Cisplatin resistance remains a significant challenge in treating lung squamous cell carcinoma (LUSC). The role of FADD in this resistance requires further investigation. Our study revealed that FADD is overexpressed in LUSC patients, correlating with lower survival rates. We also discovered that long-term cisplatin-resistant LUSC cell lines (LUSC-CR) had elevated FADD protein levels, and reducing FADD restored their cisplatin sensitivity. At the same time, LUSC-CR cells resisted cisplatin-induced DNA damage and had enhanced DNA repair, linked to P53's negative regulation of FADD. Additionally, knockdown of the long non-coding RNA (lncRNA) PPFIA1-AS1 can potentiate drug resistance in LUSC cells by decelerating FADD protein turnover and elevating FADD protein levels. In essence, this study elucidated novel mechanisms underlying cisplatin resistance in LUSC, wherein the PPFIA1-AS1/FADD axis regulates DNA damage and repair. Consequently, targeting the PPFIA1-AS1/FADD axis may present a promising avenue for overcoming cisplatin resistance and enhancing the prognosis of LUSC patients.

Assessing large language model for automated diagnosis of benign and malignant lung tumors.

Integrating multimodal data of pathological image and gene expression for cancer survival analysis can achieve better results than using a single modality. However, existing multimodal learning methods ignore fine-grained interactions between both modalities, especially the interactions between biological pathways and pathological image patches. In this article, we propose a novel Pathway-Aware Multimodal Transformer (PAMT) framework for interpretable cancer survival analysis. Specifically, the PAMT learns fine-grained modality interaction through three stages: (1) In the intra-modal pathway-pathway / patch-patch interaction stage, we use the Transformer model to perform intra-modal information interaction; (2) In the inter-modal pathway-patch alignment stage, we introduce a novel label-free contrastive loss to aligns semantic information between different modalities so that the features of the two modalities are mapped to the same semantic space; and (3) In the inter-modal pathway-patch fusion stage, to model the medical prior knowledge of "genotype determines phenotype", we propose a pathway-to-patch cross fusion module to perform inter-modal information interaction under the guidance of pathway prior. In addition, the inter-modal cross fusion module of PAMT endows good interpretability, helping a pathologist to screen which pathway plays a key role, to locate where on whole slide image (WSI) are affected by the pathway, and to mine prognosis-relevant pathology image patterns. Experimental results based on three datasets of bladder urothelial carcinoma, lung squamous cell carcinoma, and lung adenocarcinoma demonstrate that the proposed framework significantly outperforms the state-of-the-art methods.

Cardiac metastasis from lung tumors is rare, and often asymptomatic, making diagnosis challenging. Herein this report presents a case of a 69-year-old male with squamous cell carcinoma of the lung with cardiac metastasis to the left ventricle. Two days post chemotherapy, he developed symptoms of chest pain and mild hemoptysis. An electrocardiogram revealed ST-segment elevation in the inferior leads, suggesting a possible inferior ST-segment elevation myocardial infarction (STEMI). His symptoms improved with Nitroglycerin, and prescribed calcium channel blockers, and long-acting nitrates. The symptoms were confirmed to be attributed to an atherosclerotic plaque. This case highlights the challenge of differentiating between coronary artery disease and cardiac symptoms, and emphasizes the necessity of thorough investigation in cancer patients with cardiac symptoms.

The association between testosterone and lung cancer remains unclear. This study investigates the relationship between testosterone levels and lung cancer risk, focusing on bioavailable testosterone levels (BTLs), total testosterone levels (TTLs), and sex hormone-binding globulin (SHBG) in relation to lung cancer subtypes. We utilized bidirectional and multivariable Mendelian randomization (MR) analyses based on genome-wide association studies (GWAS) to assess causal links. To validate the findings clinically, immunohistochemical (IHC) staining for androgen receptor (AR) expression and survival analyses were conducted on a cohort of 90 patients with lung squamous cell carcinoma (LUSC). MR analysis demonstrated that higher BTLs were significantly associated with a reduced risk of LUSC (OR = 0.365, P = 0.001), while no significant associations were observed for TTLs or SHBG. Reverse MR analysis found no causal effect of lung cancer on testosterone levels. Multivariable MR confirmed BTLs as an independent protective factor. In the clinical cohort, AR expression was significantly associated with better prognosis, showing improved median progression-free survival (12.3 vs. 9.0 months, P = 0.01) and median overall survival (35.3 vs. 29.4 months, P < 0.01). Cox regression identified AR expression as an independent protective factor for patient outcomes. However, study limitations include potential residual confounding, ethnic heterogeneity between European GWAS data and Asian clinical cohorts, and the lack of direct experimental validation. Our findings suggest that higher BTLs may play a protective role against LUSC. BTLs and AR expression show potential as valuable biomarkers for the diagnosis and prognostic assessment of LUSC.

BackgroundLung squamous cell carcinoma (LSCC) is a prevalent subtype of non-small cell lung cancer (NSCLC). While there have been some prognostic models for LSCC, models specifically addressing stage IIIA LSCC are still limited. The aim of this study is to develop a nomogram to predict the overall survival (OS) of patients with stage IIIA LSCC.MethodsPatients diagnosed with LSCC between 2,010 and 2,015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and their basic clinical characteristics were analyzed. A 1:1 propensity score matching (PSM) analysis was performed to balance the baseline characteristics of the patients. The OS of patients was evaluated using Kaplan-Meier analysis and compared with the log-rank test. Clinical prognostic factors related to OS were analyzed using univariate and multivariate Cox regressions, and a visual nomogram model for predicting patient prognosis was developed and validated.ResultsThis study included 4,268 patients with stage IIIA LSCC, comprising 1,157 cases in the cancer-directed surgery (CDS) group and 3,111 cases in the no-cancer-directed surgery (no-CDS) group. After PSM, 1,095 patients in the CDS group were matched with 1,095 patients in the no-CDS group. Kaplan-Meier survival analysis revealed the significant beneficial effect of surgery on OS in both the original and matched cohorts. Multivariate Cox analysis indicated that sex, age, marital status, surgery, and chemotherapy were independent prognostic factors for stage IIIA LSCC. Additionally, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve demonstrated strong predictive performance in both the training and validation cohorts of the prognostic nomogram.ConclusionsThrough univariate and multivariate Cox regression analyses, sex, age, marital status, surgery, and chemotherapy were identified as independent prognostic risk factors for OS in patients with stage IIIA LSCC. A nomogram was successfully developed to assist clinicians in making more informed treatment decisions.

Rationale:Hemoptysis is a known complication of lung cancer that may occur after antiplatelet or anticoagulant therapy, chemotherapy, or antiangiogenic agents. Although immune checkpoint inhibitors have improved outcomes in lung cancer, hemoptysis remains a rare but potentially fatal adverse event. We describe an autopsy-confirmed case of complete tumor remission with cavitation leading to fatal hemoptysis after chemoradiotherapy (CRT) followed by combined ipilimumab and nivolumab therapy for squamous cell carcinoma.Patient concerns:A 68-year-old man with a history of heavy smoking was found to have a 70-mm right hilar lung lesion and was diagnosed with stage IIIC squamous cell carcinoma with 50% programmed death ligand-1 expression. After CRT and subsequent combination chemotherapy plus immune checkpoint inhibitors, he developed pneumonitis requiring prednisolone (PSL). During PSL tapering, he experienced fever and a new right upper lobe cavitary lesion, which enlarged despite empiric antibiotics. After discharge under apparently stable conditions on a reduced PSL dose, he suddenly developed massive hemoptysis.Diagnoses:The primary diagnosis was stage IIIC squamous cell carcinoma of the lung. During the treatment course, he developed immune checkpoint inhibitor–related pneumonitis and a new cavitary lesion in the right upper lobe. Autopsy revealed a necrotic cavitary lesion with vascular destruction in the right upper lobe, dense adhesions between the lung and chest wall, and no residual carcinoma, infection, or distant metastases, confirming complete tumor remission and fatal hemoptysis due to treatment-related vascular injury.Interventions:The patient received definitive CRT with carboplatin and paclitaxel plus 60 Gy thoracic radiotherapy, followed by carboplatin and paclitaxel combined with ipilimumab and nivolumab, and subsequently maintenance ipilimumab and nivolumab. Immune-related pneumonitis was treated with systemic PSL, which was tapered and subsequently re-escalated when fever and a new cavitary lesion appeared. Empiric broad-spectrum antibiotics were also administered.Outcomes:CRT and combined immune checkpoint inhibitor therapy achieved complete tumor remission pathologically. However, the progressive cavitary change in the irradiated lung ultimately resulted in massive hemoptysis, cardiac arrest, and death. Autopsy confirmed a necrotic cavity with vascular destruction without residual malignancy or infection.Lessons:In patients receiving ipilimumab and nivolumab after thoracic CRT for centrally located squamous cell carcinoma, rapid tumor necrosis and cavitation within irradiated lung with fragile vasculature may markedly increase the risk of severe and fatal hemoptysis. When initiating or resuming combined immune checkpoint inhibitor therapy in this setting, clinicians should carefully consider CRT-associated vascular fragility and potential immune-mediated vascular injury, and ensure close imaging follow-up and airway surveillance.

This study investigated the role of the mitochondrial protein MRPL17 (mitochondrial ribosomal protein L17) in non-small cell lung cancer (NSCLC), exploring its expression profile, clinical significance, and therapeutic potential. Transcriptomic analyses of TCGA and single-cell RNA sequencing data revealed significant upregulation of MRPL17 in LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma) tumor tissues, particularly within malignant epithelial and proliferating cancer cells. Elevated MRPL17 expression correlated with advanced stages, positive lymph node metastasis, and poorer overall survival. In vitro investigations demonstrated that silencing or knockout of MRPL17 attenuated cell viability, proliferation, migration, and invasion in NSCLC cells, while promoting apoptosis. Mechanistically, MRPL17 silencing impaired mitochondrial respiratory function, causing reduced oxygen consumption, diminished Complex I activity, and decreased ATP. These impairments were partially reversible by antioxidant treatment or glucose supplementation. Conversely, MRPL17 overexpression enhanced aggressive cellular phenotypes and mitochondrial energetic output. Bioinformatic analysis and subsequent experiments confirmed COX8A as a direct downstream target of MRPL17, mediating its pro-cancerous effects. In vivo, MRPL17 silencing suppressed NSCLC xenograft growth in nude mice, a phenomenon associated with reduced COX8A levels, mitochondrial dysfunction, heightened oxidative stress, and increased apoptosis. Thus, MRPL17 is an important pro-cancerous target in NSCLC, driving malignant progression through the regulation of mitochondrial function and cellular redox balance, with COX8A identified as a key mediator.

A Case of Recurrent Cytokine Release Syndrome Following Neoadjuvant Chemotherapy Including Nivolumab for Lung Squamous Cell Carcinoma: Successful Treatment with Steroid Pulse Therapy and Tocilizumab

Lung squamous-cell carcinoma (LUSC) is a highly aggressive malignancy with a poor prognosis. Tertiary lymphoid structures (TLS) play a crucial role in the immune response and significantly influence the efficacy of immunotherapy. However, the prognostic and immunological implications of TLS-associated molecular subtypes in LUSC remain unclear. In this study, we applied 10 multi-omics integration strategies to perform a multi-omics analysis of the mRNA expression profiles, DNA methylation, and genomic mutation data of 39 TLSs-related genes, along with long non-coding RNA (lncRNA) expression profiles, to generate integrated consensus subtypes of LUSC. Four molecular subtypes were identified: cancer subtype 1 (CS1), CS2, CS3, and CS4. We observed a significant difference in overall survival between cancer subtype 1 (CS1) and CS3. Subsequently, we identified 33 prognosis-related genes based on differential expression between CS1 and CS3, which were further refined to 20 genes using the least absolute shrinkage and selection operator (LASSO) regression algorithm, and constructed a prognostic signature termed the LUSC-Survival Prediction Index (LUSCSPI). The high-LUSCSPI group demonstrated a poor prognosis and was more likely to benefit from treatment with nine chemotherapeutic agents (shikonin, doxorubicin, CMK, S-Trityl-L-cysteine, paclitaxel, DMOG, gemcitabine, erlotinib, and crizotinib). In contrast, the low-LUSCSPI group exhibited a more favorable prognosis, with thapsigargin and cisplatin identified as promising treatment options. In conclusion, our results highlight the potential of LUSCSPI as an independent prognostic factor for LUSC. Further, the multi-omics consensus approach provides a robust foundation for prognostic stratification in LUSC patients, facilitating personalized treatment and disease management.

We present a case of cytomegalovirus (CMV)-associated rectal ulceration that developed as a secondary complication of immune checkpoint inhibitor-induced colitis. A 74-year-old man who received pembrolizumab for metastatic squamous cell carcinoma of the lung developed severe immune-related colitis and required high-dose corticosteroid therapy. Although his condition initially improved, his symptoms recurred approximately four weeks after the initiation of steroid tapering. Colonoscopy revealed a circumferential, band-shaped ulcer located just above the dentate line, and a histopathological examination demonstrated nuclear inclusion bodies suggestive of CMV infection. The diagnosis was confirmed by immunohistochemical staining. Treatment with valganciclovir resulted in complete clinical and virological remission within four weeks. This case highlights the need for monitoring opportunistic infections during immunosuppressive therapy for immune-related adverse events (irAEs). It also underscores the importance of early recognition and prompt intervention for clinical deterioration.

BackgroundThis study aimed to develop and validate an interpretable machine learning model that harnesses circulating tumor DNA (ctDNA) to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing immunotherapy, thereby addressing the inherent limitations of conventional biomarkers such as PD-L1 expression and tumor mutational burden.MethodsThis multicenter study involved pretreatment ctDNA profiling of 441 patients with non-small cell lung cancer (NSCLC), stratified into three independent cohorts: a training set (n=303, OAK trial), a validation set (n=97, POPLAR trial), and a local test set (n=41, multicenter retrospective cohort, 2023–2024). Using 5-fold cross-validated LASSO-Cox (Least Absolute Shrinkage and Selection Operator-Cox Proportional Hazards) regression, 25 prognostic genomic features were identified for integration into an eXtreme Gradient Boosting (XGBoost) model. Model performance was systematically evaluated via three approaches: (1) discrimination metrics, including AUC with 95% confidence intervals, accuracy, sensitivity, and specificity; (2) Kaplan-Meier survival analysis complemented by log-rank testing; and (3) SHapley Additive exPlanations (SHAP) for interpreting feature importance.ResultsThe model exhibited robust predictive performance, with AUCs of 0.82 (training cohort), 0.79 (validation cohort), and 0.77 (test cohort). Key genomic predictors included TP53 mutations, which were associated with shorter PFS, and BRCA2 mutations, which correlated with longer PFS. SHAP analysis identified NOTCH1 as a novel predictive biomarker, whose feature contribution profile suggests a role in immune modulation in lung squamous cell carcinoma. Risk stratification significantly distinguished PFS outcomes (log-rank P < 0.05). Decision curve analysis confirmed the model’s clinical utility, as it outperformed “treat-all” strategies.ConclusionThis study establishes a robust, interpretable ctDNA-derived machine learning algorithm for predicting PFS in NSCLC patients receiving immune checkpoint inhibitors. The identification of TP53, BRCA2, and NOTCH1 as biologically plausible predictive biomarkers advances understanding of immunotherapy response mechanisms and enables clinically actionable risk stratification to guide therapeutic decision-making. These findings underscore the need for prospective multicenter validation to facilitate translation into precision oncology practice.

Background: Transposable elements are normally silenced by epigenetic mechanisms; however, during malignant transformation, epigenetic alterations enable transposons to produce functional molecules like miRNAs. Among these, LINE-2 (L2) elements can generate miRNAs capable of regulating key genes, including tumor suppressors. Two L2-derived miRNAs, miR-28 and miR-708, have been linked to lung cancer, yet the mechanisms underlying their dysregulation remain poorly understood. Our study reveals how genomic context contributes to aberrant gene expression through comprehensive bioinformatic analyses. Methods: Using bioinformatics analysis, we evaluated the expression of miR-28 and miR-708 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) datasets from TCGA. Further, we assessed the expression and methylation status of miR-28 and miR-708 host genes, LPP and TENM4, respectively, TENM4 using computational tools. Finaly, we searched for potential candidate tumor suppressor genes targeted by miR-28 and miR-708, which are downregulated in LUAD and LUSC. Results: We found that intragenic L2-derived miR-28 and miR-708 are significantly upregulated in LUAD and LUSC. While TENM4 gene also displays a marked increase in expression in LUAD and LUSC, in tumor versus normal tissue, this difference is less obvious for the LPP gene. We suggest that such dysregulations in expression might be linked to specific methylation patterns of their genomic locations. Furthermore, we emphasize that miR-28 and miR-708 might contribute to lung cancer pathogenesis by targeting key tumor suppressor genes. Conclusions: Alterations in the methylation status of L2-miRNAs genomic loci might result in elevated levels of miRNAs and subsequent targeting of tumor suppressor genes with potential implications in lung cancer pathogenesis.

Lung cancer is one of the most common malignancies, characterized by a wide prognosis spectrum, different histological subtypes, and a high mortality rate. Hemostatic system imbalance in patients with lung cancer often leads to increased mortality. Intracellular RNAs that share common miRNA binding sites create a competing endogenous RNA (ceRNA) network that plays an important role in gene expression regulation. The emerging role of ceRNAs in tumor development is increasingly being recognized; however, their connection to hemostatic system imbalance in lung squamous cell carcinoma (LUSC) remains unclear. In this study, RNA-seq data of LUSC and normal tissues were downloaded from the TCGA data portal. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) between LUSC and corresponding paracancerous tissues were analyzed using the DESeq2 package in R statistical software. Hemostasis-related genes linked to coagulation and complement cascades (hsa04610) and platelet activation (hsa04611) pathways were identified using the KEGGREST package. The ceRNA network associated with system hemostasis was constructed using differentially expressed RNAs (DERNAs), including mRNAs, lncRNAs, and miRNAs. The GO and KEGG enrichment analysis of DEmRNAs was conducted using the enrichR package. Hazard ratio (HR) and Kaplan-Meier curve were employed to assess the prognostic value of DERNAs using the survival and survminer packages. A ceRNA network comprising 100 hemostasis-related genes, 5 miRNAs, and 57 lncRNAs was constructed. Of these, 19 hemostasis genes, one miRNA (miR-23-3p), and 6 lncRNAs (LINC01615, LINC00707, LINC00702, FEZF1-AS1, DLX6-AS1, CLRN1-AS1) were significantly associated with prognosis in LUSC. Based on correlation analysis, MEF2C-AS1/miR-429/F8, RAP1A, GNAI2, C3AR1, F13A1, P2RY12, LCP2, C1QC axis and CASC11, CASC9, PVT1, BBOX1-AS1/ miR-23b-3p/ PLAU axis may represent key pathways involved in hemostatic system imbalance and the pathogenesis of LUSC. Our analysis revealed a complex ceRNA network associated with system hemostasis and the prognosis of LUSC. These findings may contribute to the development of personalized therapies and valuable prognostic biomarkers for LUSC patients.

Advancements in digital pathology and computer technology have spurred artificial intelligence in histopathology, but the complexity of whole slide images (WSIs) poses challenges for manual annotation and traditional supervised learning. We propose the Sample-Positive (SP) technique, which utilizes adjacent tissue morphology in WSIs to effectively sample positive examples. By integrating pathological prior information that reflects spatial adjacency of similar tissues with self-supervised learning (SSL) frameworks like SimCLR, MoCo-v3, and SinCLR, we developed an SSL method for WSI. We validated this approach on a dataset of 65 lung squamous cell carcinoma (LSCC) cases, covering four histological categories: necrosis, tumor, stroma, and epithelium. Performance was benchmarked against supervised models and original SSL frameworks using fine-tuning and linear evaluation, with metrics including accuracy (Acc), AUC, and F1 score. Our proposed SP technique outperformed baseline SSL methods in fine-tuning and linear evaluation tasks on the LSCC dataset. SPSimCLR and SPMoCo-v3 achieved the highest F1 scores, with SPSimCLR (0.9132) showing a 0.7% improvement over SimCLR (0.9067) and SPMoCo-v3 (0.9133) a 0.5% improvement over MoCo-v3 (0.9088) in fine-tuning, and SinCLR (0.9074) performs comparably to the original SSL methods. In linear evaluation, SPSimCLR (0.9082) improved F1 scores by 1.0% over SimCLR (0.8978), and SPMoCo-v3 (0.9060) improved by 1.2% over MoCo-v3 (0.8942), and SinCLR(0.9021) is surpass the original SSL methods. Ablation studies revealed that overlapping sampling slightly outperformed non-overlapping sampling, and that models trained on patches with single tissue types performed better than those trained on patches containing multiple tissue types. Overall, combining the SP technique with contrastive learning shows significant improvements in distinguishing histological categories in LSCC, making it effective for WSIs of non-diffuse cancers.

Early cancer diagnosis is essential for improving prognosis and guiding treatment. However, the high dimensionality and complexity of omics data present major challenges. Computational approaches that extract stable biomarkers and enable reliable classification across cancer types and stages are needed. A novel feature selection method, sDCFE (synergistic Discriminative Cluster-based Feature Extraction), was developed by extending Fisher-like variance analysis with a median absolute deviation (MAD) regularization term and a cluster separation component to enhance robustness and interpretability. Features selected by sDCFE were compared with those obtained from XGBoost, and the intersected set of 82 genes was evaluated through functional enrichment (KEGG, Reactome, GO BP), survival analysis (Kaplan-Meier, Cox regression), and biomarker novelty assessment against six external resources. Hybrid classification models integrating XGBoost, sDCFE, and deep learning were applied to pancancer classification, and the framework was further extended to lung squamous cell carcinoma (LUSC) staging using RNA-seq and methylation data. The overlap between sDCFE and XGBoost yielded 82 candidate biomarkers enriched in cancer-related pathways, including cell cycle regulation, immune signalling, and DNA repair. Novelty assessment stratified these genes into established, emerging, and novel categories. Six genes-HFE2, LOC339674, SERINC2, SFTA3, SOX2OT, and ACPP-emerged as the most promising candidates, supported by enrichment and survival associations across multiple cancers. The hybrid model achieved near-perfect pancancer classification on TCGA (accuracy = 99.3%, MCC = 0.992, AUC = 1.0) and demonstrated strong generalizability on PCAWG (accuracy = 94%, MCC = 0.929, AUC = 0.997). In the LUSC staging task, multiomics integration improved classification performance: the CNN-based model reached 84% accuracy, while logistic regression applied to sDCFE-ranked features achieved 88.5% accuracy with superior calibration, highlighting the robustness of the selected features. sDCFE provides a principled extension of Fisher-like methods, enabling stable and interpretable biomarker selection. When combined with XGBoost and deep learning, the framework achieves highly accurate and biologically grounded cancer classification across both cancer types and stages. The identification of novel and prognostic biomarkers, including HFE2, LOC339674, SERINC2, SFTA3, SOX2OT, and ACPP, underscores its translational potential. These results position the framework as a promising precision oncology tool to support early diagnosis, risk stratification, and treatment decision-making.

BackgroundThe aim of the study was to analyze the survival and treatment strategies of elderly patients with locally advanced lung squamous cell carcinoma.MethodsA retrospective analysis was conducted on the clinical and follow-up data of 278 patients with locally advanced lung squamous cell carcinoma admitted to the Fourth Hospital of Hebei Medical University from January 2012 to December 2019.ResultsThe 1-, 3-, 5-, and 10-year survival rates of the entire group were 86.0%, 50.6%, 40.8%, and 24.2%, respectively. There were a total of 142 cases aged 65 or elder, with 1-, 3-, 5-, and 10-year survival rates of 83.9%, 44.3%, 32.3%, and 15.6%, respectively. Among them, the progression-free survival (PFS) of the elderly group receiving radiotherapy dose ≥ 60 Gy was significantly prolonged compared to the < 60 Gy group (although P > 0.05). Comprehensive radiotherapy and chemotherapy showed a significant trend of prolonging overall survival (OS) compared to simple radiotherapy (Breslow = 0.029, log rank = 0.126). χ2 tests were performed for radiotherapy doses < 60 Gy and ≥ 60 Gy, PFS < 12 months and ≥ 12 months, OS < 36 months and ≥ 36 months, respectively. The proportion of PFS ≥ 12 months was significantly higher in the 60 Gy group (P = 0.043), and the proportion of OS ≥ 36 months was higher in the PFS ≥ 12 months group (P = 0.001).ConclusionThe prognosis of locally advanced unresectable lung squamous cell carcinoma is poor, especially in elderly patients. If the patient’s general condition is good, it is recommended to receive radiation therapy with a dose of ≥ 60 Gy and try comprehensive treatment with tolerable toxic side effects.

Patients with lung squamous cell carcinoma (LUSC) are often diagnosed at advanced stages, limiting opportunities for early intervention. LUSC develops through multistep progression from low-grade lesions to high-grade lesions, including carcinoma in situ (CIS), of which about half progress to invasive cancer while the other half regress. Although frequent mutations and copy number alterations have been documented in LUSC and observed in precursor lesions, their prognostic significance in precancers remains largely unexplored. In this study, we leveraged gene expression data from LUSC tumors in The Cancer Genome Atlas to derive transcriptional signatures corresponding to 34 key driver genomic aberrations, including mutations, amplifications, and deletions. These tumor-derived signatures were applied to precancerous datasets to assess their ability to characterize developmental stages and predict progression risk. We found many of these signatures increased progressively across lesion stages, reflecting roles in early tumorigenesis. In particular, several signatures accurately predicted which CIS lesions would progress to invasive cancer. Furthermore, these signature scores were more strongly associated with prognosis in LUSC than the presence of genomic aberrations alone. We also examined relationships between driver-associated signatures and the tumor immune microenvironment. Signature scores were significantly correlated with immune features such as immune cell infiltration and immune checkpoint gene expression, including CD274 (PD-L1). These associations varied across stages, indicating dynamic immune interactions during cancer evolution. Together, our findings demonstrate that tumor-derived driver gene expression signatures provide valuable insight into the biology and progression risk of precancerous lesions, offering potential utility for early detection and intervention in LUSC.

Squamous cell carcinoma (SCC) of the lung most often arises in the central bronchi and is strongly associated with tobacco exposure. However, peripheral squamous cell carcinoma (pSCC) is increasingly recognised as a distinct variant, often radiologically resembling adenocarcinoma and posing diagnostic difficulties, particularly in smokers with emphysema. A 76-year-old chronic smoker presented with persistent cough and weight loss. Chest X-ray revealed an ill-defined opacity in the right perihilar region, with a few thin atelectatic linear bands in the lower zones. Contrast-enhanced computed tomography (CT) of the chest showed an irregular, spiculated soft-tissue mass measuring approximately 4.4 × 3.7 cm in the superior segment of the right lower lobe, with multiple smaller spiculated nodules in the right upper and middle lobes on a background of centrilobular emphysema. Mildly enlarged right pre-paratracheal and subcarinal lymph nodes were also noted. CT-guided biopsy revealed SCC. Histopathology showed well-differentiated SCC with keratin pearls and intercellular bridges. Immunohistochemistry was positive for p40 and cytokeratin 5/6 (CK5/6) and negative for thyroid transcription factor-1 (TTF-1) and Napsin A. Peripheral squamous cell carcinoma, though less common than central SCC, should be considered in smokers presenting with spiculated peripheral pulmonary nodules. These lesions can mimic adenocarcinoma radiologically, may invade the pleura, and often require CT-guided biopsy or surgical resection for definitive diagnosis. Awareness of this variant is important to avoid misclassification and guide appropriate management.

Drug discovery requires understanding disease mechanisms, making the integration of multi-modal data essential. These data types, including omics, disease-associated, and pathway information, must be combined to uncover therapeutic insights. We developed iPANDDA, a computational pipeline that integrates these data through a network-based approach to predict candidate drug targets for specific diseases. We applied iPANDDA to lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer representing ~25% of global cases. Despite advances in cancer therapeutics, targeted treatments for LUSC remain limited, partly due to a lack of robust models to study carcinogenesis and therapeutic response. The SOX2 gene, amplified in ~50% of patients, plays a critical role in sustaining the cancer phenotype. Using iPANDDA, we identified and validated SOX2-dependent therapeutic targets. In vitro inhibition studies confirmed AKT and mTOR complexes as key monotherapy and combination therapy targets and revealed pathways for SOX2-targeted combination therapies.