Molecular subtypes based on DNA methylation predict prognosis in lung squamous cell carcinoma

Wnt/β-catenin signaling in the development and therapeutic resistance of non-small cell lung cancer

Wnt/β-catenin signaling is a critical pathway that influences development and therapeutic response of non-small cell lung cancer (NSCLC). In recent years, many Wnt regulators, including proteins, miRNAs, lncRNAs, and circRNAs, have been found to promote or inhibit signaling by acting on Wnt proteins, receptors, signal transducers and transcriptional effectors. The identification of these regulators and their underlying molecular mechanisms provides important implications for how to target this pathway therapeutically. In this review, we summarize recent studies of Wnt regulators in the development and therapeutic response of NSCLC.

[Exploring the Role of PCDHGB4 in the Occurrence of Lung Squamous Cell Carcinoma Based on Bioinformatics Analysis]

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a subtypes of non-small cell lung cancer (NSCLC). It has been reported that members of the protocadherin γ family can regulate tumor cell growth by inhibiting the Wnt signaling pathway. Protocadherin-gamma subfamily B4 (PCDHGB4) as a family member in LUSC was rarely reported. The aim of this study was to investigate the role and potential prognostic value of PCDHGB4 in the development of LUSC using bioinformatics methods.

METHODS

The Cancer Genome Atlas (TCGA), cBioPortal and UALCAN databases were used to analyze the expression, prognosis, clinicopathological features, immune cell infiltration, immune regulatory genes, immune checkpoint inhibitors (ICIs), and methyltransferases of PCDHGB4 in LUSC. At the single cell level, we analyzed the clustering results of cell subtypes and the expression of PCDHGB4 in different immune cell subpopulations. In addition, we compared the promoter methylation levels of PCDHGB4 in LUSC tissues and normal tissues and performed protein-protein interaction and mutation analysis. Finally, enrichment analysis was performed based on the differentially expressed genes.

RESULTS

Bioinformatics analysis results showed that the expression level of PCDHGB4 in LUSC tissues was lower than that in normal tissues. Survival analysis showed that increased PCDHGB4 expression was associated with poor prognosis. Single-cell sequencing analysis showed that PCDHGB4 was expressed in T cells, monocytes or macrophages, and dendritic cells. It was further found that PCDHGB4 played an important role in tumor immunity and confirmed that PCDHGB4 was associated with immune checkpoints, immune regulatory genes, and methyltransferases. Besides, enrichment analysis revealed that PCDHGB4 was involved in multiple cancer-related pathways.

CONCLUSIONS

The expression of PCDHGB4 was low in LUSC. PCDHGB4 was related to the poor prognosis of patients, and PCDHGB4 was closely related to the infiltration and pathway of tumor immune cells. PCDHGB4 may be a potential prognostic marker and a new target for immunotherapy in LUSC.

Pulmonary squamous cell carcinoma and lymphoepithelial carcinoma - morphology, molecular characteristics and differential diagnosis

Squamous cell carcinoma (SCC) comprises one of the major groups of non-small-cell carcinoma of the lung, and is subtyped into keratinising, non-keratinising and basaloid SCC. SCC can readily be diagnosed using histomorphology alone in keratinising SCC. Confirmatory immunohistochemical analyses should always be applied in non-keratinising and basaloid tumours to exclude differential diagnoses, most prominently adenocarcinoma and high-grade neuroendocrine carcinoma, which may have important therapeutic consequences. According to the World Health Organisation (WHO) classification 2015, the diagnosis of SCC can be rendered in resections of morphologically ambiguous tumours with squamous immunophenotype. In biopsies and cytology preparations in the same setting the current guidelines propose a diagnosis of 'non-small-cell carcinoma, favour SCC' in TTF1-negative and p40-positive tumours to acknowledge a possible sampling bias and restrict extended immunohistochemical evaluation in order to preserve tissue for molecular testing. Most SCC feature a molecular 'tobacco-smoke signature' with enrichment in GG > TT mutations, in line with the strong epidemiological association of SCC with smoking. Targetable mutations are extremely rare but they do occur, in particular in younger and non- or light-smoking patients, warranting molecular investigations. Lymphoepithelial carcinoma (LEC) is a poorly differentiated SCC with a syncytial growth pattern and a usually prominent lymphoplasmacytic infiltrate and frequent Epstein-Barr virus (EBV) association. In this review, we describe the morphological and molecular characteristics of SCC and LEC and discuss the most pertinent differential diagnoses.

Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma

Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Wnt signaling pathway-derived score for predicting therapeutic resistance and tumor microenvironment in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Due to tumor heterogeneity, understanding the pathological mechanism of tumor progression helps to improve the diagnosis process and clinical treatment strategies of LUAD patients. Methods: The transcriptome pattern, mutant expression and complete clinical information were obtained from the cancer genome atlas (TCGA) database and microarray data from gene expression omnibus (GEO) database. Firstly, we used single sample Gene Set Enrichment Analysis (ssGSEA) to estimate the activation of Wnt signaling pathway in each sample. Consensus clustering algorithm was used to classify LUAD samples into different subgroups according to the transcription patterns of 152 Wnt signaling pathway related genes. Then, ESTIMATE, ssGSEA and Gene Set Variation Analysis (GSVA) algorithms were used to assess the biological pathways and immunocytes infiltration between different subtypes. LASSO-COX algorithm was conducted to construct prognostic model. Kaplan-Meier and multivariate Cox analysis were performed to evaluate the predictive performance of risk model. Gene features were further confirmed using external datasets. Finally, we conducted vitro assay for validating hub gene (LEF1). Results: Based on the transcription patterns of 152 Wnt signaling pathway related genes, four different subtypes of LUAD patients were screened out by consensus clustering algorithm. Subsequently, it was found that patients with cluster A and B had massive immunocytes infiltration, and the survival rate of patients with cluster B was better than that of other subgroups. According to the coefficients in the LASSO- Cox model and the transcriptome patterns of these 18 genes, the risk score was constructed for each sample. The degree of malignancy of LUAD patients with high-risk subgroup was remarkable higher than that of patients with low-risk subgroup (p < 0.001). Subsequently, five top prognostic genes (AXIN1, CTNNB1, LEF1, FZD2, FZD4.) were screened, and their expression values were different between cancer and normal tissues. FZD2 and LEF1 were negatively related to ImmunoScore, and AXIN1 was negatively related to ImmunoScore. The significant correlation between LUAD patient risk score and overall survival (OS) was verified in external datasets. In the A549 cell line, knockdown of LEF1 can reduce the invasive and proliferation ability of LUAD cells. Conclusion: A innovative 18 genes predictive feature based on transcriptome pattern was found in patients with lung adenocarcinoma. These investigations further promote the insight of the prognosis of lung adenocarcinoma and may contribute to disease management at risk stratification.

DNA methylation profiling of meningiomas highlights clinically distinct molecular subgroups

BACKGROUND

Introduction of the classification of brain tumours based on DNA methylation profile has significantly changed the diagnostic approach. Due to the paucity of data on the molecular profiling of meningiomas and their clinical implications, no effective therapies and new treatments have been implemented.

METHODS

DNA methylation profiling, copy number analysis, targeted sequencing and H3K27me3 expression was performed on 35 meningiomas and 5 controls.

RESULTS

Unsupervised hierarchical clustering (UHC) analysis revealed four distinct molecular subgroups: Malignant; Intermediate; Benign A, and Benign B. Molecular heterogeneity was observed within the same grade as the Intermediate, Benign A, and Benign B subgroups were composed of WHO grade 1 as well as grade 2 cases. There was association of mutations with distinct methylation subgroups (NF2, AKT1, SMO, TRAF7 and pTERT). Loss of chromosome 22q was observed across all subgroups. 1p/14q co-deletion was seen in 50% of malignant and intermediate while CDKN2A loss was predominantly observed in malignant subgroup (50%). Majority of malignant (75%) and a small proportion of other subgroups (Intermediate: 25%, Benign A: 38.5%, and Benign B: 20%) harboured H3K27me3 loss. 38,734 genes were dysregulated amongst the four subgroups. DKFZ classified 71% cases with acceptable score. On survival analysis, methylation profiling had significant impact on progression-free-survival in WHO grade1 and 2 meningiomas (p = 0.0051).

CONCLUSION

Genome-wide DNA methylation profiling highlights clinically distinct molecular subgroups and heterogeneity within the same grade of meningiomas. Molecular profiling can usher in a paradigm shift in meningioma classification, prognostic prediction, and treatment strategy.

DNMT3b-mediated SPAG6 promoter hypermethylation affects lung squamous cell carcinoma development through the JAK/STAT pathway

BACKGROUND

DNA methylation controls the transcription of genes and is involved in the development of lung cancer. Our preliminary bioinformatics prediction revealed that sperm associated antigen 6 (SPAG6) was considerably hypermethylated in lung squamous cell carcinoma (LUSC). Thus, this study aimed to probe the mechanism underlying its hypermethylation.

METHODS

The effect of DNA methylation of SPAG6 on its expression in LUSC was analyzed. The contributors to SPAG6 DNA hypermethylation were sought. CCK-8, EdU, and Transwell assays were carried out to assess the malignant phenotype of LUSC cells. KEGG pathway enrichment analysis was used to screen for pathways affected by SPAG6, which were confirmed by dual-luciferase assays. Bioinformatics analysis was conducted to dissect the impact of SPAG6 on the immune response and cancer cell stemness in LUSC.

RESULTS

DNA methyltransferase 3b (DNMT3b)-mediated hypermethylation of the SPAG6 promoter in LUSC led to SPAG6 downregulation. SPAG6 reverted the malignant phenotype of LUSC cells. SPAG6 regulated the JAK/STAT pathway by inhibiting the transcription of STAT1 and STAT3. The expression of SPAG6 was positively related to immune infiltration in LUSC and inversely related to the expressions of the immunosuppressive genes CTLA4 and PDCD1. SPAG6 expression was negatively correlated with cancer cell stemness in LUSC, and its expression inhibited the expressions of Nanog, ALDH1, and Sox2, markers of cancer cell stemness.

CONCLUSIONS

DNMT3b-mediated SPAG6 promoter hypermethylation activates the JAK/STAT pathway to promote LUSC progression.

Smoking-associated Downregulation of FILIP1L Enhances Lung Adenocarcinoma Progression Through Mucin Production, Inflammation, and Fibrosis

Lung adenocarcinoma (LUAD) is the major subtype in lung cancer, and cigarette smoking is essentially linked to its pathogenesis. We show that downregulation of Filamin A interacting protein 1-like (FILIP1L) is a driver of LUAD progression. Cigarette smoking causes its downregulation by promoter methylation in LUAD. Loss of FILIP1L increases xenograft growth, and, in lung-specific knockout mice, induces lung adenoma formation and mucin secretion. In syngeneic allograft tumors, reduction of FILIP1L and subsequent increase in its binding partner, prefoldin 1 (PFDN1) increases mucin secretion, proliferation, inflammation, and fibrosis. Importantly, from the RNA-sequencing analysis of these tumors, reduction of FILIP1L is associated with upregulated Wnt/β-catenin signaling, which has been implicated in proliferation of cancer cells as well as inflammation and fibrosis within the tumor microenvironment. Overall, these findings suggest that down-regulation of FILIP1L is clinically relevant in LUAD, and warrant further efforts to evaluate pharmacologic regimens that either directly or indirectly restore FILIP1L-mediated gene regulation for the treatment of these neoplasms.

Significance

This study identifies FILIP1L as a tumor suppressor in LUADs and demonstrates that downregulation of FILIP1L is a clinically relevant event in the pathogenesis and clinical course of these neoplasms.

Characteristic of Molecular Subtypes in Lung Squamous Cell Carcinoma Based on Autophagy-Related Genes and Tumor Microenvironment Infiltration

Background

Recently, a large number of studies have sought personalized treatment for lung squamous cell carcinoma (LUSC) by dividing patients into different molecular subtypes. Autophagy plays an important role in maintaining the tumor microenvironment and immune-related biological processes. However, the molecular subtypes mediated by autophagy in LUSC are not clear.

Methods

Based on 490 LUSC samples, we systematically analyzed the molecular subtype modification patterns mediated by autophagy-related genes. The ssGSEA and CIBERSORT algorithm were utilized to quantify the relative abundance of TME cell infiltration. Principal component analysis was used to construct autophagy prognostic score (APS) model.

Results

We identified three autophagy subtypes in LUSC, and their clinical outcomes and TME cell infiltration had significant heterogeneity. Cluster A was rich in immune cell infiltration. The enrichment of EMT stromal pathways and immune checkpoint molecules were significantly enhanced, which may lead to its immunosuppression. Cluster B was characterized by relative immunosuppression and relative stromal activation. Cluster C was activated in biological processes related to repair. Patients with high APS were significantly positively correlated with TME stromal activity and poor survival. Meanwhile, high APS showed an advantage in response to anti-PD1 and anti-CTLA4 immunotherapy.

Conclusion

This study explored the autophagy molecular subtypes in LUSC. We also discovered the heterogeneity of TME cell infiltration driven by autophagy-related genes. The established APS model is of great significance for evaluating the prognosis of LUSC patients, the infiltration of TME cells, and the effect of immunotherapy.

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.