Lung Squamous Cell Carcinoma Research Articles

A case of squamous cell carcinoma of the lung misdiagnosed as tuberculosis. Is HPV infected by oral sex the causative agent?

BackgroundThe integration of high-risk human papilloma virus (HPV) DNA into the human genome has been implicated in cervical carcinogenesis and head and neck squamous cell cancer. However, its role in lung squamous cell carcinoma is not well understood. In addition, tuberculosis (TB) and lung cancer(LC) share similar clinical symptoms and imaging features, increasing the risk of misdiagnosis.Case PresentationThe patient presented with a 16-month history of hemoptysis, chest pain, and occasional fatigue, without fever, chills, or history of mechanical damage or autoimmune diseases. Examination revealed normal vital signs and laboratory parameters, except for a positive interferon-gamma release assay indicating tuberculosis infection. Bronchoscopic examinations identified congestion and edema of the tracheal wall, along with a tiny lesion in the right wall of the trachea. She had been misdiagnosed with tuberculosis. However, the diagnosis of squamous cell carcinoma was eventually confirmed by endoscopic biopsy. The patient’s macrogenomic second-generation sequencing (mNGS) of the bronchoscopic biopsy specimen was positive for HPV-16.The patient’s sex partner tested positive for HPV-16 in penile scrapings, indicating HPV transmission through oral sex.ConclusionsThis case highlights the potential for HPV infection acquired through oral sex to lead to lung squamous cell carcinoma. It emphasizes the importance of considering HPV-associated malignancies in patients with respiratory symptoms who engage in oral sexual behaviors.

VASH2 enhances KIF3C-mediated EGFR-endosomal recycling to promote aggression and chemoresistance of lung squamous cell carcinoma by increasing tubulin detyrosination

Lung squamous cell carcinoma (LUSC) is associated with high mortality and has few therapeutic options. Chemotherapy remains the main treatment for LUSC patients, but multi-drug resistance has become the dominant challenge in the failure of chemotherapy in various cancers. Therefore, the effective therapeutic strategy for LUSC patients is an urgent unmet need. Here, we found vasohibin-2 (VASH2) was a prognostic biomarker for LUSC patients, and VASH2 promoted the malignant biological behaviors of LUSC cells and chemoresistance by increasing the detyrosination of α-tubulin. The high level of detyrosinated-tubulin was negatively associated with patient prognosis. Blocking the tubulin carboxypeptidase (TCP) activity of VASH2 inhibited the xenograft tumor growth and improved the treatment efficacy of paclitaxel in vivo. Results revealed that VASH2-induced increase in tubulin detyrosination boosted the binding of kinesin family member 3C (KIF3C) to microtubules and enhanced KIF3C-dependent endosomal recycling of EGFR, leading to the prolonged activation of PI3K/Akt/mTOR signaling. This study demonstrated that VASH2 was not only a prognostic biomarker but also a promising therapeutic target in LUSC, which offers a novel insight that combination of chemotherapy and EpoY, a TCP inhibitor, may be a promising treatment strategy for LUSC patients.

Clinical significance of CD155 expression in surgically resected lung squamous cell carcinoma.

Cluster of differentiation 155 (CD155) is expressed in many tumor types. CD155 is involved in the immune avoidance of tumor cells and contributes to tumor development and progression. Therefore, CD155 is a novel target for cancer immunotherapy. The clinical significance of CD155 expression in lung squamous cell carcinoma (LUSC) has not been fully elucidated. We performed a retrospective analysis of 264 patients with surgically resected LUSC. Immunohistochemistry was used to evaluate CD155 expression. The association of CD155 expression with clinicopathological features and clinical outcomes was assessed. We also analyzed the relationship between CD155 expression and programmed cell death-ligand 1 (PD-L1) expression and tumor-infiltrating lymphocytes. Among the 264 patients, 137 patients (51.9%) were classified in the high CD155 expression group. High CD155 expression was significantly associated with pleural invasion, vascular invasion, PD-L1 positivity, and high CD3, CD4, and CD8 expressions. In multivariate analysis, the presence of pleural invasion and PD-L1 positivity were independent predictors of high CD155 expression. Kaplan-Meier curve analysis showed that high CD155 expression was significantly associated with shorter disease-free survival and overall survival. In multivariate analysis, high CD155 expression was an independent poor prognostic factor for overall survival, but not for disease-free survival. Subgroup analyses revealed that the prognostic effect of CD155 expression was observed in the PD-L1 positive group but not the PD-L1 negative group. Our analysis revealed that high CD155 expression significantly predicted poor prognosis in patients with surgically resected LUSC, especially in patients with PD-L1-positive tumors.

LW-MorphCNN: A lightweight morphological attention-based subtype classification network for lung cancer

Abstract Lung cancer is generally considered one of the most deadly cancers globally. If it can be identified early and diagnosed correctly, the survival probability of patients can be significantly improved. In this process, histopathological examination is a commonly used method for diagnosing and detecting lung cancer. It is crucial to accurately identify lung cancer subtypes from histopathological images, as this helps doctors formulate effective treatment plans. However, the visual inspection in histopathological diagnosis requires a large amount of time and also depends on the subjective perception of clinicians. Therefore, this paper proposes a lightweight lung cancer subtype classification network based on morphological attention (LW-MorphCNN), which is used to automatically classify the histopathological images of benign lung tumors, ADC (adenocarcinoma), and SCC (squamous cell carcinoma) provided in the public dataset LC25000 (Lung and Colon). This paper takes histopathological images as input and conducts a comparative analysis with classic networks such as VGG16, VGG19, DenseNet121, and ResNet50, as well as existing classification methods proposed in the same work. The network proposed in this paper is superior to other networks in terms of parameter quantity and performance, with an accuracy rate and F1 - score reaching 99.47\% and 99.44\% respectively. Clinicians can install the provided LW-MorphCNN in the hospital to confirm the diagnosis results.

The Detailed Analysis of Polish Patients with Non-Small Cell Lung Cancer Through Insights from Molecular Testing (POL-MOL Study)

Molecular testing is recommended in patients with metastatic non-small cell lung cancer (NSCLC), but the extent of its use in Poland is unknown. The aim of the POL-MOL study was to investigate the frequency of using molecular testing in Polish patients with NSCLC. The invited Polish oncologists completed two questionnaires, and data for 1001 patients undergoing systemic treatment for NSCLC were collected. The use of molecular tests for the following genetic mutations was recorded: EGFR (del19, sub21), EGFR (other than del19/sub21), EGFR T790M, ALK (expression and rearrangement), RET, NTRK, ROS1, BRAF, HER2, and MET, as well as for immunochemical assessment of programmed cell death ligand 1 (PD-L1). Thanks to the weighting procedure, the results are representative of the population of Polish patients treated for NSCLC. Molecular tests were applied in 78% of patients with NSCL, 70% of patients with NSCLC not otherwise specified, and in 12% of patients with squamous cell carcinoma of the lung. The frequency of application increased with disease stage in all groups. In patients with squamous cell carcinoma, approximately 30% of tests for EGFR, ALK, and RET mutations were positive, which confirms the importance of testing at least a preselected subgroup of patients.

A novel approach ofdifferentiation of adenoma and carcinoma in lung cancer based on biogenic in situ synthesis of gold nanostructures on various oligonucleotide motifs.

Aunique approach is introducedfor constructing gold nanocrystals (AuNCs) with RNA motif-directed morphologies in a sequence-independent manner and itsapplications in theclinical areaare described. By using this method, a label-free LSPR-based detection method for the SOX2OT transcript, long non-coding RNAs (lncRNAs), which is a prognostic indicator of poor survival in lung cancer patientsis presented. For the first time, we examined how the structural changes of RNA after the heteroduplex formation with a specific DNA probe can change the morphology and LSPR band of AuNCs. Using this method, is was possible to differentiate lung squamous cell carcinoma from adenocarcinoma samples without a need for a prior amplification of the target lncRNA. The approach of using specific DNA probe enables the in situ synthesis of nanocrystals in a different way and expands this method for future translational medicine, particularly detection of specific RNA.

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

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

EP.01A.02 Risk Factors and Nomogram Construction of Early Death in Patients with Stage IV Lung Squamous Cell Carcinoma: A SEER-Based Study

EP.01A.02 Risk Factors and Nomogram Construction of Early Death in Patients with Stage IV Lung Squamous Cell Carcinoma: A SEER-Based Study

MA11.08 Preliminary Efficacy and Safety of SHR-1701 Plus Fluzoparib as Maintenance Therapy for Patients with Advanced Lung Squamous Cell Carcinoma

MA11.08 Preliminary Efficacy and Safety of SHR-1701 Plus Fluzoparib as Maintenance Therapy for Patients with Advanced Lung Squamous Cell Carcinoma

P1.03E.01 Effect of Gut Microbiota and Metabolites on the Efficacy of Immunotherapy Combined with Chemotherapy in Lung Squamous Cell Carcinoma Patients

P1.03E.01 Effect of Gut Microbiota and Metabolites on the Efficacy of Immunotherapy Combined with Chemotherapy in Lung Squamous Cell Carcinoma Patients

P1.02B.02 Role of Chromosome 3 Aneuploidy in Basal Cell Differentiation and Lung Squamous Cell Carcinoma Initiation

P1.02B.02 Role of Chromosome 3 Aneuploidy in Basal Cell Differentiation and Lung Squamous Cell Carcinoma Initiation

Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagnosis and pathological subtyping are crucial for lung cancer treatment. Solitary solid lung nodules with lobulation and spiculation signs are often indicative of lung cancer; however, in some cases, postoperative pathology finds benign solid lung nodules. It is critical to accurately identify solid lung nodules with lobulation and spiculation signs before surgery; however, traditional diagnostic imaging is prone to misdiagnosis, and studies on artificial intelligence-assisted diagnosis are few. Therefore, we introduce a volumetric SWIN Transformer-based method. It is a multi-scale, multi-task, and highly interpretable model for distinguishing between benign solid lung nodules with lobulation and spiculation signs, lung adenocarcinomas, and lung squamous cell carcinoma. The technique's effectiveness was improved by using 3-dimensional (3D) computed tomography (CT) images instead of conventional 2-dimensional (2D) images to combine as much information as possible. The model was trained using 352 of the 441 CT image sequences and validated using the rest. The experimental results showed that our model could accurately differentiate between benign lung nodules with lobulation and spiculation signs, lung adenocarcinoma, and squamous cell carcinoma. On the test set, our model achieves an accuracy of 0.9888, precision of 0.9892, recall of 0.9888, and an F1-score of 0.9888, along with a class activation mapping (CAM) visualization of the 3D model. Consequently, our method could be used as a preoperative tool to assist in diagnosing solitary solid lung nodules with lobulation and spiculation signs accurately and provide a theoretical basis for developing appropriate clinical diagnosis and treatment plans for the patients.

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.

How Morphology Shapes Survival in Invasive Squamous Cell Carcinoma of the Lung.

Squamous cell carcinoma (SQCC) represents a significant proportion of human malignancies affecting various anatomical sites, including the lung. Understanding the prognostic factors is crucial for establishing effective risk stratification in these patients, as multiple critical aspects significantly impact overall survival. A retrospective study was conducted on 99 patients with operable lung SQCC treated at a tertiary center. The exclusion criteria included patients under 18, those with in situ or metastatic SQCC, and those who received neoadjuvant therapy. The surgical specimens were re-analyzed, and data were collected on multiple variables, including pTNM staging, tumor characteristics, and overall survival (OS). The Kaplan-Meier survival analysis and Cox regression models were used to identify significant prognostic factors. The Kaplan-Meier analysis showed a median survival of 36 months with a 65.65% mortality rate. Significant factors influencing survival included keratinization, histological grading, tumor size and stage, pleural invasion, tumor cell arrangement, tumor budding, spread through air space (STAS), and mitotic index. A multiple Cox regression highlighted the nonkeratinizing tumors, advanced pT stages, single-cell invasion, and high mitotic index as key predictors of poorer outcomes. The nonkeratinizing tumors showed higher mortality and shorter median survival rates compared to keratinizing tumors. The tumor staging, cell arrangement, and tumor budding significantly impacted the survival curves. The study underscores the importance of detailed histopathological evaluations in lung SQCC. The nonkeratinizing tumors, advanced pT stage, single-cell invasion, and high mitotic index were associated with higher hazard rates, emphasizing the need for a comprehensive grading system incorporating these factors to improve prognostic accuracy and guide treatment strategies.

Classification of histological subtypes of non-small cell lung cancer using computerized tomography texture analysis

Objective: This study aimed to differentiate between the two main histological subtypes of non-small cell lung cancer using a non-invasive technique, computerized tomography texture analysis. Method: We included 53 patients. All patients were histopathologically proven non-small cell lung cancer cases. All patients underwent thorax CT scans. In CT images, the differences present in the texture features of adenocarcinoma and squamous cell carcinoma, which are the two main histological subtypes of non-small cell lung cancer, were determined by the consensus of two radiologists for computerized tomography-based texture analysis. Results: A total of 44 texture features were extracted, including 12 first-order features and 32 second-order features derived from gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone length matrix (GLZLM) features in 51 CT images. None of the evaluated texture parameters were statistically significant. However, in patients with squamous cell lung cancer, the values of Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation higher from adenocarcinoma patients and had the highest area under the curve in roc analyses (0.727, 0.664, 0.666 respectively) Conclusion: Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation features can be used to differentiate between the subtypes of non-small cell lung cancer, adenocarcinoma and squamous cell carcinoma. These features were highly associated with the high intratumoral heterogeneity of squamous cell lung cancer.

Unveiling ficolins: diagnostic and prognostic biomarkers linked to the Tumor Microenvironment in Lung Cancer

BackgroundFicolins (FCNs) are a family of proteins, comprising FCN1, FCN2 and FCN3, and integral to the immune system which have been implicated in the onset and progression of tumors. Despite their recognized roles, a comprehensive analysis of FCNs in lung cancer remains elusive.MethodsWe employed a variety of bioinformatics tools, including UCSC, SangerBox, Ualcan, cBioPortal, String, Metascape, GeneMANIA, TIDE, CTD, and CAMP databases to investigate the differential expression, diagnostic and prognostic significance, genetic alterations, functional enrichment, immune infiltration, and potential immunotherapeutic implications of FCN1, FCN2, and FCN3 in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Additionally, RT-qPCR and immunohistochemistry were utilized to validate the expressions of FCNs at the mRNA and protein levels in LUSC and LUAD.ResultsOur comprehensive bioinformatic analysis, supported by RT-qPCR and immunohistochemistry, revealed that the expressions of FCN1, FCN2 and FCN3 were consistently downregulated in both LUSC and LUAD tumor tissues. FCNs demonstrated significant diagnostic potential for LUSC and LUAD, with the area under the receiver operating characteristic curve (AUC) for FCN1 and FCN3 exceeding 0.90. Furthermore, FCN2 and FCN3 showed a strong negative correlation with overall survival (OS) in LUSC, whereas FCN1 and FCN2 were positively correlated with OS in LUAD, suggesting their prognostic value in lung cancer. Gene enrichment analysis indicated that FCNs were predominantly associated with the complement system and complement activation pathways. Immune infiltration analysis further revealed a significant positive correlation between FCNs and the presence of neutrophils and resting mast cells. Our analysis of immunotherapy outcomes revealed a significant disparity in the immunophenoscore (IPS) among lung cancer patients treated with immune checkpoint inhibitors (ICIs), distinguishing those with high FCN expression from those with low FCN expression. Additionally, we identified small molecule compounds related to FCNs and drugs pertinent to LUSC and LUAD.ConclusionFCNs held promise as diagnostic and prognostic biomarkers for LUSC and LUAD. This study also elucidated the relationship of FCNs with the tumor microenvironment, offering novel insights into the immunotherapeutic landscape for LUSC and LUAD.

A Case of Septin-7 Antibody-associated Encephalitis Associated with Seizures and Squamous Cell Lung Carcinoma

A Case of Septin-7 Antibody-associated Encephalitis Associated with Seizures and Squamous Cell Lung Carcinoma

Dysregulation of the DRAIC/SBK1 Axis Promotes Lung Cancer Progression

Background: Long non-coding RNAs (lncRNAs) are key regulators of cellular processes that underpin cancer development and progression. DRAIC is a migration inhibitor that has been linked with lung adenocarcinoma progression; however, its mechanisms remain to be studied. Methods: Several bioinformatics tools were used to explore the role of DRAIC in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Results: Our bioinformatics analysis illustrates that patients with low expression of DRAIC have poor overall survival outcomes. In addition, the mRNA of SH3 domain-binding kinase 1 (SBK1) was downregulated in this cohort of patients. Mechanistic analysis showed that SBK1 is under the DRAIC competing endogenous RNAs network, potentially through sponging of miRNA-92a. Conclusion: Consistent dysregulation of the DRAIC-SBK1 axis was linked to poor survival outcome in both LUAD and LUSC, suggesting a tumour inhibitor role and providing potential for new diagnostics and therapeutic approaches.

Expression and prongostic impact of galectin-7 in human lung cancer

Malignant tumors of the lung are the leading cancers worldwide. Prognostic biomarkers continue to be investigated for the detection and stratification of lung cancer for clinical use. In breast cancer cells and in lymphomas, the overexpression of galectin-7 led to increased metastasis. In lung cancer, squamous cell carcinoma, galectin-7 was also identified as a factor promoting metastasis. In this study, we investigated the expression of galectin-7 in relation to clinicopathological features and overall survival in patients with different types of lung cancer. By immunohistochemistry, expression of galectin-7 was analyzed in 308 cases of lung cancer; 108 cases of adenocarcinoma, 193 cases of squamous cell lung carcinoma and 7 cases of small cell lung cancer (SCLC) and correlated with clinicopathological characteristics as well as patients’ overall survival. Immunohistochemical detection of galectin-7 expression was most evident in squamous cell lung carcinoma (36.27%), followed by adenocarcinoma (5.55%). Negative expression of galectin-7 was found in all patients with SCLC. No significant correlation was found in patients with squamous cell lung carcinoma. Within the adenocarcinoma and squamous cell lung carcinoma subgroups, there were statistically significant correlations between the expression of galectin-7 and some clinicopathologic features of the patients. In our study, we were able to show that galectin-7 could serve as a new prognostic biomarker and is also a potential new drug target in non-small cell lung cancer.

Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.