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Background: Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. Methods: LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Results: Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. Conclusions: This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

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Background: In the absence of targeted mutations and immune checkpoints, platinum-based chemotherapy remains a gold standard agent in the treatment of patients with lung squamous cell carcinoma (LUSC). However, cisplatin resistance greatly limits its therapeutic efficacy and presents challenges in the treatment of lung cancer patients. Therefore, the potential clinical needs for this research focus on identifying novel molecular signatures to further elucidate the underlying mechanisms of cisplatin resistance in LUSC. A growing body of evidence indicates that alternative splicing (AS) events significantly influence the tumor progression and survival of patients with LUSC. However, there are few systematic analyses of AS reported in LUSC. This study aims to explore the role of messenger RNA (mRNA), microRNA (miRNA), and AS in predicting prognosis in patients with cisplatin-resistant LUSC and provide potential therapeutic targets and drugs. Methods: Gene expression and miRNA expression, using RNA sequencing (RNA-seq), and SpliceSeq data were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to construct predictive models. Kaplan-Meier survival analyses were used to evaluate patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) conducted via the R package "GSEAbase" was used to evaluate the immune-related characteristics. Immunohistochemistry was used to examine protein expression. The Connectivity Map (CMap) database was used to screen for potential drugs. The 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay was used to determine and calculate the half-maximal inhibitory concentration (IC50) of the drugs, sulforaphane and parthenolide. Results: In this study, bioinformatics were used to identify mRNAs, miRNAs, and AS events related to response to cisplatin and to establish an integrated prognostic signature for 70 patients with LUSC and cisplatin resistance. The prognostic signature served as an independent prognostic factor with high accuracy [hazard ratio (HR) =2.346, 95% confidence interval (CI): 1.568-3.510; P<0.001], yielding an area under the curve (AUC) of 0.825, 0.829, and 0.877 for 1-, 3-, and 5-year survival, respectively. It also demonstrated high predictive performance in this cohort of patients with LUSC, with an AUC of 0.734, 0.767, and 0.776 for 1-, 3-, and 5-year survival, respectively. This integrated signature was also found to be an independent indicator among conventional clinical features (HR =2.288, 95% CI: 1.547-3.383; P<0.001). In addition, we analyzed the correlation of the signature with immune infiltration and identified several small-molecule drugs that had the potential to improve the survival of patients with LUSC. Conclusions: This study provides a framework for the mRNA-, miRNA-, and AS-based evaluation of cisplatin response and several potential therapeutic drugs for targeting cisplatin resistance in LUSC. These findings may serve as a theoretical basis for the clinical alleviation of cisplatin resistance and thus help to improve treatment responses to chemotherapy in patients with LUSC.

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Lung squamous cell carcinoma (LUSC) is characterized by a high rate of metastasis and recurrence, leading to a poor prognosis for affected patients. Intestinal metastasis of LUSC is a rare clinical occurrence. Treatment options for LUSC patients with intestinal metastasis are limited, and no standard therapy guidelines exist for managing these cases. In this review, we discuss the clinical features, diagnosis, and treatment of LUSC patients with intestinal metastasis and present a rare case of LUSC with intestinal metastasis. We describe a patient who presented with a severe cough and chest pain and diagnosed with LUSC and bone tumor. Initially, the primary LUSC and bone tumor were controlled with standard treatments. However, the primary LUSC reoccurred shortly after treatment, this time with intestinal metastasis, for which effective treatments are lacking. Our observation from the case suggests that LUSC metastasizing to intestinal tract is associated with a poorer prognosis.

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Background: The significant progress has been made in targeted therapy for lung adenocarcinoma (LUAD) in the past decade. Only few targeted therapeutics have yet been approved for the treatment of lung squamous cell carcinoma (LUSC). Several higher frequency of gene alterations are identified as potentially actionable in LUSC. Our work aimed to explore the complex interplay of multiple genetic alterations and pathways contributing to the pathogenesis of LUSC, with a very low frequency of a single driver molecular alterations to develop more effective therapeutic strategies in the future. Methods: We retrospectively analyzed the targeted next-generation sequencing (NGS) data (approximately 600 genes) of 335 patients initially diagnosed with non-small cell lung cancer (NSCLC) at our institution between January 2019 and March 2023 and explored the somatic genome alteration difference between LUSC and LUAD. Results: We analyzed that the presence of loss-of-function (LoF) mutations (nonsense, frameshift, and splice-site variants) in histone-lysine N-methyltransferase 2D (KMT2D) was much more prevalent in LUSC (11/53, 20.8%) than in LUAD (6/282, 2.1%). Moreover, our data indicated TP53 co-mutated with KMT2D LoF in 90.9% (10/11) LUSC and 33.3% (2/6) LUAD. Notably, the mutation allele fraction (MAF) of KMT2D was very similar to that of TP53 in the co-mutated cases. Genomic profiling of driver gene mutations of NSCLC showed that 81.8% (9/11) of the patients with LUSC with KMT2D LoF mutations had PIK3CA amplification and/or FGFR1 amplification. Conclusions: Our results prompted that somatic LoF mutations of KMT2D occur frequently in LUSC, but are less frequent in LUAD and therefore may potentially contribute to the pathogenesis of LUSC. Concurrent TP53 mutations, FGFR1 amplification, and PIK3CA amplification are very common in LUSC cases with KMT2D LoF mutations. It needs more deeper investigation on the interplay of the genes and pathways and uses larger cohorts in the future.

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Modulation of DNA damage repair in lung squamous cell carcinoma (LUSC) can result in the generation of neoantigens and heightened immunogenicity. Therefore, understanding DNA damage repair mechanisms holds significant clinical relevance for identifying targets for immunotherapy and devising therapeutic strategies. Our research has unveiled that the tumor suppressor zinc finger protein 750 (ZNF750) in LUSC binds to the promoter region of tenascin C (TNC), leading to reduced TNC expression. This modulation may impact the malignant behavior of tumor cells and is associated with patient prognosis. Additionally, single-cell RNA sequencing (scRNA-seq) of LUSC tissues has demonstrated an inverse correlation between ZNF750/TNC expression levels and immunogenicity. Manipulation of the ZNF750-TNC axis in vitro within LUSC cells has shown differential sensitivity to CD8+ cells, underscoring its pivotal role in regulating cellular immunogenicity. Further transcriptome sequencing analysis, DNA damage repair assay, and single-strand break analyses have revealed the involvement of the ZNF750-TNC axis in determining the preference for homologous recombination (HR) repair or non-homologous end joining (NHEJ) repair of DNA damage. with involvement of the Hippo/ERK signaling pathway. In summary, this study sheds light on the ZNF750-TNC axis's role in DNA damage repair regulation in LUSC, laying a groundwork for future translational research in immune cell therapy for LUSC.

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Background: Dysregulation of fatty acid metabolism (FAM) represents a significant metabolic alteration in tumorigenesis. However, the role of FAM-related genes (FAMRGs) in early-stage lung squamous cell carcinoma (LUSC) remains incompletely understood. Methods: A series of bioinformatic analyses and machine learning strategies were performed to construct a FAMRGs-based signature to predict prognosis and guide personalized treatment for early-stage LUSC patients. FAMRGs were screened through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signature Database (MSigDB). Prognosis FAMRGs were identified using univariate Cox regression, and unsupervised clustering analysis facilitated the division of the cohort into different clusters. The least absolute shrinkage and selection operator (LASSO)-Cox regression and multivariate regression analysis were employed to develop a FAMRGs-based signature for predicting overall survival (OS). A nomogram was subsequently constructed to facilitate risk assessment for individual patients. Comprehensive analyses of metabolic pathways, immune infiltration, immunomodulators, and potentially applicable drugs were conducted across different FAMRGs-related risk groups. Results: The FAMRGs-based signature, comprising nine genes (ACOT11, APOH, BMX, CYP2R1, DPEP3, FABP6, FADS2, GLYATL2, and THRSP), demonstrated robust predictive capabilities for prognosis in The Cancer Genome Atlas (TCGA)-LUSC dataset and validated across six independent Gene Expression Omnibus (GEO)-LUSC datasets. Notably, the FAMRGs-base signature exhibited superior prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, the signature was closely associated with immune cell infiltration, human leukocyte antigen (HLA) genes, and immune checkpoint genes expression. Additionally, the signature demonstrated potential sensitivity to chemo-/target-therapy. Conclusions: The FAMRGs-based signature demonstrated superior sensitivity in predicting the prognosis of early-stage LUSC. Detecting FAMRGs may provide predictive targets for the development of clinical treatment strategies.

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Until now, there are still few comparisons between neoadjuvant immunochemotherapy and chemotherapy as first-line treatment for patients with stage IB-IIIB lung squamous cell carcinoma (LUSC). In addition, the ability of pathologic response to predict long-term survival has still not been established. In this retrospective, controlled clinical trial, we ultimately enrolled 231 patients with stage IB to IIIB LUSC who received 2-4 cycles perioperative immunochemotherapy or chemotherapy alone, followed by resection. The primary endpoint of this study was pathological response. Secondary endpoints were disease-free survival (DFS), overall survival (OS), objective response rate (ORR), surgical resection rate and adverse events (AEs). The rates of major pathologic response (MPR) and pathologic complete response (pCR) in the immunochemotherapy group were 66.7% and 41.9%, respectively, which were both higher than that in the other group (MPR: 25.0%, pCR: 20.8%) (P < 0.001). The median DFS in the chemotherapy group was 33.1 months (95% CI 8.4 to 57.8) and not reached in the immunochemotherapy group (hazard ratio [HR] for disease progression, disease recurrence, or death, 0.543; 95% CI 0.303 to 0.974; P = 0.038). The median OS of the immunochemotherapy group was not achieved (HR for death, 0.747; 95% CI 0.373 to 1.495; P = 0.41), with the chemotherapy group 64.8 months (95% CI not reached to not reached). The objective response rate (ORR) of immunochemotherapy regimen was higher than that of the chemotherapy regimen (immunochemotherapy: 74.5%, chemotherapy: 42.3%, P < 0.001). About 60.8% in the immunochemotherapy group and 61.5% in the chemotherapy group eventually underwent surgery. The incidence of grade3 and 4 adverse events was 18.3% in the immunochemotherapy group and 2.6% in the chemotherapy group. MPR was significantly associated with DFS and OS (HR, 0.325; 95% CI 0.127 to 0.833; P = 0.019; and HR, 0. 906; 95% CI 0.092 to 1.008; P = 0.051, respectively). The C-index of MPR (0.730 for DFS, 0.722 for OS) was higher than the C-index of cPR (0.672 for DFS, 0.659 for OS) and clinical response (0.426 for DFS, 0.542 for OS). Therapeutic regimen (P < 0.001; OR = 7.406; 95% CI 3.054 to 17.960) was significantly correlated with MPR. In patients with stage IB to IIIB LUSC, neoadjuvant treatment with immunochemotherapy can produce a higher percentage of patients with a MPR and longer survival than chemotherapy alone. MPR may serve as a surrogate endpoint of survival to evaluate neoadjuvant therapy.

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BACKGROUND: Endoplasmic reticulum stress (ERS) acts critical roles on cell growth, proliferation, and metastasis in various cancers. However, the relationship between ERs and lung squamous cell carcinoma (LUSC) prognoses still remains unclear. METHODS: The consensus clustering analysis of ERS-related genes and the differential expression analysis between clusters were investigated in LUSC based on TCGA database. Furthermore, ERS-related prognostic risk models were constructed by LASSO regression and Cox regression analyses. Then, the predictive effect of the risk model was evaluated by Kaplan-Meier, Cox regression, and ROC Curve analyses, as well as validated in the GEO cohort. According to the optimal threshold, patients with LUSC were divided into high- and low- risk groups, and somatic mutations, immune cell infiltration, chemotherapy response and immunotherapy effect were systematically analyzed. RESULTS: Two ERS-related clusters were identified in patients with LUSC that had distinct patterns of immune cell infiltration. A 5-genes ERS-related prognostic risk model and nomogram were constructed and validated. Kaplan-Meier curves and Cox regression analysis showed that ERS risk score was an independent prognostic factor (p < 0.001, HR = 1.317, 95% CI = 1.159-1.496). Patients with low-risk scores presented significantly lower TIDE scores and significantly lower IC50 values for common chemotherapy drugs such as cisplatin and gemcitabine. CONCLUSION: ERS-related risk signature has certain prognostic value and may be a potential therapeutic target and prognostic biomarker for LUSC patients.

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Background: The lysyl oxidate-like (LOXL) family was reported to be involved in the process of cancer development. However, the prognostic value of LOXL in lung cancer is unknown. We aimed to study the expression pattern and prognostic value of LOXL family members in lung squamous cell carcinoma (LUSC). Methods: The Wilcoxon test and logistic regression analysis were used to study the expression level of LOXLs and its correlation with clinical characteristics. The Kaplan-Meier method and Cox regression analysis were performed to estimate the correlation of LOXsL expression with the survival of LUSC patients. Receiver operator characteristic (ROC) curves were plotted, and areas under the curves (AUCs) were calculated to estimate the diagnostic and prognostic power of LOXL. Cell Counting Kit-8 (CCK-8) assays, wound healing assays and Transwell assays were used to estimate the impact of LOXL2 on LUSC cells. Results: LOXL1 and LOXL2 expression was upregulated in LUSC tissues (P<0.001). LOXL1 and LOXL2 showed high diagnostic power in LUSC patients, with AUCs of 0.784 and 0.751, respectively. Patients with high LOXL2 expression levels showed poor overall survival (OS) (P=0.019) and progression-free survival (PFS) (P=0.015). High LOXL2 expression was an independent prognostic factor for poor survival (P=0.026). Inhibition of LOXL2 suppressed proliferation, migration and invasion in LUSC cell lines. Conclusions: Increased LOXL2 was related to poor survival in LUSC. LOXL2 may be a potential prognostic biomarker and therapeutic target in LUSC.

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Background: Aberrant methylation plays an essential role in early cancer development. In this study, we investigated methylation patterns in lung squamous cell carcinoma (LUSC) and matched non-tumor tissue and plasma samples to evaluate the potential of these patterns in the diagnosis of LUSC. Methods: The study group included 49 patients with stage I-III LUSC. We collected resected tumor tissue, paired peritumoral tissue, distant normal tissue, and corresponding plasma samples. A bespoke lung cancer bisulfite sequencing panel was used to profile the methylation level. Another 48 healthy volunteers provided control plasma samples. Results: Peritumoral and distant normal tissues presented similar methylation signatures, distinct from those in tumor tissue samples. A comparison of methylation profiles led to the identification of 871 tumor-specific differentially methylated blocks, including 847 hypermethylated and 24 hypomethylated blocks (adjusted P value <0.05). All top-ranked blocks were tumor-related. Tissue samples were analyzed for field cancerization to identify progressively aggravating aberrant methylations during tumor initiation and development. The analysis revealed that 221 blocks presented a stepwise increase in methylation levels, while seven blocks presented a stepwise decrease in methylation pattern as the sampling drew nearer to the tumor. The malignant contaminated ratio (MCR) confirmed the presence of distinct methylation patterns between tumor and peritumoral tissue samples. We then constructed a diagnostic panel using a combined diagnostic score of cell-free DNA (cfDNA) that showed high sensitivity and specificity. The healthy controls had a significantly lower combined diagnostic score (cd-score) than LUSC patients. Additionally, based on the methylation profiles, LUSC could be classified into two subgroups, C1 and C2. The methylation profile of the C2 group was not distinct from the healthy controls, which had a significantly lower cd-score than did the C1 group. Conclusions: LUSC-specific methylation patterns could potentially discriminate between peritumoral tissue, distant normal tumor tissue, and tumor tissues. This preliminary study also supported the potential utility of cfDNA methylation analysis in diagnosing LUSC.

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【Objective】 To investigate the molecular mechanism of microRNA-101-5p (miR-101-5p) affecting the proliferation and invasion of lung squamous cell carcinoma (LUSC) cells. 【Methods】 We downloaded the miRNA mature expression data and total RNA sequencing data of TCGA-LUSC from TCGA database to identify differentially expressed genes. The signal pathway enriched in ATAD2 was analyzed. The mRNA expressions of miR-101-5p and ATAD2 in the LUSC cells were detected by qRT-PCR. The effects of miR-101-5p on the proliferation and invasion of LUSC cells were detected by MTT assay, cloning assay, and invasion assay. The effects of ATAD2 on the cell cycle of LUSC cells were detected by flow cytometry. Western blotting was used to detect the expression of ATAD2 protein. Double luciferase experiment was used to verify whether miR-101-5p could bind to ATAD2 target. Finally, we detected the changes in the proliferation, cloning and invasion ability of LUSC cells by co-transfection with oe-ATAD2 and miR-101-5p mimic, and further explored whether miR-101-5p could regulate the biological function of LUSC cells through ATAD2. 【Results】 The miR-101-5p was significantly downregulated in LUSC tissues and cells. Overexpression of miR-101-5p could significantly inhibit the proliferation and invasion of LUSC cells. ATAD2, its downstream regulatory target gene, was significantly upregulated in LUSC, and miR-101-5p and ATAD2 expressions were inversely correlated. GSEA enrichment results showed that ATAD2 was significantly enriched in the cell cycle signal pathway. The double luciferase experiment proved that miR-101-5p targeted ATAD2, and the recovery experiment showed that miR-101-5p regulated the proliferation and invasion of LUSC cells by targeting ATAD2. 【Conclusion】 In this study, we found that miR-101-5p had low expression in LUSC, and that miR-101-5p decreased the proliferation and invasion of LUSC cells by targeted inhibition of ATAD2.

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BACKGROUND: Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers. METHODS: The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA. A necroptosis signature was constructed by survival analysis, and its correlation with patient prognosis and clinical features was evaluated. The molecular characteristics and drug response associated with the necroptosis signature were also identified. The drug candidates were then validated at the cellular level. RESULTS: The TCGA_LUSC dataset included 51 normal samples and 502 LUSC samples. The GSE73403 dataset included 69 samples. 159 genes involved in necroptosis pathways were acquired from the KEGG database, of which most showed significant differences between two groups in terms of genomic, transcriptional and methylation alterations. In particular, CHMP4C, IL1B, JAK1, PYGB and TNFRSF10B were significantly associated with the survival (p < 0.05) and were used to construct the necroptosis signature, which showed significant correlation with patient prognosis and clinical features in univariate and multivariate analyses (p < 0.05). Furthermore, CHMP4C, IL1B, JAK1 and PYGB were identified as potential targets of trametinib, selumetinib, SCH772984, PD 325901 and dasatinib. Finally, knockdown of these genes in LUSC cells increased chemosensitivity to those drugs. CONCLUSION: We identified a necroptosis signature in LUSC that can predict prognosis and identify patients who can benefit from targeted therapies.

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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.

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BACKGROUND: Immunotherapy has been a promising treatment in advanced lung cancer. However, only a few patients could benefit from it. Herein, we aimed to explore mutationrelated predictive biomarkers in lung squamous cell carcinoma (LUSC), which could help develop clinical immunotherapy strategies and screen beneficial populations. METHODS: Co-occurrence and mutually exclusive analysis was conducted on the TCGA-LUSC cohort. Correlations between the gene mutation status and tumor mutation burden (TMB) levels, and neo-antigen levels were analyzed by Wilcoxon test. Kaplan-Meier method was employed to analyze the progression-free survival (PFS) of lung cancer patients with immunotherapy. Gene set enrichment analysis (GSEA) was used to investigate the functional changes affected by TP53mut/TTNmut. The immune cell infiltration landscape in co-mutation subgroups was analyzed using CIBERSORT. RESULTS: 1) TP53, TTN, CSMD3, MUC16, RYR2, LRP1B, USH2A, SYNE1, ZFHX4, FAM135B, KMT2D, and NAV3 were frequently mutated in LUSC patients. 2) TMB levels in highly mutated groups were higher than that in wild type groups. 3) There were higher neoantigen levels in mutation group compared to the wild-type group, and LUSC patients in mutation group had longer PFS. 4) TP53mut/TTNmut co-mutation group exhibited higher TMB levels and better response to immunotherapy. 5) A host of immune-related signaling pathways was inhibited in TP53mut/TTNmut subgroup. 6) There were more T follicular helper cells and NK cells were in TP53mut/TTNmut subgroup than in the WT subgroup. CONCLUSION: The LUSC patients with TP53 and TTN co-mutation had higher TMB levels and better response to immunotherapy. The TP53 and TTN co-mutation is a promising novel biomarker to assist LUSC immunotherapy evaluation.

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Background: Lung squamous cell carcinoma (LUSC) is a highly malignant tumor with an extremely poor prognosis. Immune checkpoint inhibitors (ICIs) improve survival in some patients with LUSC. Tumor mutation burden (TMB) is a useful biomarker to predict the efficacy of ICIs. However, predictive and prognostic factors related to TMB in LUSC remain elusive. This study aimed to find effective biomarkers based on TMB and immune response and establish a prognostic model of LUSC. Methods: We downloaded Mutation Annotation Format (MAF) files from The Cancer Genome Atlas (TCGA) database and identified immune-related differentially expressed genes (DEGs) between high- and low-TMB groups. The prognostic model was established using cox regression. The primary outcome was overall survival (OS). Receiver operating characteristic (ROC) curves and calibration curves were used to verify the accuracy of the model. GSE37745 acted as external validation set. The expression and prognosis of hub genes as well as their correlation with immune cells and somatic copy number variation (sCNA) were analyzed. Results: The TMB of patients with LUSC was correlated with prognosis and stage. High TMB group had higher survival rate (P<0.001). Five TMB-related hub immune genes (TINAGL1, FGFR2, CTSE, SFTPA1, and IGHV7-81) were identified and the prognostic model was constructed. The survival time of high-risk group was significantly shorter than that of low-risk group (P<0.001). The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively. Calibration chart, risk curve, and nomogram revealed that the prognostic model was reliable in predicting the prognostic risk of LUSC, and the risk score of the model could be used as an independent prognostic factor for LUSC patients (P<0.001). Conclusions: Our results show that high TMB is associated with poor prognosis in patients with LUSC. The prognostic model related to TMB and immunity can effectively predict the prognosis of LUSC, and risk score is one of the independent prognostic factors of LUSC. However, this study still has some limitations, which need to be further verified in large-scale and prospective studies.

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Background: Lung squamous cell carcinoma (LUSC) is a devastating and difficult-to-treat type of lung cancer, and the prognosis of LUSC is the worst. The functional roles of focal adhesion-related genes were explored in LUSC based on data from The Cancer Genome Atlas (TCGA). Methods: RNA sequencing data and clinical characteristics of LUSC patients in TCGA-LUSC were obtained from the TCGA database. Through systematic analysis, we screened the prognostic genes and determined the focal adhesion-related pathways closely associated with LUSC. Results: We identified 444 prognostic genes and focal adhesion-related pathways intimately associated with LUSC. According to the focal adhesion-related genes, TCGA-LUSC patients were well divided into two groups: the low-risk group (G1) and the high-risk group (G2). A differential expression analysis identified 44 differentially expressed genes (DEGs) upregulated in the low-risk G1 group and 379 DEGs upregulated in the high-risk G2 group. The upregulated DEGs in the G1 group were primarily related to tyrosine metabolism, steroid hormone biosynthesis, retinol metabolism, platinum drug resistance, pentose and glucuronate interconversions, and metabolism of xenobiotics by cytochrome P450, while the downregulated DEGs in the G1 group were primarily related to ECM-receptor interaction, focal adhesion, proteoglycans in cancer, small cell lung cancer, cytokine-cytokine receptor interaction, and TGF-beta signaling pathway. The immune activity of the G1 group was lower than that of the G2 group, and the half-maximal inhibitory concentration (IC50) of five chemotherapy drugs (i.e., gemcitabine, methotrexate, vinorelbine, paclitaxel, and cisplatin) was significantly different between the G1 and G2 groups. Furthermore, a 10-gene prognostic model was constructed to predict the prognosis for LUSC patients: ITGA3, VAV2, FLNC, FLT4, HGF, MYL2, ITGB1, PDGFRA, CCND2, and PPP1CB. Conclusion: The status of focal adhesion-related genes has a close relationship with tumor classification and immunity in LUSC patients. A novel focal adhesion-related signature had good prognostic and predictive performance for LUSC. Our findings may provide new insight into the diagnosis and treatment of LUSC.

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Background: Lung squamous cell carcinoma (LUSC) has a poor prognosis and lacks appropriate diagnostic and treatment strategies. Apoptosis dysregulation is associated with tumor occurrence and drug resistance, but the prognostic value of apoptosis-related genes (ARGs) in LUSC remains unclear. Methods: Using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis based on differentially expressed ARGs, we constructed an ARG-related prognostic model for LUSC survival rates. We conducted correlation analysis of prognostic ARGs by incorporating the dataset of normal lung tissue from the Genotype-Tissue Expression (GTEx) database. We then constructed a risk model, and the predictive ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Non-small cell lung cancer (NSCLC) single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, these data were subjected to single-cell analysis. Cell subgroups were determined and annotated by dimensionality reduction clustering, and the cell subgroups in disease development were identified via pseudotemporal analysis with the Monocle 2 algorithm. Results: We identified four significantly prognostic ARGs and constructed a stable prognostic risk model. Kaplan-Meier curve analysis showed that the high-risk group had a poorer prognosis (P<0.05). Furthermore, the ROC analysis of 3-, 5- and 7-year survival rates confirmed that the model had good predictive value for patients with LUSC. Single-cell RNA sequencing showed the prognostic ARGS were enriched in epithelial cells, smooth muscle cells, and T cells. Pseudotime analysis was used to infer the differentiation process and time sequence of cells. Conclusions: This study identified ARGs that are associated with prognosis in LUSC, and a risk model based on these prognostic genes was constructed that could accurately predict the prognosis of LUSC. Single-cell sequencing analysis provided new insights into the cellular-level development of tumors. These findings provide more guidance for the diagnosis and treatment of patients with LUSC.

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Background: No biomarkers have been identified for the prognosis of lung squamous cell carcinoma (LUSC). Risk models based on m6A-lncRNAs help to predict survival in some cancers. However, very few studies have reported m6A-lncRNA risk models in LUSC. We aimed to construct a prognostic model based on m6A-lncRNAs in LUSC. Methods: The clinical and RNA-sequencing information of 504 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-lncRNAs were identified by a Pearson correlation analysis and univariate Cox regression analysis. The ConsensusClusterPlus algorithm was used to cluster the prognostic m6A-lncRNAs. The overall survival (OS) and clinicopathological characteristics of the 2 clusters were compared. A gene set enrichment analysis (GSEA) analysis was performed to analyze the genes enriched in the 2 clusters. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct the risk-score model. Two hundred and forty eight patients were randomly chosen from TCGA-LUSC cohort for the training set. The receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of the model. The clinical characteristics and OS in the high- and low-risk groups were compared. The independent prognostic value of the model was tested by Cox regression analyses. Results: Thirteen m6A-lncRNAs were identified as prognostic lncRNAs and classified into cluster A and cluster B. The OS of patients in cluster A was better than that of patients in cluster B (P<0.001). Patients in cluster B had higher expressions of immune checkpoints. Immune score, stromal score, and ESTIMATE score were higher in cluster B (P<0.001). Seven of the 13 lncRNAs were used to construct the risk-score model. Patients in the high-risk group had a worse OS. ROC curves showed a under the curve (AUC) of 0.639 in the training set and 0.624 in the validation set. A high risk was associated with cluster B, a high immune score, and stage III-IV disease. Patients in the high-risk group had increased expressions of immune checkpoints. The Cox regression analyses showed that the risk-score model had independent prognostic value for OS. The risk-score model retained its prognostic value in different subgroups. Conclusions: The m6A-lncRNA risk-score model is an independent prognostic factor for OS in LUSC patients. However, the risk-score model need to be further tested clinically.

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Background: Lung cancer, especially lung squamous cell carcinoma (LUSC), is one of the most common malignant tumors worldwide. Currently, radiosensitization research is a vital direction for the improvement of LUSC therapy. Long non-coding RNAs (lncRNAs) can be novel biomarkers due to their multiple functions in cancers. However, the function and mechanism of lncRNA KCNQ1OT1 in the radioresistance of LUSC remain to be elucidated. Methods: The clonogenic assay was employed to determine the radioresistance of SK-MES-1R and NCI-H226R cells. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot were conducted for the detection of gene expression. Cell proliferation was determined by the methyl thiazolyl tetrazolium (MTT) assay, colony formation assay, and 5-ethynyl-2'-deoxyuridine (EdU) staining, and cell apoptosis was assessed by flow cytometry. The relationships between genes were also evaluated by applying the luciferase reporter and radioimmunoprecipitation (RIP) assays. Results: Radioresistant LUSC cells (SK-MES-1R and NCI-H226R) had strong resistance to X-ray irradiation, and lncRNA KCNQ1OT1 was highly expressed in SK-MES-1R and NCI-H226R cells. Moreover, knockdown of lncRNA KCNQ1OT1 prominently suppressed proliferation, attenuated radioresistance, and accelerated the apoptosis of SK-MES-1R and NCI-H226R cells. More importantly, we verified that miR-491-5p was a regulatory target of lncRNA KCNQ1OT1, and Xenopus kinesin-like protein 2 (TPX2) and RING finger protein 2 (RNF2) were the target genes of miR-491-5p. The rescue experiment results also demonstrated that miR-491-5p was involved in the inhibition of cell proliferation and the downregulation of TPX2 and RNF2 expression mediated by lncRNA KCNQ1OT1 knockdown in SK-MES-1R and NCI-H226R cells. Conclusions: LncRNA KCNQ1OT1 was associated with the radioresistance of radioresistant LUSC cells, and the lncRNA KCNQ1OT1/miR-491-5p/TPX2-RNF2 axis might be used as a therapeutic target to enhance the radiosensitivity of radioresistant LUSC cells.

RESUMEN

Background: Cancer vaccines are therapies that activate the patient's own immune system by inoculating the patient with cancer-specific antigens to identify and clear cancer cells. Messenger ribonucleic acid (mRNA) vaccines have received much attention because of their ease of synthesis, relative affordability, and long-term safety, but have not been studied in lung squamous cell carcinoma (LUSC). Thus, the identification of tumor antigens is necessary to facilitate the development of mRNA vaccines for LUSC. Methods: Genes with significant copy number amplification, the presence of gene mutations in LUSC, and genes associated with survival were identified as potential tumor antigens. Subsequently, the genes significantly correlated with the level of infiltration of 3 antigen-presenting cells in LUSC were identified as LUSC tumor antigens. Unsupervised clustering and immune profiling analysis were used to identify potential suitable patients for mRNA vaccines. Lastly, drug sensitivity analysis was used to explore individualized treatment options for mRNA vaccines suitable and unsuitable patients. Results: In this study, among the highly mutated genes in LUSC, we found that bone morphogenetic protein 5 (BMP5) and claudin 5 (CLDN5) expression were positively correlated with antigen-presenting cell infiltration, which indicates that these 2 genes have potential for development as mRNA cancer vaccines. Further, the immune landscape analysis identified different immune subtypes. Our findings provide a reference for the development of treatment strategies and the prediction of treatment responsiveness. Conclusions: Overall, our study provides a new perspective on the development of mRNA vaccines for LUSC and highlights the importance of individualized therapy.