Lung Squamous Cell Carcinoma Samples Research Articles

ERH is a prognostic biomarker associated with immune cell infiltration in lung cancer

Introduction The enhancer of rudimentary homolog (ERH) is significant in cancers, but its role in lung cancer is understudied. We divided lung cancer patients into high and low ERH expression groups based on tumor tissue levels. Methods Using the log-rank test, we analyzed the correlation between ERH expression and patient prognosis. The effects of high ERH expression on lung cancer cell proliferation, migration, and invasion were assessed using CCK8, EDU, transwell, and wound healing assays. Results ERH expression was significantly higher in cancerous versus normal lung tissue (p < 0.05), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Patients with high ERH expression had worse overall survival (HR = 1.37, p = 2.5 × 10−7) and first progression survival (HR = 1.38, p = 0.00065) in lung cancer. However, while high ERH expression predicts an unfavorable prognosis in LUAD, it does not hold true for LUSC. Furthermore, knockdown of ERH inhibited lung cancer cell proliferation, migration, and invasion. ERH expression was linked to immune cell infiltration. High ERH expression in LUAD and LUSC samples correlated with higher CD8 T cell, T cells CD4 memory activated, and M1 macrophages abundance, while low ERH expression correlated with higher T cells CD4 memory resting abundance. Conclusion Upregulation of ERH in lung cancer tissue is associated with poor prognosis and immune cell infiltration.

A Four-Gene Autophagy-Related Prognostic Model Signature and Its Association With Immune Phenotype in Lung Squamous Cell Carcinoma.

In the era of immunotherapy, there is a critical need for effective biomarkers to improve outcome prediction and guide treatment decisions for patients with lung squamous cell carcinoma (LUSC). We hypothesized that the immune contexture of LUSC may be influenced by tumor intrinsic events, such as autophagy. We aimed to develop an autophagy-related risk signature and assess its predictive value for immune phenotype. Expression profiles of autophagy-related genes (ARGs) in LUSC samples were obtained from the TCGA and GEO databases. Survival analyses were conducted to identify survival-related ARGs and construct a risk signature using the Random Forest algorithm. Four ARGs (CFLAR, RGS19, PINK1, and CTSD) with the most significant prognostic value were selected to construct the risk signature. Patients in the high-risk group exhibited worse prognosis than those in the low-risk group (p < 0.0001 in TCGA; p < 0.01 in GEO) and the risk score was identified as an independent prognostic factor. We observed that the high-risk group displayed an immune-suppressive status and showed higher levels of infiltrating regulatory T cells and macrophages, which are associated with poorer outcomes. Additionally, the risk score exhibited a significantly positive correlation with the expression of PD-1 and CTLA4, as well as the estimate score and immune score. This study provided an effective autophagy-related prognostic signature, which could also predict the immune phenotype.

Exploring the prognostic and therapeutic value of HIF1A in lung adenocarcinoma

Lung adenocarcinoma (LUAD) remains a challenge within the realm of non-small cell lung cancer (NSCLC), demanding innovative diagnostic and therapeutic solutions. In this study, we systematically detected the correlation between the expression of hypoxia-induced factor 1A (HIF1A) and the clinical characteristics of LUAD, alongside lung squamous cell carcinoma (LUSC). Our bioinformatic analysis reveals that HIF1A mRNA expression is significantly upregulated in both LUAD and LUSC samples compared to non-tumorous lung tissues. The overexpression is positively correlated with increased copy number variation and negatively associated with promoter methylation. However, meta-analysis and survival analyses revealed a pronounced association between elevated HIF1A expression and poor clinical outcome specifically within the LUAD subset, with no such correlation evident in LUSC. Additionally, we explored the interplay between HIF1A expression, leukocyte infiltration, and the presence of immunosuppressive markers, revealing HIF1A's suppressive role in cytotoxicity against cancer cells. Furthermore, we performed in silico prediction to explore the correlations between HIF1A and its interacting proteins, associated pathways, glycolysis, and m6A modification, and the feasibility of targeting HIF1A with specific drugs. In summary, our study revealed the prognostic significance and therapeutic potential of HIF1A in LUAD.

Immunogenic cell death-related genes predict prognosis and response to immunotherapy in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a malignancy with limited therapeutic options. Immunogenic cell death (ICD) has the potential to enhance the efficacy of cancer therapy by triggering immune responses. We aimed to explore the potential of ICD-based classification in predicting prognosis and response to immunotherapy for LUSC. RNA-seq information and clinical data of LUSC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. ICD-related gene expressions in LUSC samples were analyzed by consensus clustering. Subsequently, differentially expressed genes (DEGs) between different ICD-related subsets were analyzed. Tumor mutation burden, immune cell infiltration, and survival analyses were conducted between different ICD subsets. Finally, an ICD-related risk signature was constructed and evaluated in LUSC patients, and the immunotherapy responses based on the gene expressions were also forecasted. ICD-high and ICD-low groups were defined, and 1466 DEGs were identified between the two subtypes. These DEGs were mainly enriched in collagen-containing extracellular matrix, cytokine-cytokine receptor interaction, the PI3K-Akt signaling pathway, and neuroactive ligand-receptor interaction. Furthermore, the ICD-low group exhibited a favorable prognosis, enhanced TTN and MUC16 mutation frequencies, increased infiltrating immune cells, and downregulated immune checkpoint expressions. Furthermore, we demonstrated that an ICD-related model (based on CD4, NLRP3, NT5E, and TLR4 genes) could forecast the prognosis of LUSC, and ICD risk scores were lower in the responder group. In summary, the predicted values of ICD-related genes (CD4, NLRP3, NT5E, and TLR4) for the prognosis and response to immunotherapy in LUSC were verified in the study, which benefits immunotherapy-based interventions for LUSC patients.

Efficacy analysis of ALK inhibitors for treating lung squamous carcinoma patients harboring ALK rearrangement.

Anaplastic lymphoma kinase (ALK)-rearranged pulmonary squamous cell carcinoma (SCC) is a rare subtype of non-small cell lung cancer and the treatment options are limited. We aimed to evaluate the efficacy of ALK tyrosine kinase inhibitors (TKIs) in advanced lung SCC patients with ALK rearrangement. We collected 11 primary lung SCC samples at the Zhejiang Cancer Hospital between March 2015 and October 2022. In addition, we conducted a literature search of previous studies, and a pooled analysis of 34 patients was performed. The Kaplan-Meier method was applied to generate progression-free survival (PFS) and overall survival (OS) curves, and a log-rank test was used to compare PFS and OS curves for different subgroups. A pooled analysis of 36 patients was performed. Nineteen patients (52.8%) achieved partial response and 9 (25.0%) had stable disease. The objective response rate was 52.8%, and the disease control rate was 77.8%. The median PFS was 7.10 months. Further, alectinib was not superior to crizotinib in prolonging PFS (9.00 vs. 6.00 months, P=0.60). The median PFS of patients receiving initial ALK TKIs as the first-line therapy and second- or further-line therapy was 9.00 and 6.00 months (P=0.26), respectively. Patients with ALK-rearranged lung SCC obtained moderate benefit from ALK-inhibitor therapy. Compared with crizotinib, alectinib did not show superior efficacy in the treatment of ALK-positive lung SCC. Further high-quality trials are warranted.

Depletion of squalene epoxidase in synergy with glutathione peroxidase 4 inhibitor RSL3 overcomes oxidative stress resistance in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) lacks effective targeted therapies and has a poor prognosis. Disruption of squalene epoxidase (SQLE) has been implicated in metabolic disorders and cancer. However, the role of SQLE as a monooxygenase involved in oxidative stress remains unclear. We analyzed the expression and prognosis of lung adenocarcinoma (LUAD) and LUSC samples from GEO and TCGA databases. The proliferative activity of the tumors after intervention of SQLE was verified by cell and animal experiments. JC-1 assay, flow cytometry, and Western blot were used to show changes in apoptosis after intervention of SQLE. Flow cytometry and fluorescence assay of ROS levels were used to indicate oxidative stress status. We investigated the unique role of SQLE expression in the diagnosis and prognosis prediction of LUSC. Knockdown of SQLE or treatment with the SQLE inhibitor terbinafine can suppress the proliferation of LUSC cells by inducing apoptosis and reactive oxygen species accumulation. However, depletion of SQLE also results in the impairment of lipid peroxidation and ferroptosis resistance such as upregulation of glutathione peroxidase 4. Therefore, prevention of SQLE in synergy with glutathione peroxidase 4 inhibitor RSL3 effectively mitigates the proliferation and growth of LUSC. Our study indicates that the low expression of SQLE employs adaptive survival through regulating the balance of apoptosis and ferroptosis resistance. In future, the combinational therapy of targeting SQLE and ferroptosis could be a promising approach in treating LUSC.

Identification of Key DNA methylation sites related to differentially expressed genes in Lung squamous cell carcinoma

Changes in DNA methylation level at some CpG locus are closely associated with the occurrence of lung squamous cell carcinoma (LUSC). However, its specific regulatory mechanism is still unclear. Therefore, it is necessary to systematically identify and analyze those key CpG sites whose DNA methylation levels are closely related to the differential expression of up- and down-regulated genes in LUSC. Due to the dispersion of DNA methylation sites in different regions of genome, to study the correlation between gene expression level and DNA methylation, we divided gene into 6 non-overlapping functional regions and proposed a two-step correlation analysis method to identify differential DNA methylation sites and matched differential expression genes. As a results, we obtained 39 key CpG sites scattered in 27 genes. Through comparative analysis of LUSC-normal sample pairs, we found that these sites and genes can accurately cluster LUSC samples and normal samples. Finally, we used these sites and genes to distinguish LUSC from normal samples. The results suggest that they can be used as effective biomarkers for identifying LUSC. In addition, the proposed two-step correlation analysis method can also be extended to the identification of biomarkers of other cancers and diseases.

Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways.

Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes.

MMP12 serves as an immune cell-related marker of disease status and prognosis in lung squamous cell carcinoma.

Worldwide, lung squamous cell carcinoma (LUSC) has wreaked havoc on humanity. Matrix metallopeptidase 12 (MMP12) plays an essential role in a variety of cancers. This study aimed to reveal the expression, clinical significance, and potential molecular mechanisms of MMP12 in LUSC. There were 2,738 messenger RNA (mRNA) samples from several multicenter databases used to detect MMP12 expression in LUSC, and 125 tissue samples were validated by immunohistochemistry (IHC) experiments. Receiver operator characteristic (ROC) curves, Kaplan-Meier curves, and univariate and multivariate Cox regression analyses were used to assess the clinical value of MMP12 in LUSC. The potential molecular mechanisms of MMP12 were explored by gene enrichment analysis and immune correlation analysis. Furthermore, single-cell sequencing was used to determine the distribution of MMP12 in multiple tumor microenvironment cells. MMP12 was significantly overexpressed at the mRNA level (p<0.05, SMD = 3.13, 95% CI [2.51-3.75]), which was verified at the protein level (p<0.001) by internal IHC experiments. MMP12 expression could be used to differentiate LUSC samples from normal samples, and overexpression of MMP12 itself implied a worse clinical prognosis and higher levels of immune cell infiltration in LUSC patients. MMP12 was involved in cancer development and progression through two immune-related signaling pathways. The high expression of MMP12 in LUSC might act as an antigen-presenting cell-associated tumor neoantigen and activate the body's immune response. MMP12 expression is upregulated in LUSC and high expression of MMP12 serves as a risk factor for LUSC patients. MMP12 may be involved in cancer development by participating in immune-related signaling pathways and elevating the level of immune cell infiltration.

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC. TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways. This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response. Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Kallikrein-related peptidase 13 expression and clinicopathological features in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) is associated with poor prognosis. Molecular targeting drugs have been demonstrated to be effective for lung adenocarcinoma; however, they are often not effective for LSCC. Kallikrein-related peptidase 13 (KLK13) expression enhances the malignancy of lung adenocarcinoma; however, its expression and crucial role in LSCC remain largely unknown. The present study examined the relationship between the KLK13 expression and clinicopathological features of LSCC. A total of 94 patients diagnosed with LSCC who underwent lobectomy, segmentectomy or wedge resection were selected. KLK13 expression was evaluated through immunostaining of formalin-fixed paraffin-embedded sections of surgical specimens. Of the 94 LSCC samples, 70 exhibited no KLK13 expression, while the remaining 24 exhibited ectopic expression. KLK13 expression in tumors was focal and restricted to the cytoplasm of keratinized cells. LSCC cases were classified into KLK13-negative and KLK13-positive groups, and KLK13 expression was positively associated with E-cadherin expression (P=0.0143). Associations between KLK13 expression and keratinization (P=0.0052) or absence of lymphatic vessel invasion (P=0.0603) were observed; however, these trends did not reach statistical significance. The present findings indicated that KLK13 expression in keratinized LSCC may have a protective role in lymphatic vessel invasion of LSCC, which suggests its significance for therapeutic applications against LSCC.

Identification of Clinical Prognostic Regulators and Analysis of Ferroptosis-Related Signatures in the Tumor Immune Microenvironment in Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC) is a common respiratory malignancy and presents an increasing prevalence. Ferroptosis is a newly identified controlled cell death that has captured clinical attention worldwide. However, the ferroptosis-related lncRNA expression in LUSC and its relevance to prognosis remain elusive. The research measured predictive ferroptosis-related lncRNAs in LUSC samples from the TCGA datasets. Data on the stemness indices (mRNAsi) and corresponding clinical characteristics were obtained from TCGA. A prognosis model was established using the LASSO regression. Changes within the neoplasm microenvironment (TME) and medicine association were examined to grasp higher immune cell infiltration in numerous risk teams. In line with coexpression studies, the expression of lncRNAs is closely associated with that of ferroptosis. They were overexpressed in unsound people in the absence of alternative clinical symptoms. The low-risk and speculative teams were considered to have substantial differences in CCR and inflammation-promoting genes. C10orf55, AC016924.1, AL161431.1, LUCAT1, AC104248.1, and MIR3945HG were highly expressed in the high-risk group, suggesting their involvement in the oncology process of LUSC. Moreover, AP006545.2 and AL122125.1 were considerably higher in the low-risk group, implying the potential of these genes as LUSC tumor suppressor genes. The biomarkers listed above may serve as therapeutic targets for LUSC. lncRNAs were also linked to patient outcomes in the LUSC trial. lncRNAs of ferroptosis were overexpressed in the high-risk cohort without other clinical signs, implying their potential to predict BLCA prognosis. GSEA highlighted immunological and tumor-related pathways in the high-risk group. LUSC occurrence and progression are linked to lncRNAs of ferroptosis. Corresponding prognostic models help forecast the prognosis of LUSC patients. lncRNAs of ferroptosis and associated immune cell infiltration in the tumor microenvironment (TME) may serve as potential therapeutic targets in LUSC, which requires further trials. In addition, the lncRNAs of ferroptosis signature offer a viable alternative to predict LUSC, and these ferroptosis-lncRNAs show a prospective research area for LUSC-targeted treatment in the future.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Reciprocal FGF19-GLI2 signaling induces epithelial-to-mesenchymal transition to promote lung squamous cell carcinoma metastasis.

Metastatic lung squamous cell carcinoma (LUSC) is one of the most common causes of cancer death worldwide. As yet, however, the molecular mechanism underlying LUSC metastasis remains elusive. In this study, we report a novel mechanism involving signaling interactions between FGF19 and GLI2 that could drive the progression of LUSC. The expression of FGF19 in human LUSC samples was assessed by immunohistochemistry. The concentration of FGF19 in serum samples was assessed by ELISA. RNA sequencing, scratch wound-healing, trans-well, GO analysis, GSEA, luciferase reporter, Western blotting, immunofluorescence and immunohistochemistry assays, as well as an animal model were used to investigate the molecular mechanism underlying FGF19 driven LUSC progression. The therapeutic effect of a GLI2 inhibitor was determined using both in vitro cellular and in vivo animal experiments. We found that FGF19, a member of the fibroblast growth factor family, plays a crucial role in the invasion and metastasis of LUSC, and identified GLI2 as an important downstream effector of FGF19 involved in metastasis. Surprisingly, we found that FGF19 and GLI2 could reciprocally induce the expression of each other, and form a positive feedback loop to promote LUSC cell invasion and metastasis. These findings were corroborated by an association between a poor prognosis of LUSC patients and FGF19/GLI2 co-expression. In addition, we found that the GLI inhibitor GANT61 could effectively reduce FGF19-mediated LUSC invasion and metastasis. Our data suggest that FGF19 may serve as a novel biomarker for predicting metastatic LUSC. Intervening with the FGF19-GLI2 feedback loop may be a strategy for the treatment of FGF19-driven LUSC metastasis.

Construction of the metabolism-related models for predicting prognosis and infiltrating immune phenotype in lung squamous cell carcinoma.

Purpose: Cancers often display disorder metabolism, which closely related to the poor outcome of patients. We aimed to establish prognostic models using metabolism-associated genes, and identify the key factor involved in metabolism in lung squamous cell carcinoma (LUSC). Materials and Methods: R package 'TCGA biolinks' was used to download the mRNA sequencing data of LUSC from TCGA. The clusterProfiler package was performed to analyze biological pathways. The online tool GEPIA2 and cox regression method were applied to identify the two gene lists associated with metabolism and prognosis of LUSC. The lasso modeling was conducted to establish prognostic models. The quantiseq method was used to identify the cellular abundance of expression matrix in TCGA-LUSC dataset. Immunohistochemistry and western blotting were done to evaluate the STXBP1 expression in LUSC samples. Lactate assay and ATP detection were performed to assess metabolic effect, and CCK8 assay was done to test cell proliferation in the LUSC cells with overexpression and suppression of STXBP1. Results: Two lists of survival-metabolism-associated genes (11 and 28 genes) were identified and applied in the prognostic model 1 and model 2 construction from TCGA-LUSC dataset. High-risk LUSC patients associated with poor survival in the training cohort and the test cohort of both model 1 and model 2. Higher ROC values for 10- year survival was shown in model 2 than in model 1. In addition, macrophage M1, macrophage M2, neutrophil, and T regulatory cell were enriched in the high-risk group of model 2. STXBP1 was the only optimized gene in both model 1 and model 2, and related to the poor outcome of LUSC patients. Furthermore, STXBP1 associated with infiltrating immune cells, and increased lactate, ATP levels, and cell proliferation. Conclusion: Our finding provides the metabolism-associated models to predict prognosis of LUSC patients. STXBP1, as the key optimized gene in the model, promotes metabolic progress to increase lactate and ATP levels in LUSC cells.

The Expression of Histone Acetyltransferase KAT6A in Non-small Cell Lung Cancer.

KAT6A is considered a factor influencing carcinogenesis. Due to the fact that lung cancer is one of the leading causes of death, the aim of our study was to evaluate KAT6A expression in non-small cell lung cancer (NSCLC) tumors and the NSCLC cell line model. The expression of KAT6A was examined in NSCLC tumors by real-time PCR and immunohistochemistry. KAT6A expression was investigated in the NSCLC cell line model by real-time PCR, western blot, and immunofluorescence. KAT6A protein level was elevated in NSCLC tumors compared to non-malignant lung tissues (NMLT). The KAT6A mRNA expression in NSCLC, lung adenocarcinoma, and lung squamous cell carcinoma samples was differentiated. The results from the in vitro NSCLC model demonstrated elevated expression of KAT6A in lung cancer cell lines in comparison to normal lung fibroblasts. The outcomes showed an increased KAT6A protein level in NSCLC compared to control samples, which suggests the oncogenic role of KAT6A in this type of cancer. Therefore, targeting KAT6A in NSCLC might be worth consideration.

Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration.

Increasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated. TCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package. We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven. This study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.

Prediction of lung squamous cell carcinoma immune microenvironment and immunotherapy efficiency with pyroptosis-derived genes.

Lung squamous cell carcinoma (LUSC) is a common subtype of lung cancer that exhibits diverse pyroptosis regulatory patterns. Studies have highlighted the significance of pyroptosis in cancer invasion and immune responses. We aimed to explore the signatures of pyroptosis-related genes and their immune relevance in LUSC. Using The Cancer Genome Atlas (TCGA)-LUSC cohort and 5 gene expression omnibus (GEO) datasets, we performed consensus clustering based on 41 pyroptosis-related genes, and single sample gene set enrichment analysis (ssGSEA) was employed to calculate the infiltration levels of distinct clusters. A pyroptosis scoring scheme using the principal component analysis (PCA) method was used to quantify pyroptosis regulation in patients with LUSC and predict their prognosis. Four pyroptosis clusters were identified among 833 LUSC samples, which were associated with different Kyoto encyclopedia of genes and genome (KEGG) signaling pathways and tumor microenvironment infiltration features, and were highly consistent with 4 reported immune phenotypes: immune-responsive, immune-non-functional, immune-exclusion, and immune-ignorance. We then divided the patients into high- and low-pyroptosis score subgroups, and patients with higher scores were characterized by prolonged survival and attenuated immune infiltration. Moreover, higher scores were correlated with male patients, higher microsatellite instability, lower immune checkpoint inhibitor expression (such as CTLA-4 and GAL-9), and high mutation rates of typical mutated genes (e.g., TP53 and TTN). In particular, patients with lower pyroptosis scores showed better immune response to immune checkpoint inhibitor treatment. Pyroptosis regulatory patterns in the immune microenvironment can predict the clinical outcomes of patients with LUSC. Accurately quantifying the pyroptosis of individual patients will strengthen the understanding of heterogeneity within the LUSC tumor microenvironment infiltration areas.

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.

A Novel Prognosis Signature Based on Ferroptosis-Related Gene DNA Methylation Data for Lung Squamous Cell Carcinoma.

Ferroptosis-related genes regulating an iron- and lipid reactive oxygen species (ROS)-dependent form of programmed cell death suggest critical roles for ferroptosis in cancers. However, the prognostic value of ferroptosis-related epigenetic features such as DNA methylation in lung squamous cell carcinoma (LUSC) needs to be studied. Ferroptosis-related genes are collected from the FerrDb database, and the methylation data of these related genes in LUSC methylation data downloaded from the TCGA are retrieved. The DNA methylation data (362 LUSC samples) were analyzed to screen prognostic ferroptosis-related methylation sites. After patients with complete overall survival (OS) information were randomly separated into training cohort (n = 200) and validation cohort (n = 162), the least absolute shrinkage and selection operator (LASSO) and the Cox regression were used to establish and validate the prognostic signature. The time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses, Harrell's concordance index (C-index), calibration analysis, and decision curve analysis (DCA) were performed to evaluate the risk signature and related nomogram. A series of other bioinformatics approaches such as mexpress, cbioportal, maftools, string, metascape, TIMER, and Kaplan–Meier survival curve analysis were also used to determine the methylation, mutation status, protein interaction network or functional enrichment, effects on immune cell infiltration, or expression level prognosis of those signature-related genes. A total of 137 DNA methylation sites were identified as prognostic predictors corresponding to 109 ferroptosis-related genes (FRGs). The methylation signature containing 31 methylation sites proved to be superior predictive efficiency in predicting the 1-, 3-, 5-, and 10-year OS. 8 out of 28 signature-related genes were significantly related to OS time or OS state in patients with LUSC. In addition, DUSP1, ZFN36, and ALOX5 methylation status also correlated with pathological M and ALOX5 methylation correlated with pathological N. The prognostic prediction efficiency of T, N, M, and the stage was inferior to that of the DNA methylation signature. LUSC patients in the high-risk group own a significantly larger number of variants of FRGs than those in the low-risk group. In addition, negative or positive correlation patterns were presented among the different infiltrating immune cells with risk scores or signature-related genes in patients with LUSC. The expression level of 15 signature-related genes showed a significant relationship with OS of LUSC patients. A novel prognostic nomogram survival model containing 4 factors including age, pathologic T, stage, and risk group was constructed and validated, AndC-index, decision curve analysis (DCA), and calibration analysis demonstrated its excellent predictive performance. The FRG DNA methylation data-based prognostic model acts as a powerful prognostic prediction indicator in LUSC patients and is advantageous over the traditional model based on T, N, M, and stage.