Subtype Of Non-small Cell Lung Cancer Research Articles

Hiding in plain sight: NUT carcinoma is an unrecognized subtype of squamous cell carcinoma of the lungs and head and neck.

In the past two decades, treatment for non-small-cell lung cancers (NSCLCs) and head and neck squamous cell carcinoma (HNSCC) has advanced considerably, owing largely to the characterization of distinct oncological subtypes, the development of targeted therapies for each subtype and the advent of immunotherapy. Data emerging over the past two decades suggest that NUT carcinoma, a highly aggressive malignancy driven by a NUT fusion oncoprotein and arising in the lungs, head and neck, and rarely in other sites, is a squamous cell carcinoma (SCC) based on transcriptional, histopathological, cell-of-origin and molecular characteristics. NUT carcinoma has an estimated incidence of 1,400 cases per year in the United States, surpassing that of some rare NSCLC and HNSCC subtypes. However, NUT carcinoma is currently not recognized as an SCC of the lungs or head and neck. The orphan classification of NUT carcinoma as a distinct entity leads to a lack of awareness of this malignancy among oncologists and surgeons, despite early diagnosis being crucial for this cancer type with a median survival of only ~6.5 months. Consequently, NUT carcinoma is underdiagnosed and often misdiagnosed, resulting in limited research and progress in developing effective treatments in one of the most aggressive forms of lung and head and neck cancer. With a growing number of targeted agents that can potentially be used to treat NUT carcinoma, improved recognition through reclassification and inclusion of NUT carcinoma as a squamous NSCLC or an HNSCC when arising in these locations will accelerate the development of effective therapies for this disease. Thus, in the Perspective, we propose such a reclassification of NUT carcinoma as an SCC and discuss the supporting evidence.

Lung cancer in females: Military cancer center experience in Jordan

Objective: to evaluate the clinical properties, pathological properties and the survival data for 80 lung cancer female patients who were managed in our center. Methods: this retrospective study included 80 female patients with lung cancer that were treated in The Military Cancer Center of the Jordanian Royal Medical services between January 2022 and January 2024, the patients pathological, clinical and survival data will be reviewed and analyzed. Results: group age was between 41 to 82 years (mean of 60.18 ± 11.72). 82.5% of patients were diagnosed with non-small cell lung cancer (NSCLC), 17.5% of patients had small cell lung cancer (SCLC), 30% of patients were smokers. Significant weight loss was observed in 40% of patients. Comorbidity was present in 43.75% of patients with hypertension being the most common one 36.25%. Adenocarcinoma was the most common histological subtype 56%. The one-year survival rate was 70% and the two-year survival rate was 37.5%. Conclusion: our study showed that advanced stage, weight loss, poor ECOG score and NSCLC subtype were a main prognostic factors in females with lung cancer in Jordan.

Real‑world therapeutic strategies and survival outcomes in advanced HER2-mutant non-small cell lung cancer.

Limited information is available regarding the clinical features and outcomes of advanced human epidermal growth factor receptor 2 (HER2)-mutant non-small cell lung cancer (NSCLC) in Taiwan, despite expanding treatment options for this distinct subtype. The present study explored the clinical features and outcomes of HER2-mutant NSCLC in a real-world setting. Relevant data were collected from patients with advanced or recurrent HER2-mutant NSCLC who received systemic therapy between 2011 and 2021 and were followed up until 2022 at two medical centers in Taiwan. Clinical features, treatment responses, and survival-associated factors were analyzed. This study included 45 patients (median age: 59.7 [range: 41.3-78.7] years). A775_G776insYVMA was the most common NSCLC subtype (57.8%), followed by G778_P780dup (11.1%). Approximately 53.3% of the patients received first-line platinum-based chemotherapy (PC) alone, whereas 13.3% received PC combined with an immune checkpoint inhibitor (ICI). The median overall survival (OS) and progression-free survival (PFS) after first-line therapy were 25.8 and 4.4 months, respectively. The objective response rate was generally higher in patients receiving first-line PC + ICI than in those receiving PC alone (33.3% vs. 12.5%; p = 0.269). Furthermore, patients receiving PC + ICI had longer PFS than did those receiving PC alone (9.5 vs. 4.4 months; p = 0.131) and those receiving tyrosine kinase inhibitor/ICI monotherapy (9.5 vs. 0.5 months; p = 0.015). Compared with patients having other NSCLC subtypes, those carrying HER2 exon 20 insertion mutations had shorter median PFS (17.3 vs. 2.9 months; p = 0.043) and OS (not reached vs. 19 months; p = 0.031). This study highlights the clinical features and outcomes of advanced HER2-mutant NSCLC in Taiwan. PC + ICI may be more effective than other regimens as first-line therapy. The prognostic role of HER2 exon 20 insertion mutations warrants further investigation.

Sequential Proteomic and N-Glycoproteomic Analyses of Bronchoalveolar Lavage Fluids for Potential Biomarker Discovery of Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of nonsmall-cell lung cancer. Herein, a multiomics method, which combined proteomic and N-glycoproteomic analyses, was developed to analyze the normal and cancerous bronchoalveolar lavage fluids (BALFs) from six LUAD patients to identify potential biomarkers of LUAD. The data-independent acquisition proteomic analysis was first used to analyze BALFs, which identified 59 differentially expressed proteins (DEPs). The bioinformatic analyses of 59 DEPs have shown that a potential marker protein, beta-1,4-galactosyltransferase 1 (B4GALT1), was consistently downregulated in all cancerous lung lobes (CLLs). As the downregulation of B4GALT1 may indicate changes in protein N-glycosylation, site-specific N-glycoproteome analysis of BALFs from the normal lung lobes (NLLs) and CLLs was further performed by using a fully automated glycopeptide enrichment and separation system. Comparing the glycan structures containing free GlcNAc in BALFs between NLLs and CLLs qualitatively, the percentage of unique glycan structure for free GlcNAc existing only in NLLs was 52.8%, which was significantly higher than the 46.3% existing only in CLLs. Furthermore, the sequential proteomic and N-glycoproteomic analyses allowed us to identify a panel of functionally related potential biomarkers consisting of one protein (B4GALT1) and four glycoproteins (NFKB1, F2, LTF, and DLD).

The X-Linked Tumor Suppressor TSPX Regulates Genes Involved in the EGFR Signaling Pathway and Cell Viability to Suppress Lung Adenocarcinoma.

Background: TSPX is an X-linked tumor suppressor that was initially identified in non-small cell lung cancer (NSCLC) cell lines. However, its expression patterns and downstream mechanisms in NSCLC remain unclear. This study aims to investigate the functions of TSPX in NSCLC by identifying its potential downstream targets and their correlation with clinical outcomes. Methods: RNA-seq transcriptome and pathway enrichment analyses were conducted on the TSPX-overexpressing NSCLC cell lines, A549 and SK-MES-1, originating from lung adenocarcinoma and squamous cell carcinoma subtypes, respectively. In addition, comparative analyses were performed using the data from clinical NSCLC specimens (515 lung adenocarcinomas and 502 lung squamous cell carcinomas) in the Cancer Genome Atlas (TCGA) database. Results: TCGA data analysis revealed significant downregulation of TSPX in NSCLC tumors compared to adjacent non-cancerous tissues (Wilcoxon matched pairs signed rank test p < 0.0001). Notably, the TSPX expression levels were inversely correlated with the cancer stage, and higher TSPX levels were associated with better clinical outcomes and improved survival in lung adenocarcinoma, a subtype of NSCLC (median survival extended by 510 days; log-rank test, p = 0.0025). RNA-seq analysis of the TSPX-overexpressing NSCLC cell lines revealed that TSPX regulates various genes involved in the cancer-related signaling pathways and cell viability, consistent with the suppression of cell proliferation in cell culture assays. Notably, various potential downstream targets of TSPX that correlated with patient survival (log-rank test, p = 0.016 to 4.3 × 10-10) were identified, including EGFR pathway-related genes AREG, EREG, FOSL1, and MYC, which were downregulated. Conclusions: Our results suggest that TSPX plays a critical role in suppressing NSCLC progression by downregulating pro-oncogenic genes, particularly those in the EGFR signaling pathway, and upregulating the tumor suppressors, especially in lung adenocarcinoma. These findings suggest that TSPX is a potential biomarker and therapeutic target for NSCLC management.

Comparative Analysis of Transcriptomic and Proteomic Expression between Two Non-Small Cell Lung Cancer Subtypes.

Non-small cell lung cancer (NSCLC) is frequently diagnosed late and has poor survival. The two predominant subtypes of NSCLC, adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), are currently differentially diagnosed using immunohistochemical markers; however, they are increasingly recognized as very different cancer types suggestive of potential for new, more targeted therapies. There are extensive efforts to find more precise and noninvasive differential diagnostic tools. Here, we examined these two NSCLC subtypes for differences that may inform treatment and identify potential novel therapeutic pathways. We presented a comparative analysis of transcriptomic and proteomic expression in tumors from a cohort of 22 NSCLC patients: 8 LUSC and 14 LUAD. Comparing NSCLC subtypes, we found differential gene expression related to cell differentiation for LUSC and cellular structure and immune response regulation for LUAD. Differential protein expression between NSCLC subtypes was related to extracellular structure for LUSC and metabolic processes, including glucose metabolism for LUAD. This direct comparison was more informative about subtype-specific pathways than between each subtype and control (nontumor) tissues. Many of our observations between NSCLC subtypes support and inform existing observations and reveal differences that may aid research seeking to identify and validate novel subtype biomarkers or druggable targets.

Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images.

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings.

Case report: Therapeutic response of front-line cadonilimab plus chemotherapy on patient with advanced lung adenocarcinoma harboring STK11 genetic aberration

The STK11 gene mutation is a common genetic alteration in non-small cell lung cancer (NSCLC) and is significantly associated with poor responses to current immunotherapy regimens. Despite its prevalence, there is currently no established standard for front-line treatment in this subtype of NSCLC, underscoring the increasing need for personalized therapeutic strategies. In this report, we present a case of a patient with STK11-mutant NSCLC who was treated with first-line cadonilimab (10mg/kg) in combination with pemetrexed (500mg/m^2) plus carboplatin (AUC=5), resulting in a notable extension of progression-free survival (PFS). This case highlights the potential efficacy and feasibility of combining immunotherapy with chemotherapy in patients with STK11-mutant NSCLC. Additionally, we provide a review of recent advancements in research related to STK11 mutations in lung cancer as reported in the literature.

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC samples relative to adjacent healthy tissues using an explainable artificial intelligence (XAI) framework. The LASSO algorithm was employed to identify the top gene features in the LUAD and LUSC datasets. An ensemble-based extreme gradient boosting (XGBoost) machine learning (ML) algorithm was trained and interpreted using SHapley Additive exPlanations (SHAP), with top features undergoing biological annotation through survival and functional enrichment analyses. The XAI-based SHAP module addresses the opaque nature of ML models. Notably, 35 and 33 genes were identified for LUAD and LUSC, respectively, using the LASSO algorithm. Performance metrics such as average accuracy and Matthew's correlation coefficient were evaluated. The XGBoost model demonstrated an average accuracy of 99.1 % for LUAD and 98.6 % for LUSC. The SFTPC gene emerged as the most significant feature across both NSCLC subtypes. For LUAD, genes such as STX11, CLEC3B, EMP2, and LYVE1 significantly influenced the XAI-SHAP framework. Conversely, GKN2, OGN, SLC39A8, and MMRN1 were identified for LUSC. Survival analysis and functional validation of these genes highlighted the physiological functions observed to be dysregulated in the NSCLC subtypes. These identified genes have the potential to enhance current medical diagnostics and therapeutics.

Methylation status of selected genes in non-small cell lung carcinoma - current knowledge and future perspectives.

DNA methylation is recognized as an early event in cancer initiation and progression. This review aimed to compare the methylation status of promoter regions in selected genes across different histological subtypes of non-small cell lung cancer (NSCLC), including adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and the rare but highly aggressive large-cell neuroendocrine carcinoma (LCNEC). A comprehensive literature search was conducted in the PubMed database until August 17, 2024, using standardized keywords to identify reports on promoter methylation in NSCLC. Seventy-five studies were reviewed, focusing on the promoter methylation of key genes, such as APC, BRCA1, CDH1, CDH13, DAPK1, DLEC1, FHIT, GSTP1, hMLH1, MGMT, CDKN2A, RARβ, RASSF1, RUNX3, and TIMP3. These studies explored diagnostic, prognostic, epidemiological, and therapeutic aspects across NSCLC subtypes. Additionally, mutational profiles of TP53, RB1, KEAP1, and STK11 and expression patterns of ASCL1, DLL3, and NOTCH were analyzed. The findings suggest that LCNEC may serve as a biological bridge between non-small cell and small-cell lung carcinoma. Our analysis highlights that the methylation status of selected genes could enhance diagnosis, prognosis, and personalized treatment strategies in patients with NSCLC, particularly those with LCNEC.

Protocol of a single-arm, multicenter, phase III trial for selective lymph node dissection in cT1N0M0 invasive non-small cell lung cancer with consolidation-tumor ratio >0.5 located in the apical segment: Eastern Cooperative Thoracic Oncology Projects ECTOP-1018 (SELLAS study).

Systemic lymph node dissection (LND) is the standard procedure for operable invasive non-small cell lung cancer (NSCLC), for radical cure and lymph node (LN) staging. However, its necessity is controversial in early-stage patients without LN metastasis, as evidence shows it could not improve prognosis and may cause surgical complications. We initiated a prospective, multi-center, single-arm, phase III trial to confirm the non-inferior survival rate of surgery sparing the lower mediastinal LND in apical cT1N0M0 invasive NSCLC with consolidation-tumor ratio (CTR) >0.5 at high-resolution computed tomography (CT) scan. We plan to enroll a total of 634 patients with invasive NSCLC with predominantly non-lepidic subtype confirmed by intraoperative frozen pathology, who will receive radical lung cancer surgery but waiver lower mediastinal LND. The primary endpoint is recurrence-free survival (RFS), the secondary endpoints are overall survival (OS) rate of these patients, metastatic distribution of LNs in different histological subtypes of NSCLC, and consistency between frozen and paraffin pathology reports in determining the tumor invasiveness. The results of this study will validate the necessity of LND in the lower mediastinum in the patients with early-stage NSCLC located in the apical segment with CTR >0.5 at CT scan. ClinicalTrials.gov Identifier NCT06031246.

Proteogenomic analysis reveals non-small cell lung cancer subtypes predicting chromosome instability, and tumor microenvironment

Non-small cell lung cancer (NSCLC) is histologically classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC). However, some tumors are histologically ambiguous and other pathophysiological features or microenvironmental factors may be more prominent. Here we report integrative multiomics analyses using data for 229 patients from a Korean NSCLC cohort and 462 patients from previous multiomics studies. Histological examination reveals five molecular subtypes, one of which is a NSCLC subtype with PI3K-Akt pathway upregulation, showing a high proportion of metastasis and poor survival outcomes regardless of any specific NSCLC histology. Proliferative subtypes are present in LUAD and LSCC, which show strong associations with whole genome doubling (WGD) events. Comprehensive characterization of the immune microenvironment reveals various immune cell compositions and neoantigen loads across molecular subtypes, which predicting different prognoses. Immunological subtypes exhibit a hot tumor-enriched state and a higher efficacy of adjuvant therapy.

The electronic nose in lung cancer diagnostics: A systematic review and meta-analysis

BackgroundLung cancer remains a leading cause of cancer-related deaths. Early-stage detection is pivotal for curative interventions, yet most cases are diagnosed at advanced stages. Non-invasive point-of-care diagnostic strategies are needed to reduce lung cancer mortality rates. We assess the potential of electronic nose (eNose) devices as a tool for lung cancer detection.Study design and methodsAll online available databases were searched. Following PRISMA guidelines, we reviewed studies utilising eNose forin vivoexhaled air analysis in lung cancer patients. Included patients had pathological confirmed lung cancer, both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in various stages. Primary outcomes were sensitivity, specificity, negative predictive value and area under the curve. Secondary outcomes evaluated eNose performance in distinguishing NSCLC subtypes and SCLC. Additionally, a subgroup analysis was conducted to evaluate the efficacy of various commonly used sensors in this differentiation. The QUADAS-2 tool was used for risk-of-bias assessment.ResultsThirty-five studies, comprising 4483 patients, were included. Pooled sensitivity and specificity were 0.90 (CI 0.87–0.93) and 0.89 (0.85–0.93), respectively. Median negative predictive value was 0.93 and median area under the curve was 0.91. Both the likelihood of bias and concerns regarding applicability were minimal.InterpretationeNose devices hold promise as non-invasive, point-of-care tools for lung cancer diagnosis. Due to high sensitivity and negative predictive value, and great ease of use they might be valuable additions to the diagnostic toolbox. Further research and validation efforts are critical to maximise their impact on early lung cancer detection.

A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images.

Background/Objectives: Despite recent advances in research, cancer remains a significant public health concern and a leading cause of death. Among all cancer types, lung cancer is the most common cause of cancer-related deaths, with most cases linked to non-small cell lung cancer (NSCLC). Accurate classification of NSCLC subtypes is essential for developing treatment strategies. Medical professionals regard tissue biopsy as the gold standard for the identification of lung cancer subtypes. However, since biopsy images have very high resolutions, manual examination is time-consuming and depends on the pathologist's expertise. Methods: In this study, we propose a hybrid model to assist pathologists in the classification of NSCLC subtypes from histopathological images. This model processes deep, textural and contextual features obtained by using EfficientNet-B0, local binary pattern (LBP) and vision transformer (ViT) encoder as feature extractors, respectively. In the proposed method, each feature matrix is flattened separately and then combined to form a comprehensive feature vector. The feature vector is given as input to machine learning classifiers to identify the NSCLC subtype. Results: We set up 13 different training scenarios to test 4 different classifiers: support vector machine (SVM), logistic regression (LR), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). Among these scenarios, we obtained the highest classification accuracy (99.87%) with the combination of EfficientNet-B0 + LBP + ViT Encoder + SVM. The proposed hybrid model significantly enhanced the classification accuracy of NSCLC subtypes. Conclusions: The integration of deep, textural, and contextual features assisted the model in capturing subtle information from the images, thereby reducing the risk of misdiagnosis and facilitating more effective treatment planning.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data.

To develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data. This study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC. Following the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance. We developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

Loss of CDKN2A Enhances the Efficacy of Immunotherapy in EGFR-Mutant Non-Small Cell Lung Cancer.

Mutant EGFR is a common driver of non-small cell lung cancer (NSCLC). Although mutant EGFR has been reported to limit the efficacy of immunotherapy, a subset of patients with EGFR-mutant NSCLC benefit from treatment with immune checkpoint inhibitors. A better understanding of how co-occurring genomic alterations in oncogenic driver genes impact immunotherapy efficacy may provide a more complete understanding of cancer heterogeneity and identify biomarkers of response. Here, we investigated the effects of frequent EGFR co-mutations in EGFR-mutant lung cancer models and identified loss-of-function mutation of CDKN2A as a potential sensitizer to anti-PD-1 treatment in vitro and in vivo. Mechanistically, CDKN2A loss impacted the composition of the tumor immune microenvironment by promoting the expression of PD-L2 through reduced ubiquitination of c-MYC, and mutant EGFR cooperating to upregulate c-MYC and PD-L2 by activating the MAPK pathway. Blocking PD-L2 induced antitumor immune responses mediated by CD8+ T cells in EGFR/CDKN2A co-mutated lung cancer. Importantly, a small-molecule PD-L2 inhibitor, zinc undecylenate, remodeled the tumor immune microenvironment of EGFR/CDKN2A co-mutant tumors and enhanced the antitumor efficacy of EGFR tyrosine kinase inhibitors. Collectively, these results identify EGFR/CDKN2A co-mutation as a distinct subtype of NSCLC that shows superior sensitivity to immune checkpoint blockade and reveals a potential combined therapeutic strategy for treating this NSCLC subtype. Significance: Upregulation of c-MYC driven by co-mutation of CDKN2A and EGFR increases PD-L2 to abrogate CD8+ T-cell activity in lung cancer, which confers sensitivity to PD-L2 blockade in combination with tyrosine kinase inhibitors.

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool

ObjectivesThe objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to currently popular deep-learning models. Material and methodsData were collected from six publicly available datasets in The Cancer Imaging Archive (TCIA). Following the inclusion and exclusion criteria, a total of 4548 CT slices from 758 cases were allocated. We evaluated multiple multi-task learning models that integrate attention mechanisms to resolve challenges in NSCLC subtype classification and clinical staging. These models utilized convolution-based modules in their shared layers for feature extraction, while the task layers were dedicated to histological subtype classification and staging. Each branch sequentially processed features through convolution-based and attention-based modules prior to classification. ResultsOur study evaluated 758 NSCLC patients (mean age, 66.2 years ± 10.3; 473 men), spanning ADC and SCC cases. In the classification of histological subtypes and clinical staging of NSCLC, the MobileNet-based multi-task learning model enhanced with attention mechanisms (MN-MTL-A) demonstrated superior performance, achieving Area Under the Curve (AUC) scores of 0.963 (95 % CI: 0.943, 0.981) and 0.966 (95 % CI: 0.945, 0.982) for each task, respectively. The model significantly surpassed its counterparts lacking attention mechanisms and those configured for single-task learning, as evidenced by P-values of 0.01 or less for both tasks, according to DeLong’s test. ConclusionsThe integration of attention encoder blocks into our multi-task learning network significantly enhanced the accuracy of NSCLC histological subtyping and clinical staging. Given the reduced reliance on precise radiologist annotation, our proposed model shows promising potential for clinical application.

The value of multiple diffusion metrics based on whole-lesion histogram analysis in evaluating the subtypes and proliferation status of non-small cell lung cancer.

Limited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67. Patients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann-Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman's or Pearson's methods. The D 10th percentile, D 90th percentile, D mean, D median, Dapp 10th percentile, Dapp 90th percentile, Dapp mean, Dapp median, Dapp skewness, Dapp SD of the AC groups were significantly higher than those of the SCC groups, while the Kapp entropy and Kapp SD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dapp mean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp 90thpercentile, Dapp mean, Kapp skewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05). Our study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dapp mean, Dapp 90th percentile, and Kapp skewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning.

Genetic profile of ferroptosis in non-small cell lung carcinoma and pharmaceutical options for ferroptosis induction.

Lung cancer (LC) is the leading cause of cancer-related deaths and the second most commonly diagnosed malignancy worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell LC (LUSCC) are the most common subtypes of non-small cell LC (NSCLC). Early diagnosis of LC can be challenging due to a lack of biomarkers. The overall survival (OS) of patients with NSCLC is still poor despite the enormous efforts that have been made to develop novel treatments. Understanding fundamental molecular and genetic mechanisms is necessary to develop new therapeutic approaches for NSCLC. A recently identified type of programmed cell death known as ferroptosis is one potential approach. Ferroptosis causes oxidative damage and the death of cancerous cells by peroxidizing unsaturated phospholipids and accumulating reactive oxygen species (ROS) in an iron-dependent manner. Ferroptosis-related gene (FRG) signatures have recently been evaluated for their ability to predict patient OS and prognosis. These analyses show FRGs are involved in cancer progression, and may serve as promising biomarkers for tumor diagnosis and therapy. Moreover, we summarize the current pharmaceutical options of ferroptosis induction and their underlying molecular mechanism in LC. Therefore, this review aims to provide a comprehensive summary of FRG-based prognostic models, their associated metabolic and signaling pathways, and promising therapeutic options for ferroptosis induction in NSCLC.

Foxd3/SLC5A6 axis regulates apoptosis in LUAD cells by controlling mitochondrial biotin uptake

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 85 % of cases. Lung adenocarcinoma (LUAD) is the most common subtype of NSCLC, and while targeted therapies and immune checkpoint inhibitors have improved outcomes, many patients exhibit resistance, necessitating the development of novel treatments. This study explores the role of the SLC5A6 gene, which encodes a sodium-dependent multivitamin transporter critical for mitochondrial function, in LUAD progression. We found that SLC5A6 is significantly upregulated in LUAD tissues and is associated with poor prognosis. Overexpression of SLC5A6 enhanced cell proliferation and migration, while knockout of SLC5A6 impaired these processes and induced apoptosis by disrupting mitochondrial function. Additionally, we identified Foxd3 as a key transcription factor regulating SLC5A6 expression. In vivo experiments demonstrated that SLC5A6 knockout effectively inhibited tumor growth. These findings suggest that SLC5A6 is a potential therapeutic target for LUAD, offering a new avenue for treatment strategies.