Lung Squamous Cell Carcinoma Patients Research Articles

Global epidemiologic patterns of oropharyngeal cancer incidence trends.

The emergence of human papillomavirus (HPV)-positive oropharyngeal cancer and evolving tobacco use patterns have changed the landscape of head and neck cancer epidemiology internationally. We investigated updated trends in oropharyngeal cancer incidence worldwide. We analyzed cancer incidence data between 1993 and 2012 from 42 countries using the Cancer Incidence in Five Continents database volumes V through XI. Trends in oropharyngeal cancer incidence were compared with oral cavity cancers and lung squamous cell carcinomas using log-linear regression and age period-cohort modeling. In total, 156 567 oropharyngeal cancer, 146 693 oral cavity cancer, and 621 947 lung squamous cell carcinoma patients were included. Oropharyngeal cancer incidence increased (P < .05) in 19 and 23 countries in men and women, respectively. In countries with increasing male oropharyngeal cancer incidence, all but 1 had statistically significant decreases in lung squamous cell carcinoma incidence, and all but 2 had decreasing or nonsignificant net drifts for oral cavity cancer. Increased oropharyngeal cancer incidence was observed both in middle-aged (40-59 years) and older (≥60 years) male cohorts, with strong nonlinear birth cohort effects. In 20 countries where oropharyngeal cancer incidence increased for women and age period-cohort analysis was possible, 13 had negative or nonsignificant lung squamous cell carcinoma net drifts, including 4 countries with higher oropharyngeal cancer net drifts vs both lung squamous cell carcinoma and oral cavity cancer (P < .05 for all comparisons). Increasing oropharyngeal cancer incidence is seen among an expanding array of countries worldwide. In men, increased oropharyngeal cancer is extending to older age groups, likely driven by human papillomavirus-related birth cohort effects. In women, more diverse patterns were observed, suggesting a complex interplay of risks factors varying by country, including several countries where female oropharyngeal cancer increases may be driven by HPV.

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.

Ethnicity-specific association between TERT rs2736100 (A > C) polymorphism and lung cancer risk: a comprehensive meta-analysis

The rs2736100 (A > C) polymorphism of the second intron of Telomerase reverse transcriptase (TERT) has been confirmed to be closely associated with the risk of Lung cancer (LC), but there is still no unified conclusion on the results of its association with LC. This study included Genome-wide association studies (GWAS) and case–control studies reported so far on this association between TERT rs2736100 polymorphism and LC to clarify such a correlation with LC and the differences in it between different ethnicities and different types of LC. Relevant literatures published before May 7, 2022 on ‘TERT rs2736100 polymorphism and LC susceptibility’ in PubMed, EMbase, CENTRAL, MEDLINE databases were searched through the Internet, and data were extracted. Statistical analysis of data was performed in Revman5.3 software, including drawing forest diagrams, drawing funnel diagrams and so on. Sensitivity and publication bias analysis were performed in Stata 12.0 software. The C allele of TERT rs2736100 was associated with the risk of LC (Overall population: [OR] = 1.21, 95%CI [1.17, 1.25]; Caucasians: [OR] = 1.11, 95%CI [1.06, 1.17]; Asians: [OR] = 1.26, 95%CI [1.21, 1.30]), and Asians had a higher risk of LC than Caucasians (C vs. A: Caucasians: [OR] = 1.11 /Asians: [OR]) = 1.26). The other gene models also showed similar results. The results of stratified analysis of LC patients showed that the C allele was associated with the risk of Non-small-cell lung carcinoma (NSCLC) and Lung adenocarcinoma (LUAD), and the risk of NSCLC and LUAD in Asians was higher than that in Caucasians. The C allele was associated with the risk of Lung squamous cell carcinoma (LUSC) and Small cell lung carcinoma(SCLC) in Asians but not in Caucasians. NSCLC patients ([OR] = 1.27) had a stronger correlation than SCLC patients ([OR] = 1.03), and LUAD patients ([OR] = 1.32) had a stronger correlation than LUSC patients ([OR] = 1.09).In addition, the C allele of TERT rs2736100 was associated with the risk of LC, NSCLC and LUAD in both smoking groups and non-smoking groups, and the risk of LC in non-smokers of different ethnic groups was higher than that in smokers. In the Asians, non-smoking women were more at risk of developing LUAD. The C allele of TERT rs2736100 is a risk factor for LC, NSCLC, and LUAD in different ethnic groups, and the Asian population is at a more common risk. The C allele is a risk factor for LUSC and SCLC in Asians but not in Caucasians. And smoking is not the most critical factor that causes variation in TERT rs2736100 to increase the risk of most LC (NSCLC, LUAD). Therefore, LC is a multi-etiological disease caused by a combination of genetic, environmental and lifestyle factors.

Proposal of a grading system for squamous cell carcinoma of the lung - the prognostic importance of tumour budding, single cell invasion, and nuclear diameter.

The prognostic markers of lung squamous cell carcinoma (LSCC) are less investigated. The aim of our study was to evaluate tumour budding (TB), minimal cell nest size, nuclear diameter (ND), and spread through air spaces (STAS) among patients with resected LSCC, semi-quantitatively. Furthermore, we aimed to identify a grading system for the best prognostic stratification of LSCC. Patients who underwent surgical resection at the Department of Surgery, University of Szeged between 2010 and 2016 were included. Follow-up data were collected from medical charts. Morphological characteristics were recorded from histologic revision of slides. Kaplan-Meier analysis, log rank test and Cox proportional-hazards model, ROC curve analysis, and intraclass correlation were utilised. Altogether 220 patients were included. In univariate analysis, higher degree of TB, infiltrative tumour border, larger ND, the presence of single cell invasion (SCI) and STAS were associated with adverse prognosis. Based on our results, we proposed an easily applicable grading scheme focusing on TB, ND, and SCI. In multivariate analysis, the proposed grading system (pOS< 0.001, pRFS< 0.001) and STAS (pOS= 0.008, pRFS< 0.001) were independent prognosticators. Compared to the previously introduced grading systems, ROC curve analysis revealed that the proposed grade had the highest AUC values (AUCOS: 0.83, AUCRFS: 0.78). Each category of the proposed grading system has good (ICC: 0.79-0.88) reproducibility. We validated the prognostic impact of TB, SCI, ND, and STAS in LSCC. We recommend a reproducible grading system combining TB, SCI, and ND for proper prognostic stratification of LSCC patients. Further research is required for validation of this grading scheme.

Tumor suppressor PTEN regulation by tobacco smoke in lung squamous-cell carcinoma based on bioinformatics analysis

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN), is a tumor suppressor inactivated in a variety of human cancers. PTEN alteration correlates with lung squamous-cell carcinoma (LUSC) histology. However, it is still unclear how tobacco smoke regulates PTEN in LUSC tissues. In this study, we used free online databases and online tools to analyze PTEN expression and the role of smoking on PTEN alteration in patients with LUSC. We validated bioinformatics data by performing RT-PCR analysis using LUSC patient samples. Our results showed a correlation between the downregulation of PTEN in LUSC tissues compared to normal tissues and smoking exposure. In silico results using online platforms suggest that hsa-mir-301a down-regulates PTEN expression level in smoking patients with LUSC. RT-PCR analysis demonstrated that the PTEN expression was significantly decreased, whereas expression of hsa-mir-301a was up-regulated in the smoker cohort of LUSC tissue compared to adjacent non-cancerous tissues. A significant negative correlation between PTEN and hsa-mir-301a levels was observed in tumour tissues in our cohort of LUSC patients. Our results suggest that the downregulation PTEN gene caused by tobacco smoke-mediated increase of hsa-mir-301a may play an important role in LUSC tumorigenesis.

Hyper-methylation of ABCG1 as an epigenetics biomarker in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most prevalent histological type of lung cancer and the leading cause of death globally. Patients with NSCLC have a poor prognosis for various factors, and a late diagnosis is one of them. The DNA methylation of CpG island sequences found in the promoter regions of tumor suppressor genes has recently received attention as a potential biomarker of human cancer. In this study, we report DNA methylation changes of the adenosine triphosphate (ATP)-binding cassette transporter G1 (ABCG1), which belongs to the ATP cassette transporter family in NSCLC patients. Our results demonstrate that ABCG1 is hyper-methylation in NSCLC samples, and these changes are negatively correlated to gene and protein expression. Furthermore, the expression of the ABCG1 gene is significantly associated with the survival time of lung adenocarcinoma (LUAD) patients; however, it did not show a correlation to overall survival (OS) of lung squamous cell carcinoma (LUSC) patients. Notably, we found ABCG1 methylation status at locus cg20214535 is strongly associated with the survival time and consistently observed hyper-methylation in LUAD samples. This novel finding suggests ABCG1 is a potential candidate for targeted therapy in lung cancer via this specific probe. In addition, we illustrate the protein-protein interaction (PPI) of ABCG1 with other proteins and the strong communication of ABCG1 with immune cells.

Downregulation of VRK1 Inhibits Progression of Lung Squamous Cell Carcinoma through DNA Damage.

Lung squamous cell carcinoma (LUSC) is a common malignancy. And the antitumor effect of bovine pox virus-associated kinase 1 (VRK1) is becoming a hot research topic. VRK1 expression and prognosis in LUSC were analyzed using the GEPIA database. The expression of VRK1 mRNA was detected in 25 LUSC clinical tissue samples by RT-PCR. VRK1 shRNA was transfected into LUSC NCI-H520 and SK-MES-1 cell lines to interfere with VRK1 expression, and the efficiency of VRK1 shRNA interference was detected by the western blot. The effects of VRK1 downregulation on LUSC cell viability, migration, cell cycle, and apoptosis were analyzed by the CCK8 assay, scratch assay, transwell assay, and flow cytometry. The effect of VRK1 downregulation on DNA damage response (DDR) was examined by immunofluorescence staining and western blot assays and further validated by in vivo experiments. VRK1 was highly expressed in both LUSC tissues and cells. Survival analysis showed that the overall survival of LUSC patients with high VRK1 expression was significantly lower than that of LUSC patients with low VRK1 expression (P=0.0026). The expression level of the VRK1 gene was significantly higher in cancer tissues of LUSC patients than in paracancerous tissues. After transfection of VRK1 shRNA in both LUSC cells, cell activity decreased (P < 0.001), migration ability started to be inhibited (P < 0.001), the ratio of G0/G1 phase cells increased (P < 0.001), and apoptosis rate increased (P < 0.001). Immunofluorescence and western blot results showed that shVRK1 increased the level of γ-H2A.X (P < 0.001) and promoted apoptosis of tumor cells (P < 0.001). In addition, the results of animal experiments showed that shVRK1 had antitumor effects (P < 0.001) and a combined effect with DOX (P < 0.001). The downregulation of VRK1 significantly affected the proliferation, apoptosis, migration, and cell cycle progression of LUSC cells via DDR, suggesting that VRK1 is a suitable target for potential LUSC therapy.

Prognostic impact of metastatic patterns and treatment modalities on overall survival in lung squamous cell carcinoma: A population-based study.

This study aimed to investigate the impact of distinct metastasis patterns on the overall survival (OS) of individuals diagnosed with organ metastatic lung squamous cell carcinoma (LUSC). OS was calculated using the Kaplan-Meier method, and univariate and multivariate Cox regression analyses were conducted to further assess prognostic factors. A total of 36,025 cases meeting the specified criteria were extracted from the Surveillance, Epidemiology, and End Results database. Among these patients, 30.60% (11,023/36,025) were initially diagnosed at stage IV, and 22.03% (7936/36,025) of these individuals exhibited metastasis in at least 1 organ, including the liver, bone, lung, and brain. Among the 4 types of single metastasis, patients with bone metastasis had the lowest mean OS, at 9.438 months (95% CI: 8.684-10.192). Furthermore, among patients with dual-organ metastases, those with both brain and liver metastases had the shortest mean OS, at 5.523 months (95% CI: 3.762-7.285). Multivariate Cox regression analysis revealed that metastatic site is an independent prognostic factor for OS in patients with single and dual-organ metastases. Chemotherapy was beneficial for patients with single and multiple-organ metastases; although surgery was advantageous for those with single and dual-organ metastases, it did not affect the long-term prognosis of patients with triple organ metastases. Radiotherapy only conferred benefits to patients with single-organ metastasis. LUSC patients exhibit a high incidence of metastasis at the time of initial diagnosis, with significant differences in long-term survival among patients with different patterns of metastasis. Among single-organ metastasis cases, lung metastasis is the most frequent and is associated with the longest mean OS. Regarding treatment options, patients with single-organ metastasis can benefit from chemotherapy, surgery, and radiotherapy, and those with metastasis in 2 organs can benefit from chemotherapy and surgery. Patients with metastasis in more than 2 organs, however, can only benefit from chemotherapy. Understanding the variations in metastasis patterns assists in guiding pretreatment assessments and in determining appropriate therapeutic interventions for LUSC.

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.

Characterization of platelet-related genes and constructing signature combined with immune-related genes for predicting outcomes and immunotherapy response in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a highly malignant subtype of non-small cell lung cancer with poor prognosis. Platelets are known to play a critical role in cancer development and progression, and recent studies suggest that they can also regulate immune response in tumors. However, the relationship between platelet-related genes (PRGs) and LUSC prognosis and tumor microenvironments remains unclear. In this study, we used multiple bioinformatics algorithms to identify 25 dysregulated PRGs that were significantly associated with LUSC prognosis. We found that PRGs were involved in multiple biological processes, particularly in the tumor microenvironment, and that platelet-related scores (PRS) were a risk factor. Additionally, we established a 6-gene prognostic signature combining PRGs and immune-related genes that accurately predicted outcomes and immunotherapy efficacy in LUSC patients. Our study provides a comprehensive analysis of the biological functions and potential therapeutic targets of PRGs in LUSC, which may inform the development of new treatments for this disease.

Targeting ITGB4/SOX2-driven lung cancer stem cells using proteasome inhibitors

This study investigates the role of integrin β4 (ITGB4) and stemness-associated factor SOX2 in platinum resistance in lung squamous cell carcinoma (LUSC). The expression of SOX2 and ITGB4 is found to be high in all LUSC subtypes, but the impact of ITGB4 expression on overall patient survival varies by subtype. Cancer stem cells (CSCs) isolated from LUSC patients were found to be resistant to cisplatin, but knocking down ITGB4 or SOX2 sensitized them to cisplatin. Carfilzomib (CFZ) synergized with cisplatin and suppressed CSC growth by inhibiting ITGB4 and SOX2 expression. Additionally, CFZ was found to inhibit SOX2 expression epigenetically by inhibiting histone acetylation at the SOX2 promoter site. CFZ also suppressed the growth of SOX2-dependent small cell lung cancer cells invitro and invivo. The study highlights the unique function of CFZ as a transcriptional suppressor of SOX2, independent of its proteasome inhibitory function.

Involvement of CHRNA6 in the Immune Response in Lung Squamous Cell Carcinoma and its Potential as a Drug Target for the Disease.

Lung squamous cell carcinoma (LUSC) is a subtype of lung cancer with a poor prognosis and limited treatment options. Previous studies show that some components of the cholinergic pathway may play important roles in the tumorigenesis of lung cancer, including LUSC. The purpose of this study is to investigate the involvement of cholinergic genes in immune infiltration in LUSC, and identify the key genes in the pathway and analyze their potential as targets for LUSC treatment and novel drugs. We first screened the cholinergic genes associated with immune infiltration in LUSC based on transcriptomic samples and explored the correlation between the key genes and immune infiltrating cells and immune pathways. Then, we assessed the effect of immunotherapeutic response in the high and low-expression groups of key genes in vitro. And finally, we screened potential drugs for the treatment of LUSC. We found that the expression of CHRNA6, the gene encoding the α6 subunit of nicotinic acetylcholine receptors (nAChR), was significantly correlated with the proportion of immune infiltrating cells in LUSC, and the high expression level of the gene was associated with poor prognosis of the disease. Also, the proportion of Tregs, M1 macrophages, and resting mast cells was correlated with the expression of CHRNA6. In addition, LUSC patients with higher CHRNA6 expression levels had better immunotherapy responses. Furthermore, we found that the drugs, i.e., adavosertib, varbulin and pyrazoloacridine, had a strong affinity with CHRNA6, with adavosertib binding most stably with the protein. CHRNA6 may be associated with immune infiltration in LUSC and affects patient prognosis and immunotherapeutic response by regulating immune cells and immune pathways. In addition, adavosertib may be a potential drug for the treatment of LUSC.

Post-therapeutic circulating tumor cell-associated white blood cell clusters predict poor survival in patients with advanced driver gene-negative non-small cell lung cancer

PurposeThis study aimed to investigate the clinical utility of diverse aneuploid circulating tumor cell (CTC) subtypes and particularly CTC-associated white blood cell (CTC-WBC) clusters in predicting treatment response, prognosis and real-time monitoring disease progression in advanced driver gene-negative non-small lung cancer (NSCLC) patients.Materials and methodsA total of 74 eligible patients were prospectively enrolled and serial blood samples were collected at pre-treatment(t0), after two cycles of therapy (t1) and at post-four-to-six treatment cycles (t2). Co-detection of diverse subtypes of aneuploid CTCs and CTC-WBC clusters was conducted in advanced NSCLC patients receiving first-line treatment.ResultsAt baseline, CTCs were detected in 69 (93.24%) patients and CTC-WBC clusters were detected in 23 (31.08%) patients. Patients with CTCs < 5/6ml or with CTC-WBC clusters undetectable exhibited a better treatment response than patients with pre-therapeutic aneuploid CTCs ≥ 5/6ml or harboring CTC-WBC clusters (p = 0.034 and p = 0.012, respectively). Before treatment, patients bearing tetraploid CTCs ≥ 1/6ml showed significantly inferior progression-free survival (PFS) [hazard ratio (HR):2.420, 95% confidence interval (CI): 1.426–4.106; p = 0.001] and overall survival (OS) compared to patients with tetraploid CTCs < 1/6ml (HR:1.907, 95%CI: 1.119–3.251; p = 0.018). A longitudinal study demonstrated that post-therapeutic patients harboring CTC-WBC clusters displayed the reduced PFS and OS compared with those without CTC-WBC clusters, and subgroup analysis showed that the presence of CTC-WBC clusters indicated a worse prognosis in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. After adjusting for multiple significant factors, post-therapeutic CTC-WBC clusters were the only independent predictor of both PFS (HR:2.872, 95% CI: 1.539–5.368; p = 0.001) and OS (HR:2.162, 95% CI: 1.168–4.003; p = 0.014).ConclusionsIn addition to CTCs, longitudinal detection of CTC-WBC clusters provided a feasible tool to indicate initial treatment response, dynamically monitor disease progression and predict survival in driver gene-negative advanced NSCLC patients.

Quantitative proteomics identifies tumour matrisome signatures in patients with non-small cell lung cancer.

The composition and remodelling of the extracellular matrix (ECM) are important factors in the development and progression of cancers, and the ECM is implicated in promoting tumour growth and restricting anti-tumour therapies through multiple mechanisms. The characterisation of differences in ECM composition between normal and diseased tissues may aid in identifying novel diagnostic markers, prognostic indicators and therapeutic targets for drug development. Using tissue from non-small cell lung cancer (NSCLC) patients undergoing curative intent surgery, we characterised quantitative tumour-specific ECM proteome signatures by mass spectrometry. We identified 161 matrisome proteins differentially regulated between tumour tissue and nearby non-malignant lung tissue, and we defined a collagen hydroxylation functional protein network that is enriched in the lung tumour microenvironment. We validated two novel putative extracellular markers of NSCLC, the collagen cross-linking enzyme peroxidasin and a disintegrin and metalloproteinase with thrombospondin motifs 16 (ADAMTS16), for discrimination of malignant and non-malignant lung tissue. These proteins were up-regulated in lung tumour samples, and high PXDN and ADAMTS16 gene expression was associated with shorter survival of lung adenocarcinoma and squamous cell carcinoma patients, respectively. These data chart extensive remodelling of the lung extracellular niche and reveal tumour matrisome signatures in human NSCLC.

Characterization of stemness features and construction of a stemness subtype classifier to predict survival and treatment responses in lung squamous cell carcinoma

BackgroundCancer stemness has been proven to affect tumorigenesis, metastasis, and drug resistance in various cancers, including lung squamous cell carcinoma (LUSC). We intended to develop a clinically applicable stemness subtype classifier that could assist physicians in predicting patient prognosis and treatment response.MethodsThis study collected RNA-seq data from TCGA and GEO databases to calculate transcriptional stemness indices (mRNAsi) using the one-class logistic regression machine learning algorithm. Unsupervised consensus clustering was conducted to identify a stemness-based classification. Immune infiltration analysis (ESTIMATE and ssGSEA algorithms) methods were used to investigate the immune infiltration status of different subtypes. Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenotype Score (IPS) were used to evaluate the immunotherapy response. The pRRophetic algorithm was used to estimate the efficiency of chemotherapeutic and targeted agents. Two machine learning algorithms (LASSO and RF) and multivariate logistic regression analysis were performed to construct a novel stemness-related classifier.ResultsWe observed that patients in the high-mRNAsi group had a better prognosis than those in the low-mRNAsi group. Next, we identified 190 stemness-related differentially expressed genes (DEGs) that could categorize LUSC patients into two stemness subtypes. Patients in the stemness subtype B group with higher mRNAsi scores exhibited better overall survival (OS) than those in the stemness subtype A group. Immunotherapy prediction demonstrated that stemness subtype A has a better response to immune checkpoint inhibitors (ICIs). Furthermore, the drug response prediction indicated that stemness subtype A had a better response to chemotherapy but was more resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Finally, we constructed a nine-gene-based classifier to predict patients’ stemness subtype and validated it in independent GEO validation sets. The expression levels of these genes were also validated in clinical tumor specimens.ConclusionThe stemness-related classifier could serve as a potential prognostic and treatment predictor and assist physicians in selecting effective treatment strategies for patients with LUSC in clinical practice.

Comprehensive analyses of a CD8+ T cell infiltration related gene signature with regard to the prediction of prognosis and immunotherapy response in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is associated with a worse prognosis than other histological subtypes of non-small cell lung cancer. Due to the vital role of CD8+ T cells in anti-tumor immunity, the characterization of CD8+ T cell infiltration-related (CTLIR) gene signature in LUSC is worthy of in-depth exploration. In our study, tumor tissues of LUSC patients from Renmin Hospital of Wuhan University were stained by multiplex immunohistochemistry to evaluate the density of infiltrated CD8+ T cells and explore the correlation with immunotherapy response. We found that the proportion of LUSC patients who responded to immunotherapy was higher in the high density of CD8+ T cell infiltration group than in the low density of CD8+ T cell infiltration group. Subsequently, we collected bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) database. The abundance of infiltrating immune cells in LUSC patients was analyzed by using CIBERSORT algorithm, and weighted correlation network analysis was performed to identify the co-expressed gene modules related to CD8+ T cells. We then developed a prognostic gene signature based on CD8+ T cell co-expressed genes and calculated the CTLIR risk score, which stratified LUSC patients into high-risk and low-risk groups. With univariate and multivariate analyses, the gene signature was identified as an independent prognostic factor in LUSC patients. The overall survival of LUSC patients in the high-risk group was significantly shorter than that of the low-risk group in the TCGA cohort, which was validated in Gene Expression Omnibus datasets. We analyzed immune cell infiltration in the tumor microenviroment and found fewer CD8+ T cells and more regulatory T cell infiltration in the high-risk group, which is characterized as an immunosuppressive phenotype. Furthermore, the LUSC patients in the high-risk group were predicted to have a better response to immunotherapy than those in the low-risk group when treated with PD-1 and CTLA4 inhibitors. In conclusion, we performed a comprehensive molecular analysis of the CTLIR gene signature in LUSC and constructed a risk model for LUSC patients to predict prognosis and immunotherapy response.

Network pharmacology and bioinformatics analysis identifies potential therapeutic targets of Naringenin against COVID-19/LUSC.

Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease that has posed a serious threat to people's daily lives and caused an unprecedented challenge to public health and people's health worldwide. Lung squamous cell carcinoma (LUSC) is a common type of lung malignancy with a highly aggressive nature and poor prognosis. Patients with LUSC could be at risk for COVID-19, We conducted this study to examine the potential for naringenin to develop into an ideal medicine and investigate the underlying action mechanisms of naringenin in COVID-19 and LUSC due to the anti-viral, anti-tumor, and anti-inflammatory activities of naringenin. LUSC related genes were obtained from TCGA, PharmGKB, TTD,GeneCards and NCBI, and then the transcriptome data for COVID-19 was downloaded from GEO, DisGeNET, CTD, DrugBank, PubChem, TTD, NCBI Gene, OMIM. The drug targets of Naringenin were revealed through CTD, BATMAN, TCMIP, SymMap, Chemical Association Networks, SwissTargetPrediction, PharmMapper, ECTM, and DGIdb. The genes related to susceptibility to COVID-19 in LUSC patients were obtained through differential analysis. The interaction of COVID-19/LUSC related genes was evaluated and demonstrated using STRING to develop a a COX risk regression model to screen and evaluate the association of genes with clinical characteristics. To investigate the related functional and pathway analysis of the common targets of COVID-19/LUSC and Naringenin, KEGG and GO enrichment analysis were employed to perform the functional analysis of the target genes. Finally, The Hub Gene was screened and visualized using Cytoscape, and molecular docking between the drug and the target was performed using Autodock. We discovered numerous COVID-19/LUSC target genes and examined their prognostic value in LUSC patients utilizing a variety of bioinformatics and network pharmacology methods. Furthermore, a risk score model with strong predictive performance was developed based on these target genes to assess the prognosis of LUSC patients with COVID-19. We intersected the therapeutic target genes of naringenin with the LUSC, COVID-19-related targets, and identified 354 common targets, which could be used as potential target genes for naringenin to treat COVID-19/LUSC. The treatment of COVID-19/LUSC with naringenin may involve oxidative stress, anti-inflammatory, antiviral, antiviral, apoptosis, immunological, and multiple pathways containing PI3K-Akt, HIF-1, and VEGF, according to the results of the GO and KEGG enrichment analysis of these 354 common targets. By constructing a PPI network, we ascertained AKT1, TP53, SRC, MAPK1, MAPK3, and HSP90AA1 as possible hub targets of naringenin for the treatment of COVID-19/LUSC. Last but not least, molecular docking investigations showed that naringenin has strong binding activity in COVID-19/LUSC. We revealed for the first time the pharmacological targets and potential molecular processes of naringenin for the treatment of COVID-19/LUSC. However, these results need to be confirmed by additional research and validation in real LUSC patients with COVID-19.

Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma

Exploringearly detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression.

Pre-treatment plasma-informed circulating tumor DNA detection as an early identification of therapeutic benefit for patients with untreated advanced lung squamous cell carcinoma.

9041 Background: Circulating tumor DNA (ctDNA) detection as a promising prognostic factor to assess treatment response has been widely studied with customized tumor-informed strategies, the application of which was limited by the inaccessibility for tumor biopsies in a substantial proportion of patients. To overcome this challenge, we presented a proof-of-concept study for the development of a novel personalized pre-treatment plasma-informed next generation sequencing (NGS) panel and investigated its prognostic value for first line treatment in patients with advanced lung squamous cell carcinoma (LUSC). Methods: Paired plasma and white blood cell (WBC) samples were collected from 227 of stage IIIB-IV LUSC patients who were treated with first line either chemotherapy or chemotherapy plus PD-1 blockade at pre- and post-treatment. Somatic mutations in pre-treatment plasma were identified with a 769-gene based NGS panel (Genecast, Wuxi, China) followed with germline or clonal hematopoietic variants filtering by referring to the WBC samples. Positive variants detected in the pre-treatment plasma served as a basis for personalized variants tracking in post-treatment plasma. ctDNA kinetics was measured by plasmaDelta (Δgenevaf=((post-treatment genevaf – pre-treatment genevaf) / pre-treatment genevaf; genevaf was defined by variant allele frequency for each individual gene). Progression-free survival (PFS) as the primary endpoint and overall survival (OS) as the secondary endpoint were assessed. Results: ctDNA positivity was observed in 80% (182/227) of pre-treatment samples and subsequently 54% (99/182) of post-treatment samples. ctDNA positivity at post-treatment was shown to be a significant predictor for both PFS and OS in the whole group as well in the sub-treatment groups (Table). After adjusting for clinicopathologic variables, ctDNA positivity remained significantly associated with both worse PFS and OS (PFS: HR = 2.3, P &lt;0.001 and OS: HR= 2.56, P &lt;0.001). ctDNA kinetics further identified a worse treatment response group. Patients with high plasmaDelta had remarkably worse outcomes than patients with low plasmaDelta (PFS: HR =4.8; 95%CI=2.8-8.3; P &lt; 0.001 and OS: HR=2.8; 95%CI=1.7-4.6; P &lt;0.001). Conclusions: These results demonstrated the feasibility of pre-treatment plasma-informed ctDNA detection and kinetics for monitoring treatment response in LUSC patients whose tumor biopsies were unavailable. [Table: see text]

Deep learning-based approach to predict multiple genetic mutations in colorectal and lung cancer tissues using hematoxylin and eosin-stained whole-slide images.

1549 Background: The presence of genetic mutations is a vital prognostic in many types of cancer. However, genomic testing is expensive and challenging to perform. In contrast, hematoxylin and eosin (H&amp;E) staining is relatively inexpensive and straightforward. Thus, in this study, we propose a method of predicting the presence of genetic mutations using H&amp;E-stained whole-slide images (WSIs). Methods: We divided each H&amp;E–stained WSI into small pieces or “patches.” We used a deep learning model to classify each patch based on the presence of tumor-containing regions. We then extracted image features from each tumor-containing patch using a deep learning-based feature extractor. We created image features for the entire WSI by concatenating the features of the patches. We then trained genetic mutation classification models using the WSI features as the input and the presence or absence of genetic mutations as the output. Finally, we evaluated the performance of these models using the area under the receiver operating characteristic curve (AUC). Results: First, we evaluated our methods using The Cancer Genome Atlas (TCGA) colorectal cancer dataset. We used H&amp;E–stained WSIs and data associated with Microsatellite Instability ( MSI) and BRAF gene mutations, which are directly relevant to therapeutic strategies, obtained from an independent clinical cohort of 566 patients with TCGA colon and rectum adenocarcinoma. We divided the data into training, validation, and test splits, comprising 367, 90, and 109 patients, respectively. We used the training and validation splits for model training and selection, and the test split for model evaluation. The AUC values of the classification models and associated 95% confidence intervals (CIs) were 0.721 (CI = 0.572–0.870) for MSI and 0.712 (CI = 0.547–0.877) for BRAF gene mutations. We also applied our approach to MUC16, KRAS, and ALK mutations using the TCGA lung cancer dataset. We divided 909 TCGA lung adenocarcinoma and lung squamous cell carcinoma patients into training, validation, and test splits, comprising 582, 146, and 181 patients, respectively. In contrast with those of the colorectal dataset, WSI image features were generated using all patches. The AUC values on the test splits were 0.897 (CI = 0.85–0.95) for MUC16, 0.845 (CI = 0.75–0.94) for KRAS, and 0.756 (CI = 0.57–0.94) for ALK mutations. Conclusions: We proposed an approach to predict the presence of genetic mutations using only H&amp;E–stained WSIs and evaluated its performance using colorectal and lung cancer datasets. Our model has the potential to predict the presence of certain genetic mutations with superior performance. These predictions can be used to improve the accuracy of prognostic prediction using WSIs alone.