protein-protein interactions were studied and a biological process annotation analysis was conducted. The present study suggested that ADARB1 was downregulated in LUSC; therefore, ADARB1 may serve as a specific biomarker and a potential therapeutic target for LUSC.

Long Noncoding MAGI2-AS3 Suppresses Several Cellular Processes of Lung Squamous Cell Carcinoma Cells by Regulating miR-374a/b-5p/CADM2 Axis

Background

Lung squamous cell carcinoma (LUSC) accounts for approximately 30% of all lung cancers that possesses the highest occurrence and mortality in all cancer types. Long noncoding RNAs have been reported to modulate tumor development for several decades.

Aim of the Study

This research aims to investigate the role of MAGI2-AS3 in LUSC.

Methods

RT-qPCR tested genes (including MAGI2-AS3, miR-374a/b-5p and CADM2) expression. Cell proliferation was detected by colony formation and EdU assays. Cell migration and invasion were evaluated by transwell assay. Flow cytometry analysis of apoptotic cells and Western blot analysis on apoptosis-related genes were applied to measure cell apoptosis. Nuclear-cytoplasmic fractionation and FISH assay positioned MAGI2-AS3. The combination between miR-374a/b-5p and MAGI2-AS3 (or CADM2) was determined by luciferase reporter assay and RIP assay.

Results

MAGI2-AS3 inhibited the proliferative, migratory and invasive capability of LUSC cells with upregulated expression. Additionally, MAGI2-AS3 overexpression promoted cell apoptosis. We discovered that MAGI2-AS3 was located in the cytoplasm. Hereafter, we found out that MAGI2-AS3 targeted miR-374a/b-5p. CADM2 was targeted by miR-374a/b-5p. Finally, rescue assays indicated that the promoting effects of miR-374a/b-5p amplification on biological activities were restored by CADM2 addition.

Conclusion

In conclusion, lncRNA MAGI2-AS3 suppressed LUSC by regulating miR-374a/b-5p/CADM2 axis, which might potentially serve as a therapeutic marker for LUSC patients.

FANCI Cooperates with IMPDH2 to Promote Lung Adenocarcinoma Tumor Growth via a MEK/ERK/MMPs Pathway

Purpose

Fanconi anemia complementation group I (FANCI) is a key protein in ribosome biogenesis and DNA repair. Here, we aimed to determine the clinical significance, prognostic value and biology functions of FANCI in lung adenocarcinoma (LUAD).

Methods

The expression of FANCI in LUAD tissue and its relationship with patient outcomes were assessed using bioinformatics analysis, as well as quantitative reverse-transcription PCR (qRT-PCR) and Western blot analysis of LUAD tissue and adjacent normal lung tissue. The chi-squared test and Cox regression analysis were used to analyze the clinical significance of FANCI expression. The biological effects of FANCI knockdown in human LUAD cell lines were investigated by analysis of proliferation, colony formation, cell cycle distribution, migration, and invasion in vitro, and monitoring of tumor xenograft growth in vivo. FANCI interactions with IMPDH2 and involvement in MEK/ERK/MMPs signaling were analyzed using co-immunoprecipitation assays, immunofluorescence microscopy, and Western blotting.

Results

FANCI was identified as a hub gene for LUAD. FANCI expression was upregulated in LUAD tissues compared with normal lung tissues and was positively associated with lymphatic metastasis, distant metastasis, and poor outcome. FANCI was also an independent prognostic factor in LUAD patients. Knockdown of FANCI in LUAD cell lines decreased their proliferation, migration, invasion, and cell cycle progression in vitro, and decreased the growth of xenografts in mice. Direct binding of FANCI to IMPDH2 decreased IMPDH2 degradation, regulated activation of MEK/ERK/MMPs signaling. Overexpression of IMPDH2 reversed the inhibitory effects of FANCI knockdown.

Conclusion

FANCI may act as an oncogene in LUAD by cooperating with IMPDH2 to promote cell proliferation via the MEK/ERK/MMPs pathway. These results identify FANCI as a potential prognostic biomarker and therapeutic target for LUAD.

Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

Background

Lung cancer is one of the most malignant tumors, causing over 1,000,000 deaths each year worldwide. Deep learning has brought success in many domains in recent years. DNA methylation, an epigenetic factor, is used for model training in many studies. There is an opportunity for deep learning methods to analyze the lung cancer epigenetic data to determine their subtypes for appropriate treatment.

Results

Here, we employ variational autoencoders (VAEs), an unsupervised deep learning framework, on 450K DNA methylation data of TCGA-LUAD and TCGA-LUSC to learn latent representations of the DNA methylation landscape. We extract a biologically relevant latent space of LUAD and LUSC samples. It is showed that the bivariate classifiers on the further compressed latent features could classify the subtypes accurately. Through clustering of methylation-based latent space features, we demonstrate that the VAEs can capture differential methylation patterns about subtypes of lung cancer.

Conclusions

VAEs can distinguish the original subtypes from manually mixed methylation data frame with the encoded features of latent space. Further applications about VAEs should focus on fine-grained subtypes identification for precision medicine.

Upregulation of OTUD7B (Cezanne) Promotes Tumor Progression via AKT/VEGF Pathway in Lung Squamous Carcinoma and Adenocarcinoma

OTUD7B, a multifunctional deubiquitinylase, plays an essential role in inflammation and proliferation signals. However, its function in lung cancer remains largely unknown. The aim of this study was to evaluate the prognostic significance of OTUD7B in patients with lung adenocarcinoma and squamous carcinoma and to characterize its molecular mechanisms in lung cancer progression and metastasis. Two tissue microarrays containing 150 pairs of lung squamous carcinoma and matched adjacent non-cancer tissues, and one tissue microarray containing 75 pairs of lung adenocarcinoma and adjacent non-cancer tissues were included, and immunohistochemical staining was performed to assess the clinical relevance of OTUD7B in non-small cell lung cancer. OTUD7B is highly expressed in both lung squamous carcinoma and adenocarcinoma and correlates with a worse prognosis. MTT proliferation, colony formation, migration and invasion assays and immunoblotting assay in NCI-H358 and A549 cell lines suggested that OTUD7B enhances EGF-induced Akt signal transduction and promotes lung cancer cell proliferation and migration. Immunohistochemical staining of large-scale lung cancer subjects (171 cases) revealed positive correlation of OTUD7B and VEGF expression. ELISA and tube formation assay revealed OTUD7B promotes VEGF production and angiogenesis. NCI-H358 tumor model demonstrated OTUD7B is required for lung tumor progression by facilitating activation of Akt signaling. These findings collectively identified OTUD7B as an independent predictive factor for the prognosis of non-small cell lung cancer and revealed OTUD7B promotes lung cancer cell proliferation and metastasis via Akt/VEGF signal pathway.

SPOP regulates the DNA damage response and lung adenocarcinoma cell response to radiation

Speckle-type POZ protein (SPOP) plays an important role in maintaining genome stability. Disability or mutation of the SPOP gene has been reported to contribute to prostate cancer incidence and prognosis. However, the functions of SPOP in lung cancer remain poorly understood, especially in lung adenocarcinoma (LUAD). Here, we found that SPOP affects the LUAD cell response to radiation by regulating the DNA damage response (DDR) pathway. SPOP is widely expressed in lung cancer cell lines, and SPOP protein levels are upregulated when cells experience DNA damage. SPOP knockdown affects DDR repair kinetics, apoptosis and cell cycle checkpoints that are induced by IR (ionizing radiation). Furthermore, we found that SPOP positively regulates the expression of DDR factors Rad51 and Ku80. Taken together, these data indicate the essential roles of SPOP in the DDR signaling pathways and LUAD cell response to radiation.

Systematic Analysis of Gene Expression in Lung Adenocarcinoma and Squamous Cell Carcinoma with a Case Study of FAM83A and FAM83B

Introduction: In our previous study, we constructed a Lung Cancer Explorer (LCE) database housing lung cancer-specific expression data and clinical data from over 6700 patients in 56 studies. Methods: Using this dataset of the largest collection of lung cancer gene expression along with our meta-analysis method, we systematically interrogated the association between gene expression and overall survival as well as the expression difference between tumor and normal (adjacent non-malignant tissue) samples in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQCC). A case study for FAM83A and FAM83B was performed as a demonstration for hypothesis testing with our database. Results: We showed that the reproducibility of results across studies varied by histological subtype and analysis type. Genes and pathways unique or common to the two histological subtypes were identified and the results were integrated into LCE to facilitate user exploration. In our case study, we verified the findings from a previous study on FAM83A and FAM83B in non-small cell lung cancer. Conclusions: This study used gene expression data from a large cohort of patients to explore the molecular differences between lung ADC and SQCC.

Clinical significance of miRNA‑1 and its potential target gene network in lung squamous cell carcinoma

Previous studies demonstrated that miRNA-1 (miR-1) is downregulated in certain human cancer and serves a crucial role in the progression of cancer. However, there are only a few previous studies examining the association between miR-1 and lung squamous cell carcinoma (LUSC) and the regulatory mechanism of miR-1 in LUSC remains unclear. Therefore, the present study investigated the clinical significance and determined the potential molecular mechanism of miR-1 in LUSC. The expression of miR-1 and its clinical significance in LUSC was examined by conducting a meta-analysis of 12 studies using Stata 14, MetaDiSc1.4 and SPSS version 23. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the potential target genes of miR-1 gathered from Gene Expression Omnibus and ArrayExpress. Meta-analysis demonstrated that miR-1 was significantly downregulated in LUSC [standardized mean difference: −1.44; 95% confidence interval (CI): −2.08, −0.81], and the area under the curve was 0.9096 (Q*=0.8416) with sensitivity of 0.71 (95% CI: 0.66, 0.76) and specificity of 0.88 (95% CI: 0.86, 0.90). The pooled positive likelihood ratio and negative likelihood ratio were 4.93 (95% CI: 2.54, 9.55) and 0.24 (95% CI: 0.10, 0.54), respectively. Bioinformatics analysis demonstrated that miR-1 may be involved in the progression of LUSC via the ‘cell cycle’, ‘p53 signaling pathway’, ‘Fanconi anemia pathway’, ‘homologous recombination’, ‘glycine, serine and threonine metabolism’ and ‘oocyte meiosis’. In summary, miR-1 was significantly downregulated in LUSC, suggesting a novel and promising non-invasive biomarker for diagnosing LUSC, and miR-1 was involved in LUSC progression via a number of significant pathways.

An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma

The tumor microenvironment consists of an intricately organized system through which immune cells and cancer cells may communicate to regulate anti-tumor immunogenicity. To this end, non-small cell lung cancer (NSCLC) has been shown to activate a variety of immunological mechanisms, thereby broadening our understanding of lung cancer immunobiology. However, while recent work has highlighted the importance of NSCLC immunology and prognosis, studies have not yet examined the tumor microenvironment (TME) globally in regards to the survival outcomes between two major NSCLC subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In the present study, we identify an immunogenic tumor microenvironment state in NSCLC that is enriched for the lung adenocarcinoma subtype. By utilizing TME cell enrichment scores and RNA-seq expression data, we show that the inflamed TME is associated with favorable patient survival in lung adenocarcinoma, but this does not hold true for lung squamous cell carcinoma. Moreover, differentially regulated pathways between immune-inflamed and immune-excluded tumors within LUAD and LUSC were not subtype specific. Instead, immune-inflamed LUSC samples possessed elevated immune checkpoint marker expression when compared to those of the LUAD samples, thereby offering a putative explanation for our prognostic observations. These results shed light on the immunological prognostic effects within lung cancer and may encourage further TME exploration between these two subtypes as the landscape of NSCLC therapy progresses.

Rab11-FIP2 suppressed tumor growth via regulation of PGK1 ubiquitination in non-small cell lung cancer

Mounting evidence has shown that the Rab11-FIP2 has critical roles in cancer cell growth. However, the clinical significance of Rab11-FIP2 in Non-small cell lung cancer (NSCLC) remains to be fully elucidated. In this study, we investigated the expression of Rab11-FIP2 using immunohistochemistry in 150 patients with NSCLC. We found that its expression level in NSCLC was much lower than that in the corresponding adjacent normal tissues. The DNA methylation data revealed that Rab11-FIP2 were significantly hypermethylated in NSCLC. The methylation level in the gene body was negatively correlated with the expression level of Rab11-FIP2 in NSCLC. Furthermore, enforced expression of Rab11-FIP2 dramatically reduced cancer cell proliferation and tumorigenesis, indicating a tumor suppressor role of PGK1 in NSCLC progression. Mechanistic investigations showed that Rab11-FIP2 interacted with the glycolytic kinase PGK1 and promoted its ubiquitination in NSCLC cells, leading to inactivation of the oncogenic AKT/mTOR signaling pathway. Overall, our data indicate that reduced expression of Rab11-FIP2 by DNA hypermethylation plays an important role in NSCLC tumor growth.

Comprehensive analysis of the tumor immune micro-environment in non-small cell lung cancer for efficacy of checkpoint inhibitor

Characterizing the molecular immune subtype and micro-environment of lung cancer is necessary to understand immunogenic interactions between infiltrating immune and stromal cells, and how tumor cells overcome immune checkpoint blockades. This study seeks to identify computational methodologies for subtyping gene expression-based tumor-immune micro-environment interactions, which differentiate non-small cell lung cancer (NSCLC) into immune-defective and immune-competent subtypes. Here, 101 lung squamous cell carcinomas (LUSCs) and 87 lung adenocarcinomas (LUADs) tumor samples have been analyzed. Several micro-environmental factors differentially induce LUAD or LUSC immune subtypes, as well as immune checkpoint expression. In particular, tumor-associated macrophages (TAMs) are key immune cells play a vital role in inflammation and cancer micro-environments of LUSCs; whereas, regulatory B cells are immunosuppressive and tumorigenic in LUADs. Additionally, cytolytic activity upon CD8⁺ T cell activation is decreased by the abundance of B cells and macrophages in immune-competent subtypes. Therefore, identifying immune subtypes in lung cancer and their impact on tumor micro-environment will lead to clinical tools for assessing LUADs and LUSCs in patients, as well as maximize the efficacy of immune checkpoint inhibitors.

Dissecting the expression landscape of mitochondrial genes in lung squamous cell carcinoma and lung adenocarcinoma

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of lung cancer. To explore mitochondrial respiratory gene expression profiles in LUSC and LUAD, RNA sequencing data from The Cancer Genome Atlas was used for comprehensive analyses to establish the molecular characteristics of LUSC and LUAD. To elucidate expression profiles, subtypes were defined using unsupervised clustering of mitochondrial gene expression data. Differences in nuclear gene expression levels, signaling pathways and tumor microenvironments between subtypes were investigated. The analysis revealed that mitochondrial respiratory genes were generally expressed at lower levels in tumor tissues compared with matched control tissues. The expression of mitochondrially encoded NADH dehydrogenase 5 or 6 was associated with tumor progression in LUAD and LUSC. Patients were clustered into three subgroups based on the expression profile of 13 mitochondrial protein-encoding genes, and patients in Cluster 3 exhibited poor survival rates compared with patients from Cluster 1. Furthermore, this association was also observed in another independent data set. Further analyses of the expression of nuclear-encoded genes in the three clusters revealed the enrichment of several cancer-associated signaling pathways in Cluster 3, particularly the apoptotic signaling pathway, suggesting a potential association between the decreased expression of mitochondrial DNA genes and increased tumor aggressiveness. Furthermore, the analyses of immune cell compositions in the tumor microenvironment detected a significant increase in the proportion of CD4+ T cells and a decrease in the proportion of macrophages in LUAD compared with LUSC (P=0.0000104 and P=0.0000105, respectively). In conclusion, the present study revealed an association between the expression patterns of mitochondrial-encoded genes and lung cancer, which may contribute to novel therapeutic strategies for patients with LUSC and LUAD.

Transcription Factors Contribute to Differential Expression in Cellular Pathways in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancers are broadly classified into small cell lung cancers and non-small cell lung cancers (NSCLC). Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are two common subtypes of NSCLC, and despite the fact that both occur in lung tissues, these two subtypes show a number of different pathological characteristics. To investigate the differences and seek potential therapy targets, we used bioinformatics methods to analyze RNA-Seq data from different aspects. The previous studies and comparative pathway enrichment analysis on publicly available data showed that expressed or inhibited genes are different in two cancer subtypes through important pathways. Some of these genes could not only affect cell function through expression, but also could regulate other genes' expression by binding to a specific DNA sequence. This kind of genes is called transcription factor (TF) or sequence-specific DNA-binding factor. Transcription factors play important roles in controlling gene expression in carcinoma pathways. Our results revealed transcription factors that may cause differential expression of genes in cellular pathways of LUAD and LUSC, which provide new clues for study and treatment. Once such TF is NFE2l2 which may regulate genes in the Wnt signaling pathway, and the MAPK signaling pathway, thus leading to an increase the cell growth, cell division, and gene transcription. Another TF-XBP1 has high correlation with genes related to cell adhesion molecules and cytokine-cytokine receptor interaction pathways that may further affect the immune system. Moreover, the two TF and high correlated genes also show similar patterns in an independent GEO data set.

SPP1 and AGER as potential prognostic biomarkers for lung adenocarcinoma

Overdue treatment and prognostic evaluation lead to low survival rates in patients with lung adenocarcinoma (LUAD). To date, effective biomarkers for prognosis are still required. The aim of the present study was to screen differentially expressed genes (DEGs) as biomarkers for prognostic evaluation of LUAD. DEGs in tumor and normal samples were identified and analyzed for Kyoto Encyclopedia of Genes and Genomes/Gene Ontology functional enrichments. The common genes that are up and downregulated were selected for prognostic analysis using RNAseq data in The Cancer Genome Atlas. Differential expression analysis was performed with 164 samples in GSE10072 and GSE7670 datasets. A total of 484 DEGs that were present in GSE10072 and GSE7670 datasets were screened, including secreted phosphoprotein 1 (SPP1) that was highly expressed and DEGs ficolin 3, advanced glycosylation end-product specific receptor (AGER), transmembrane protein 100 that were lowly expressed in tumor tissues. These four key genes were subsequently verified using an independent dataset, GSE19804. The gene expression model was consistent with GSE10072 and GSE7670 datasets. The dysregulation of highly expressed SPP1 and lowly expressed AGER significantly reduced the median survival time of patients with LUAD. These findings suggest that SPP1 and AGER are risk factors for LUAD, and these two genes may be utilized in the prognostic evaluation of patients with LUAD. Additionally, the key genes and functional enrichments may provide a reference for investigating the molecular expression mechanisms underlying LUAD.