Characterization of prognostic signature related with twelve types of programmed cell death in lung squamous cell carcinoma.

OBJECTIVE: This study aimed to develop a prognostic cell death index (CDI) based on the expression of genes related with various types of programmed cell death (PCD), and to assess its clinical relevance in lung squamous cell carcinoma (LUSC). METHODS: PCD-related genes were gathered and analyzed in silico using the transcriptomic data from the LUSC cohorts of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Differentially expressed PCD genes were analyzed, and a prognostic model was subsequently constructed. CDI scores were calculated for each patient, and their correlations with clinical features, survival outcomes, tumor mutation burden, gene clusters, and tumor microenvironment were investigated. Unsupervised consensus clustering was performed based on CDI model genes. Furthermore, the correlation of CDI for sensitivity of targeted drugs, chemotherapy efficacy, and immunotherapy responses was assessed. RESULTS: Based on 351 differentially expressed PCD genes in LUSC, a CDI signature comprising FGA, GAB2, JUN, and CDKN2A was identified. High CDI scores were significantly associated with poor survival outcomes (p < 0.05). Unsupervised clustering revealed three distinct patient subsets with varying survival rates. CDKN2A exhibited significantly different mutation patterns between patients with high and low CDI scores (p < 0.01). High CDI scores were also linked to increased immune cell infiltration of specific subsets and altered expression of immune-related genes. Patients with high-CDI showed reduced sensitivity to several chemotherapeutic drugs and a higher Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating potential resistance to immunotherapy. CONCLUSION: The CDI signature based on PCD genes offers valuable prognostic insights into LUSC, reflecting molecular heterogeneity, immune microenvironment associations, and potential therapeutic challenges. The CDI holds potential clinical utility in predicting treatment responses and guiding the selection of appropriate therapies for patients with LUSC. Future studies are warranted to further validate the prognostic value of CDI in combination with clinical factors and to explore its application across diverse patient cohorts.

Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.

Objectives: The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods: PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results: The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion: Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412851, identifier CRD42023412851.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

High circulating activin A plasma levels are associated with tumour stage and poor survival in treatment-naive lung squamous cell cancer patients.

OBJECTIVES: Lung squamous cell carcinoma (LUSC) is associated with a poor prognosis and a lack of specific treatment options. The dysregulation of activin A (ActA) has been reported in various malignancies. Herein, we investigated the diagnostic and prognostic significance of ActA in LUSC. MATERIALS AND METHODS: ActA concentrations were measured using ELISA in plasma samples of 128 LUSC patients (stage I-IV) and 73 controls, and correlated those values with clinicopathological parameters and survival. RESULTS: ActA plasma levels were significantly higher in therapy-naive LUSC patients compared to controls (444.1 ± 310.9 pg/mL vs 338.9 ± 145.5 pg/mL, p = 0.010). ActA levels significantly correlated with advanced stage as well as with T and N factors. High circulating ActA levels were significantly increased in metastatic disease patients compared to M0 disease. Further, patients with ActA levels above a computationally established optimal cut-off value of 443.0 pg/mL had a significantly worse median overall (OS, 17.63 vs 64.77 months, HR 0.391, 95 % CI 0.200-0.762, p < 0.001) and median disease-/progression-free survival (DFS/PFS; 11.57 vs 30.20 months, HR 0.502, 95 % CI 0.248-1.019, p = 0.020). Multivariate analysis revealed that high ActA levels were an independent prognostic factor for shorter OS (p = 0.001) and DFS/PFS (p = 0.018). A newly developed score combining CRP and ActA levels was also an independent prognostic factor for OS and DFS/PFS. CONCLUSION: Measurement of circulating ActA levels may help identify advanced-stage LUSC patients, and this value could serve as a prognostic parameter in LUSC. Thus, ActA may be a novel blood-based biomarker for identifying LUSC patients with distant metastasis.

Case report: a case of lung squamous cell carcinoma with a novel FGFR3-IER5L fusion mutation responding to anlotinib.

Lung squamous cell carcinoma (LUSC) is the second most common pathological type of non-small cell lung cancer (NSCLC). However, compared with lung adenocarcinoma (LUAD), the incidence of driver gene mutations in LUSC is relatively lower and treatment options for LUSC patients are very limited. We described a LUSC patient with a novel FGFR3-IER5L fusion revealed by next generation sequencing in this report. The patient refused surgery, radiotherapy or chemotherapy and received anlotinib treatment. Anlotinib is a small molecular multi-target tyrosine kinase inhibitor, which can inhibit the activity of kinases including vascular endothelial growth factor receptor 2/3 (VEGFR2/3), fibroblast growth factor receptor 1-4 (FGFR1-4), platelet-derived growth factor receptor α/ß (PDGFRα/ß), and c-Kit. The patient achieved partial response and the progression-free survival was 3.8 months.

A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis.

We used a mathematical approach to investigate the quantitative spatial profile of cancer cells and stroma in lung squamous cell carcinoma tissues and its clinical relevance. The study enrolled 132 patients with 3-5 cm peripheral lung squamous cell carcinoma, resected at the National Cancer Center Hospital East. We utilized machine learning to segment cancer cells and stroma on cytokeratin AE1/3 immunohistochemistry images. Subsequently, a spatial form of Shannon's entropy was employed to precisely quantify the spatial distribution of cancer cells and stroma. This quantification index was defined as the spatial tumor-stroma distribution index (STSDI). The patients were classified as STSDI-low and -high groups for clinicopathological comparison. The STSDI showed no significant association with baseline clinicopathological features, including sex, age, pathological stage, and lymphovascular invasion. However, the STSDI-low group had significantly shorter recurrence-free survival (5-years RFS: 49.5% vs. 76.2%, p < 0.001) and disease-specific survival (5-years DSS: 53.6% vs. 81.5%, p < 0.001) than the STSDI-high group. In contrast, the application of Shannon's entropy without spatial consideration showed no correlation with patient outcomes. Moreover, low STSDI was an independent unfavorable predictor of tumor recurrence and disease-specific death (RFS; HR = 2.668, p < 0.005; DSS; HR = 3.057, p < 0.005), alongside the pathological stage. Further analysis showed a correlation between low STSDI and destructive growth patterns of cancer cells within tumors, potentially explaining the aggressive nature of STSDI-low tumors. In this study, we presented a novel approach for histological analysis of cancer tissues that revealed the prognostic significance of spatial tumor-stroma distribution in lung squamous cell carcinoma.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

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.

No genetic causal association between human papillomavirus and lung cancer risk: a bidirectional two-sample Mendelian randomization analysis.

INTRODUCTION: Several observational or retrospective studies have previously been conducted to explore the possible association between lung cancer and human papillomavirus (HPV) infection. However, there may be inconsistencies in the data and conclusions due to differences in study design and HPV testing methods. There are currently no studies that provide conclusive evidence to support the involvement of HPV in the occurrence and development of lung cancer. Therefore, the relationship between HPV and lung cancer remains controversial and uncertain. This study aimed to explore whether HPV infection is causally related to lung cancer risk by systematically performing a two-way Two-Sample Mendelian Randomization (TSMR) analysis. METHODS: In the International Lung Cancer Consortium (ILCCO) genome-wide association study dataset, we included 11,348 lung cancer (LUCA) cases, including 3275 squamous cell carcinoma (LUSC) cases, 3442 adenocarcinoma (LUAD) cases, and 15,861 cases of control. Using genetic variants associated with the HPV E7 protein as instrumental variables, we summarized statistics associated with HPV infection in the MRC IEU OpenGWAS database, which included the HPV-16 E7 protein and the HPV-18 E7 protein. Two-sample Mendelian randomization (MR) results are expressed as odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Based on a comprehensive analysis of genome-wide association study (GWAS) data from public databases, we mainly used inverse-variance weighted (IVW) to estimate causal relationships, while using MR-Egger, weighted median, simple mode, and weighted mode, and other four methods as supplements. Two-sample MR Analysis revealed no causal relationship between exposure factors (HPV-16 E7 protein and HPV-18 E7 protein) and outcome factors (lung cancer (LUCA) and its subtypes squamous cell carcinoma (LUSC) and adenocarcinoma (LUAD)) in forward MR Analysis using the IVW approach.HPV-16 E7 protein and LUCA and its subtypes LUSC and LUAD by IVW method results: [OR] = 1.002; 95% [CI]: 0.961 - 1.045; p = 0.920; [OR] = 1.023; 95% [CI]: 0.966 - 1.084; p = 0.438; [OR] = 0.994; 95% [CI]: 0.927 - 1.066; p = 0.872); HPV-18 E7 protein and LUCA and its subtypes LUSC and LUAD by IVW method results: [OR] = 0.965; 95% [CI]: 0.914 - 1.019; p = 0.197; [OR] = 0.933; 95% [CI]: 0.834 - 1.043; p = 0.222; [OR] = 1.028; 95% [CI]: 0.945 - 1.118; p = 0.524. It was observed through reverse MR that LUCA and its subtypes LUSC and LUAD were used as exposure factors, and HPV infection (HPV-16 E7 protein and HPV-18 E7 protein) was used as the outcome factors, the results of the IVW method are also invalid.LUCA and HPV-16 E7 protein and HPV-18 E7 protein by IVW method results: [OR] = 1.036; 95% [CI]: 0.761 - 1.411; p = 0.82; [OR] = 1.318; 95% [CI]: 0.949 - 1.830; p = 0.099; LUSC and HPV-16 E7 protein and HPV-18 E7 protein by IVW method results: [OR] = 1.123; 95% [CI]0.847 - 1.489; p = 0.421; [OR] = 0.931; 95% [CI]: 0.660 - 1.313; p = 0.682; LUAD and HPV-16 E7 protein and HPV-18 E7 protein by IVW method results: [OR] = 1.182; 95% [CI] 0.983 - 1.421; p = 0.075; [OR] = 1.017; 95% [CI]: 0.817 - 1.267; p = 0.877.Our results indicate that there is no causal relationship between genetically predicted HPV infection and LUCA and its subtypes LUSC and LUAD. In addition, in the reverse MR analysis, we did not observe a significant causal relationship between LUCA and its subtypes LUSC and LUAD on HPV infection. CONCLUSIONS: Our findings do not support a genetic association between HPV infection and lung cancer.

Wee1 inhibitor PD0166285 sensitized TP53 mutant lung squamous cell carcinoma to cisplatin via STAT1.

BACKGROUND: Lung squamous cell carcinoma (LUSCs) is associated with high mortality (20-30%) and lacks of effective treatments. Almost all LUSC exhibit somatic mutations in TP53. Wee1, a tyrosine kinase, regulates the cell cycle at the G2/M checkpoint. In TP53-deficient cells, the dependence on G2/M checkpoints increases. PD0166285 is the first reported drug with inhibitory activity against both Wee1 and PKMYT1. METHODS: Protein expression was determined by Western blot analysis. Cell proliferation was assessed using cell colony formation and CCK-8 assays. Cell cycle was performed by PI staining with flow cytometry. Apoptosis was evaluated using Annexin V-Phycoerythrin double staining and flow cytometry. DNA damage was detected through comet assay and immunofluorescence assay. In vivo, apoptosis and anti-tumor effects were assessed using the TUNEL assay, a nude mouse model, and immunohistochemistry (IHC). Co-immunoprecipitation assay was used to detect protein-protein interactions. We analyzed Wee1, PKMYT1, and Stat1 expression in pan-cancer studies using the Ualcan public database and assessed their prognostic implications with Kaplan-Meier curves. RESULT: PD0166285, a Wee1 inhibitor, effectively inhibits Wee1 activity, promoting cell entry into a mitotic crisis. Moreover, PD0166285 sensitizes cells to cisplatin, enhancing clinical outcomes. Our study demonstrated that PD016628 regulates the cell cycle through Rad51 and results in cell cycle arrest at the G2/M phase. We observed increased apoptosis in tumor cells treated with PD0166285, particularly when combined with cisplatin, indicating an enhanced apoptotic response. The upregulation of γ-H2AX serves as an indicator of mitotic catastrophe. Co-immunoprecipitation and data analysis revealed that apoptosis in LUSC is mediated through the Stat1 pathway, accompanied by decreased levels of Socs3. Furthermore, IHC staining confirmed significant differences in the expression of Phospho-CDK1 and γ-H2AX in LUSCs, suggesting involvement in DNA damage. CONCLUSIONS: In summary, our study suggests that PD0166285, an inhibitor of Wee1, sensitizes LUSC cells to cisplatin and modulates DNA damage and apoptosis pathways through Rad51 and Stat1, respectively. These findings highlight the combination of PD0166285 and cisplatin as a promising therapeutic approach for treating LUSC.

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.

SNAI1: a key modulator of survival in lung squamous cell carcinoma and its association with metastasis.

BACKGROUND: Snail family zinc finger 1 (SNAI1) has been implicated in cancer progression and prognosis across various malignancies. This study aims to elucidate the prognostic significance of SNAI1 expression in Lung Squamous Cell Carcinoma (LUSC) using data from The Cancer Genome Atlas (TCGA) database. METHODS: SNAI1 expression levels in LUSC patients were stratified using X-tile software to establish optimal cut-off values. Kaplan-Meier survival analysis was performed to assess the impact of SNAI1 expression on overall survival (OS). Univariate and multivariate Cox regression analyses were conducted to evaluate the prognostic value of SNAI1, considering clinical parameters such as age, clinical stage, and TNM classification. Additionally, we explored the interaction between SNAI1 expression and metastatic status, and performed Gene Set Enrichment Analysis (GSEA) to investigate associated cellular pathways. Correlations between SNAI1 and immune checkpoint molecules were also examined. RESULTS: Kaplan-Meier analysis revealed significant differences in OS among high, medium, and low SNAI1 expression groups (p < 0.001), with median survival times of 1.6, 3.0, and 5.8 years, respectively. Dichotomizing patients into high and low SNAI1 expression groups confirmed that high SNAI1 expression was associated with significantly poorer OS (p < 0.001). SNAI1 remained an independent prognostic factor in multivariate analysis. High SNAI1 expression correlated with poorer survival outcomes regardless of metastatic status, and the combination of high SNAI1 expression and metastasis resulted in the poorest survival. GSEA identified significant associations between SNAI1 and inflammatory, immune response pathways. Positive correlations were observed between SNAI1 and key immune checkpoint molecules, suggesting an interplay with immune checkpoint mechanisms. CONCLUSIONS: High SNAI1 expression is a robust prognostic indicator of poor survival in LUSC, independent of other clinical factors. Its association with immune checkpoint molecules highlights its potential as a therapeutic target. These findings underscore the prognostic and therapeutic relevance of SNAI1 in LUSC and possibly other cancers. Further research is warranted to explore targeted therapies against SNAI1.

Ultrasound-mediated nanobubbles loaded with STAT6 siRNA inhibit TGF-ß1-EMT axis in LUSC cells via overcoming the polarization of M2-TAMs.

M2-like tumor-associated macrophages (M2-TAMs) are closely correlated with metastasis and poor clinical outcomes in lung squamous cell carcinoma (LUSC). Previous studies have demonstrated that STAT6 is an important signaling molecule involved in the polarization of M2-TAMs, EMT is the main way for TAMs to promote tumor progression. However, little attention has been paid to the effect of STAT6 inhibition on LUSC, and it is difficult to achieve an ideal gene silencing effect in immune cells using traditional gene transfection methods. Here, we investigated the optimal concentration of 12-myristic 13-acetate (PMA), lipopolysaccharide (LPS) for the induction of THP-1 into M1-TAMs and M2-TAMs. The expression of pSTAT6 and STAT6 was confirmed in three types of macrophages, and it was demonstrated that pSTAT6 can be used as a specific target of M2-TAMs derived from THP-1. Ultrasound-mediated nanobubble destruction (UMND) is a non-invasive and safe gene delivery technology. We also synthesized PLGA-PEI nanobubbles (NBs) to load and deliver STAT6 small interfering RNA (siRNA) into M2-TAMs via UMND. The results show that the NBs could effectively load with siRNA and had good biocompatibility. We found that UMND enhanced the transfection efficiency of siRNA, as well as the silencing effect of pSTAT6 and the inhibition of M2-TAMs. Simultaneously, when STAT6 siRNA entered M2-TAMs by UMND, proliferation, migration, invasion and EMT in LUSC cells could be inhibited via the transforming growth factor-ß1 (TGF-ß1) pathway. Therefore, our results confirm that UMND is an ideal siRNA delivery strategy, revealing its potential to inhibit M2-TAMs polarization and ultimately treat LUSC.

STAT3 inhibitor Stattic Exhibits the Synergistic Effect with FGFRs Inhibitor Erdafitinib in FGFR1-positive Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC), a subset of non-small cell lung cancer (NSCLC), accounts for about 30% of all lung cancers (LC) and exhibits a dismal response to current therapeutic protocols. Existed studies have indicated that aberrations in fibroblast growth factor receptors (FGFRs) play a pivotal role in the progression of LUSC, rendering them as attractive targets for therapeutic intervention in this cancer type. This study found that Erdafitinib (Erda), a novel pan-FGF receptor tyrosine kinase inhibitor (TKI), exerted a cytotoxic effect on LUSC cells. However, STAT3, the downstream target of FGFRs, remained still activated despite Erdafitinib treatment. Then, a STAT3 inhibitor, Stattic (Sta), was concurrently used with Erdafitinib, and the combined treatment demonstrated a synergistic efficacy in both in vitro and in vivo models of LUSC when compared to that of the treatment of the Erdafitinib or Stattic alone. Further molecular studies showed that such an effect of Erdafitinib and Stattic was associated with their concurrently inhibitory effect on FGFR1 and STAT3 signaling in LUSC cells. Therefore, the findings of this study indicated that the concurrent use of Erdafitinib and Stattic is a promising therapeutic approach for the treatment of FGFR1-positive LUSC.

Pyroptosis-related gene GSDMC indicates poor prognosis and promotes tumor progression by activating the AKT/mTOR pathway in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is one of the most common malignant tumors of the respiratory. Pyroptosis plays an essential role in cancer, but there is limited research investigating pyroptosis in LUSC. In this study, pyroptosis-related genes were observed to have extensive multiomics alterations in LUSC through analysis of the TCGA database. Utilizing machine learning for selection and verifying expression levels, GSDMC was chosen as the critical gene for further experiments. Our research found that GSDMC is overexpressed in LUSC tissues and cells, and is associated with poor prognosis. Knockdown of GSDMC in LUSC inhibits cell proliferation, invasion, metastasis, chemotherapeutic sensitivity, and reduced tumor formation in nude mice, accompanied by downregulation of proliferative and EMT-related protein expression. However, these effects were counteracted in cells where GSDMC is overexpressed. Mechanistically, the oncogenic role of GSDMC is primarily achieved through the activation of the AKT/mTOR pathway, and this effect can be significantly reversed by rapamycin. Finally, SMAD4's interaction with the promoter region of GSDMC results in the suppression of GSDMC expression. In summary, our study through bioinformatics and experimental approaches not only proves that SMAD4 regulates the protumorigenic role of GSDMC through transcriptional targeting, but also indicates the possibility of developing the SMAD4/GSDMC/AKT/mTOR signaling axis as a potential biomarker and treatment target for LUSC.

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.

Plasma extracellular vesicle long RNA profiling identifies a predictive signature for immunochemotherapy efficacy in lung squamous cell carcinoma.

Introduction: The introduction of Immune Checkpoint Inhibitors (ICIs) has marked a paradigm shift in treating Lung Squamous Cell Carcinoma (LUSC), emphasizing the urgent need for precise molecular biomarkers to reliably forecast therapeutic efficacy. This study aims to identify potential biomarkers for immunochemotherapy efficacy by focusing on plasma extracellular vesicle (EV)-derived long RNAs (exLRs). Methods: We enrolled 78 advanced LUSC patients undergoing first-line immunochemotherapy. Plasma samples were collected, and exLR sequencing was conducted to establish baseline profiles. A retrospective analysis was performed on 42 patients to identify differentially expressed exLRs. Further validation of the top differentially expressed exLRs was conducted using quantitative reverse transcription PCR (qRT-PCR). Univariate Cox analysis was applied to determine the prognostic significance of these exLRs. Based on these findings, we developed a predictive signature (p-Signature). Results: In the retrospective analysis of 42 patients, we identified 460 differentially expressed exLRs, with pathways related to leukocyte migration notably enriched among non-responders. Univariate Cox analysis revealed 45 exLRs with prognostic significance. The top 6 protein-coding exLRs were validated using qRT-PCR, identifying CXCL8, SSH3, and SDHAF1 as differentially expressed between responders and non-responders. The p-Signature, comprising these three exLRs, demonstrated high accuracy in distinguishing responders from non-responders, with an Area Under the Curve (AUC) of 0.904 in the retrospective cohort and 0.812 in the prospective cohort. Discussion: This study highlighted the potential of plasma exLR profiles in predicting LUSC treatment efficacy. Intriguingly, lower p-Signature scores were associated with increased abundance of activated CD4+ and CD8+ T cells, indicating a more robust immune environment. These findings suggest that the p-Signature could serve as a valuable tool in guiding personalized and effective therapeutic strategies for LUSC.

Unveiling potential drug targets for lung squamous cell carcinoma through the integration of druggable genome and genome-wide association data.

Background: Lung squamous cell carcinoma (LSCC) is a major subtype of lung cancer with poor prognosis and low survival rate. Compared with lung adenocarcinoma, yet no FDA-approved targeted-therapy has been found for lung squamous cell carcinoma. Methods: To identify potential drug targets for LSCC, Summary-data-based Mendelian randomization (SMR) analysis was used to examine the potential association between 4,543 druggable genes and LSCC, followed by colocalization analysis and HEIDI tests to confirm the robustness of the result. Phenome-wide association study (PheWAS) explored potential side effects of candidate drug targets. Enrichment analysis and protein-protein interaction networks revealed the function and significance of therapeutic targets. Single-cell expression analysis was used to examine cell types with enrichment expression of druggable genes in LSCC tissue. Drug prediction included screening potential drug candidates and evaluating their interactions with targets through molecular docking. Results: This research has identified ten significant drug targets for LSCC through a comprehensive SMR analysis. These targets included (COPA, PKD2L1, CCR1, C2, CYP21A2, and NCSTN as risk factors, and CCNA2, C4A, APOM, and LPAR2 as protective factors). PheWAS demonstrated that C2, CCNA2, LPAR2, and NCSTN exhibited associations with other phenotypes at the genetic level. Then, we found four potentially effective drugs with the Dsigdb database. Subsequently, molecular docking indicated that favorable binding interactions between drug candidates and potential target molecules. In the druggability evaluation, five out of ten drug target genes have been used in drug development (APOM, C4A, CCNA2, COPA, and PKD2L1). Six out of ten druggable genes showed significant expression in LSCC tissues (COPA, PKD2L1, CCR1, C2, NCSTN, LPAR2). Besides, Single-cell expression analysis revealed that C2 and CCNA2 were primarily enriched in macrophages, while COPA and NCSTN were enriched in both macrophages and epithelial cells. Conclusion: Our research revealed ten potential druggable genes for LSCC treatment, which might help to advance the precise and efficient therapeutic approaches of LSCC.

Pretreatment pulmonary tumor necrosis is a promising prognostic imaging biomarker for first-line anti-PD-1/PD-L1 therapy in advanced lung squamous cell carcinoma: a multi-institutional, propensity score-matching cohort analysis.

Background: Tumor necrosis (TN) is a common feature in lung squamous cell carcinoma (LSCC), which could provide useful predictive and prognostic information. Objectives: This study aimed to investigate the effect of pretreatment pulmonary TN (PTN) on the prognosis of first-line anti-programmed cell death 1 (PD-1)/PD ligand 1 (PD-L1) inhibitor in advanced LSCC. Design: We conducted a retrospective study to analyze the association between the presence of PTN and clinical outcomes in advanced LSCC patients treated with anti-PD-1/PD-L1 inhibitors. Methods: Data from 240 eligible patients were collected from 27 hospitals across China between 2016 and 2020. The presence of PTN was assessed using contrast-enhanced chest computed tomography (CT) imaging at baseline. We utilized the Cox proportional-hazards regression model to analyze the association between PTN and clinical outcomes. In addition, to account for potential confounding factors and ensure comparability between groups, we employed propensity score-matching (PSM) analysis. Results: In the overall patient cohort, the presence of PTN was 39.6%. The median follow-up duration was 20.3 months. The positive PTN group exhibited a notably inferior median progression-free survival (PFS; 6.5 months vs 8.6 months, p = 0.012) compared to the negative PTN group. Within the Cox proportional-hazards regression model, PTN emerged as an independent predictor of unfavorable PFS (hazard ratio (HR) = 1.354, 95% confidence interval (CI): 1.002-1.830, p = 0.049). After PSM, the median PFS for the positive PTN group (6.5 months vs 8.0 months, p = 0.027) remained worse than that of the negative PTN group. Multivariate analyses also further underscored that the presence of PTN independently posed a risk for shorter PFS (HR = 1.494, 95% CI: 1.056-2.112, p = 0.023). However, no statistically significant difference in overall survival was observed between the two groups. Conclusion: Our study suggests that the presence of PTN on baseline contrast-enhanced chest CT is a potential negative prognostic imaging biomarker for the outcome of anti-PD-1/PD-L1 inhibitor therapy in advanced LSCC. Further studies are warranted to validate these findings and explore the underlying mechanisms.

Predicting anti-PD-1/PD-L1 inhibitor treatment outcomes: pulmonary tumor necrosis in lung squamous cell carcinoma Our study focused on lung squamous cell carcinoma (LSCC) patients receiving first-line anti-PD-1/PD-L1 therapy. We explored the impact of a feature called pretreatment pulmonary tumor necrosis (PTN) on their prognosis. PTN was identified in 39.6% of patients using baseline chest CT scans. Results revealed that patients with PTN had a shorter time without disease progression (median PFS of 6.5 months compared to 8.6 months) and a higher risk of unfavorable outcomes. This suggests that PTN may serve as a negative prognostic imaging marker for anti-PD-1/PD-L1 therapy in advanced LSCC. Further research is needed to confirm and understand these findings better.

Primary Lung Squamous Cell Carcinoma With Intestinal Metastasis: A Case Report and Literature Review.

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.

Single-cell transcriptome analysis deciphers the CD74-mediated immune evasion and tumour growth in lung squamous cell carcinoma with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune-inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single-cell level. METHODS: We performed single-cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co-culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single-cell analyses. RESULTS: LSCC with COPD showed increased proportions of tumour-associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-programmed cell death-1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion. CONCLUSIONS: Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy. HIGHLIGHT: Our results demonstrated higher proportions of tumour-associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-PD-L1 signalling pathway, facilitating immune evasion.