Diffuse coexpression of thyroid transcription factor-1 and p40 in a non-small cell carcinoma of the lung: Second case in the female population / Coexpresión difusa del factor de transcripción tiroidea 1 y p40 en un carcinoma de célula no pequeña del pulmón: segundo caso en la población de sexo femenino

Some non-small cell carcinomas of the lung can express TTF1 and p40 in the same tumor cells. This event has been described in only six cases prior to this one, and only in one other female. It is an extraordinary event that appears as a new entity yet to be defined. The case presented is a woman with a non-small cell lung carcinoma with diffuse coexpression of TTF1 and p40 in the same cells. (AU)

Algunos carcinomas de célula no pequeña del pulmón pueden expresar TTF1 y p40 en las mismas células tumorales. Este evento se ha descrito únicamente en 6 casos anteriores a este, y solo en otra persona del sexo femenino. Se trata de un evento extraordinario que se muestra como una nueva entidad todavía por definir. El caso que se presenta versa sobre una mujer con un carcinoma de pulmón de célula no pequeña con coexpresión difusa en las mismas células de TTF1 y p40. (AU)

Nicotinamide in Combination with EGFR-TKIs for the Treatment of Stage IV Lung Adenocarcinoma with EGFR Mutations: A Randomized Double-Blind (Phase IIb) Trial.

PURPOSE: RUNX3 is a tumor suppressor gene, which is inactivated in approximately 70% of lung adenocarcinomas. Nicotinamide, a sirtuin inhibitor, has demonstrated potential in re-activating epigenetically silenced RUNX3 in cancer cells. This study assessed the therapeutic benefits of combining nicotinamide with first-generation EGFR-tyrosine kinase inhibitors (TKI) for patients with stage IV lung cancer carrying EGFR mutations. PATIENTS AND METHODS: We assessed the impact of nicotinamide on carcinogen-induced lung adenocarcinomas in mice and observed that nicotinamide increased RUNX3 levels and inhibited lung cancer growth. Subsequently, 110 consecutive patients with stage IV lung cancer who had EGFR mutations were recruited: 70 females (63.6%) and 84 never-smokers (76.4%). The patients were randomly assigned to receive either nicotinamide (1 g/day, n = 55) or placebo (n = 55). The primary and secondary endpoints were progression-free survival (PFS) and overall survival (OS), respectively. RESULTS: After a median follow-up of 54.3 months, the nicotinamide group exhibited a median PFS of 12.7 months [95% confidence interval (CI), 10.4-18.3], while the placebo group had a PFS of 10.9 months (9.0-13.2; P = 0.2). The median OS was similar in the two groups (31.0 months with nicotinamide vs. 29.4 months with placebo; P = 0.2). Notably, subgroup analyses revealed a significant reduction in mortality risk for females (P = 0.01) and never-smokers (P = 0.03) treated with nicotinamide. CONCLUSIONS: The addition of nicotinamide with EGFR-TKIs demonstrated potential improvements in PFS and OS, with notable survival benefits for female patients and those who had never smoked (ClinicalTrials.gov Identifier: NCT02416739).

Integrative single-cell analysis of LUAD: elucidating immune cell dynamics and prognostic modeling based on exhausted CD8+ T cells.

Background: The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD. Method: Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2. Results: Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment. Conclusion: The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Background: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. Methods: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Results: Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Conclusion: Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Exploring the Mechanism of Zilongjin in Treating Lung Adenocarcinoma Based on Network Pharmacology Combined with Experimental Verification.

Zilongjin (ZLJ) is a common traditional Chinese medicine for lung adenocarcinoma (LUAD) treatment. However, its mechanisms of action remain to be elucidated. Network pharmacology was used to explore the underlying mechanisms of ZLJ on LUAD treatment. The disease-related targets were determined from the Gene-Cards and DisGeNET databases. Active compounds and targets of ZLJ were obtained from the HIT, TCMSP, and TCMID databases. Then the protein-protein interaction (PPI) network was built by the STRING database to identify core-hub targets of ZLJ in LUAD. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to analyze the enriched regulatory pathways of targets. Molecular docking analysis was used to evaluate interactions between potential targets and active compounds. Finally, qRT-PCR was used to further verify the results of network pharmacology. A total of 124 LUAD-related targets of ZLJ and 5 active compounds of ZLJ from the relevant databases were screened out. Among these target proteins, JUN, CDH1, PPARG, and FOS were core hub-genes in the PPI network. GO and KEGG pathway enrichment analysis indicated that these targets might regulate the PPAR signaling pathway in LUAD. JUN, PPARG, and FOS levels were upregulated, while CDH1 level was downregulated in LUAD cells. This study discerned that ZLJ may target genes such as JUN, FOS, PPARG, and CDH1 via the PPAR signaling pathway in LUAD, offering foundational insights for further exploration of ZLJ in clinical applications.

RACGAP1 promotes lung cancer cell proliferation through the PI3K/AKT signaling pathway.

We aimed to investigate the expression and clinic significance of Rac GTPase Activating Protein 1 (RACGAP1) in human lung adenocarcinoma (LUAD). Online database analysis revealed a significant increase in RACGAP1 mRNA expression among 26 types of tumor tissues, including LUAD tissues. Online database and tissue microarray analyses indicated that RACGAP1 expression was significantly upregulated in LUAD tissues. Genetic variation analysis identified four different genetic variations of RACGAPs in LUAD. Moreover, online database analysis showed that RACGAP1 upregulation was correlated with shorter survival in patients with LUAD. After silencing RACGAP1 expression in A549 cells using siRNA and assessing its protein levels via Western blotting, we found that RACGAP1 knockdown inhibited cell growth and induced apoptosis determined using the Cell Counting Kit-8 assay, colony formation assay, and flow cytometry. Mechanistically, western blot analysis indicated that Bax expression increased, whereas Bcl-2 expression decreased. Moreover, RACGAP1 knockdown attenuated PI3K/AKT pathway activation in lung cancer cells. Taken together, our findings showed that RACGAP1 was overexpressed in LUAD tissues and played an important role in lung cancer by increasing cell growth through the PI3K/AKT signaling pathway. This study suggests recommends evaluating RACGAP1 in clinical settings as a novel biomarker and potential therapeutic target for lung cancer.

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.

Comprehensive Analysis of Lung Adenocarcinoma and Brain Metastasis through Integrated Single-Cell Transcriptomics.

Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer, comprising approximately half of all cases. It is often diagnosed at advanced stages with brain metastasis (BM), resulting in high mortality rates. Current BM management involves complex interventions and conventional therapies that offer limited survival benefits with neurotoxic side effects. The tumor microenvironment (TME) is a complex system where cancer cells interact with various elements, significantly influencing tumor behavior. Immunotherapies, particularly immune checkpoint inhibitors, target the TME for cancer treatment. Despite their effectiveness, it is crucial to understand metastatic lung cancer and the specific characteristics of the TME, including cell-cell communication mechanisms, to refine treatments. Herein, we investigated the tumor microenvironment of brain metastasis from lung adenocarcinoma (LUAD-BM) and primary tumors across various stages (I, II, III, and IV) using single-cell RNA sequencing (scRNA-seq) from publicly available datasets. Our analysis included exploring the immune and non-immune cell composition and the expression profiles and functions of cell type-specific genes, and investigating the interactions between different cells within the TME. Our results showed that T cells constitute the majority of immune cells present in primary tumors, whereas microglia represent the most dominant immune cell type in BM. Interestingly, microglia exhibit a significant increase in the COX pathway. Moreover, we have shown that microglia primarily interact with oligodendrocytes and endothelial cells. One significant interaction was identified between DLL4 and NOTCH4, which demonstrated a relevant association between endothelial cells and microglia and between microglia and oligodendrocytes. Finally, we observed that several genes within the HLA complex are suppressed in BM tissue. Our study reveals the complex molecular and cellular dynamics of BM-LUAD, providing a path for improved patient outcomes with personalized treatments and immunotherapies.

Dichotomous Effects of Glypican-4 on Cancer Progression and Its Crosstalk with Oncogenes.

Glypicans are linked to various aspects of neoplastic behavior, and their therapeutic value has been proposed in different cancers. Here, we have systematically assessed the impact of GPC4 on cancer progression through functional genomics and transcriptomic analyses across a broad range of cancers. Survival analysis using TCGA cancer patient data reveals divergent effects of GPC4 expression across various cancer types, revealing elevated GPC4 expression levels to be associated with both poor and favorable prognoses in a cancer-dependent manner. Detailed investigation of the role of GPC4 in glioblastoma and non-small cell lung adenocarcinoma by genetic perturbation studies displays opposing effects on these cancers, where the knockout of GPC4 with CRISPR/Cas9 attenuated proliferation of glioblastoma and augmented proliferation of lung adenocarcinoma cells and the overexpression of GPC4 exhibited a significant and opposite effect. Further, the overexpression of GPC4 in GPC4-knocked-down glioblastoma cells restored the proliferation, indicating its mitogenic effect in this cancer type. Additionally, a survival analysis of TCGA patient data substantiated these findings, revealing an association between elevated levels of GPC4 and a poor prognosis in glioblastoma, while indicating a favorable outcome in lung carcinoma patients. Finally, through transcriptomic analysis, we attempted to assign mechanisms of action to GPC4, as we find it implicated in cell cycle control and survival core pathways. The analysis revealed upregulation of oncogenes, including FGF5, TGF-ß superfamily members, and ITGA-5 in glioblastoma, which were downregulated in lung adenocarcinoma patients. Our findings illuminate the pleiotropic effect of GPC4 in cancer, underscoring its potential as a putative prognostic biomarker and indicating its therapeutic implications in a cancer type dependent manner.

Integrated analysis highlights the significance role of ITGAL in lung adenocarcinoma.

Integrin alpha L (ITGAL), a member of the integrin family, is associated with carcinogenesis and immune regulation. However, the biological functions of ITGAL in lung adenocarcinoma (LUAD) remain poorly understood. In this study, we utilized the TCGA dataset to analyse ITGAL mRNA expression in LUAD and examined its correlation with clinical prognosis. Three-dimensional (3D) Matrigel culture, 5-bromodeoxyuridine (BrdU) ELISA, wound-healing migration and cell adherence assays were used to demonstrate the potential role of ITGAL in LUAD progression. Additionally, we analysed single-cell sequencing data of LUAD to determine the expression and biological function of ITGAL. Our research revealed that the expression of ITGAL in LUAD samples is an independent predictor of prognosis. Patients with high expression of ITGAL had significantly better overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS) compared to the low-expression group. Meanwhile, the expression of ITGAL suppressed malignant progression in LUAD cells. Functional enrichment analyses showed that ITGAL was significantly correlated with cell immune response and immune checkpoint, consistent with the analysis of single-cell sequencing in paired samples of normal and tumour. Furthermore, we confirmed that ITGAL expression affect the tumour microenvironment (TME) through regulation of the expression of cytokines in NK cells of LUAD. In summary, ITGAL is a prognostic biomarker for LUAD patients, and it repressed malignant progression in LUAD cells. Moreover, ITGAL expression also enhanced the effect of immunotherapy and may be an important target in LUAD therapy.

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.

OBJECTIVE: To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma. METHODS: Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness. RESULTS: In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group). CONCLUSIONS: Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.

ESCO2's oncogenic role in human tumors: a pan-cancer analysis and experimental validation.

PURPOSE: Establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the mitotic S-phase adhesins acetylation and is responsible for bridging two sister chromatids. However, present ESCO2 cancer research is limited to a few cancers. No systematic pan-cancer analysis has been conducted to investigate its role in diagnosis, prognosis, and effector function. METHODS: We thoroughly examined the ESCO2 carcinogenesis in pan-cancer by combining public databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), UALCAN and Tumor Immune Single-cell Hub (TISCH). The analysis includes differential expression analysis, survival analysis, cellular effector function, gene mutation, single cell analysis, and tumor immune cell infiltration. Furthermore, we confirmed ESCO2's impacts on clear cell renal cell carcinoma (ccRCC) cells' proliferative and invasive capacities in vitro. RESULTS: In our study, 30 of 33 cancer types exhibited considerably greater levels of ESCO2 expression in tumor tissue using TCGA and GTEx databases, whereas acute myeloid leukemia (LAML) exhibited significantly lower levels. Kaplan-Meier survival analyses in adrenocortical carcinoma (ACC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), and pancreatic adenocarcinoma (PAAD) demonstrated that tumor patients with high ESCO2 expression have short survival periods. However, in thymoma (THYM), colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), ESCO2 was a favorable prognostic factor. Moreover, ESCO2 expression positively correlates with tumor stage and tumor size in several cancers, including LIHC, KIRC, KIRP and LUAD. Function analysis revealed that ESCO2 participates in mitosis, cell cycle, DNA damage repair, and other processes. CDK1 was identified as a downstream gene regulated by ESCO2. Furthermore, ESCO2 might also be implicated in immune cell infiltration. Finally, ESCO2'S knockdown significantly inhibited the A498 and T24 cells' proliferation, invasion, and migration. CONCLUSIONS: In conclusion, ESCO2 is a possible pan-cancer biomarker and oncogene that can reliably predict the prognosis of cancer patients. ESCO2 was also implicated in the cell cycle and proliferation regulation. In a nutshell, ESCO2 is a therapeutically viable and dependable target.

Targeted therapeutic options in early and metastatic NSCLC-overview.

The complex therapeutic strategy of non-small cell lung cancer (NSCLC) has changed significantly in recent years. Disease-free survival increased significantly with immunotherapy and chemotherapy registered in perioperative treatments, as well as adjuvant registered immunotherapy and targeted therapy (osimertinib) in case of EGFR mutation. In oncogenic-addictive metastatic NSCLC, primarily in adenocarcinoma, the range of targeted therapies is expanding, with which the expected overall survival increases significantly, measured in years. By 2021, the FDA and EMA have approved targeted agents to inhibit EGFR activating mutations, T790 M resistance mutation, BRAF V600E mutation, ALK, ROS1, NTRK and RET fusion. In 2022, the range of authorized target therapies was expanded. With therapies that inhibit KRASG12C, EGFR exon 20, HER2 and MET. Until now, there was no registered targeted therapy for the KRAS mutations, which affect 30% of adenocarcinomas. Thus, the greatest expectation surrounded the inhibition of the KRAS G12C mutation, which occurs in ∼15% of NSCLC, mainly in smokers and is characterized by a poor prognosis. Sotorasib and adagrasib are approved as second-line agents after at least one prior course of chemotherapy and/or immunotherapy. Adagrasib in first-line combination with pembrolizumab immunotherapy proved more beneficial, especially in patients with high expression of PD-L1. In EGFR exon 20 insertion mutation of lung adenocarcinoma, amivantanab was registered for progression after platinum-based chemotherapy. Lung adenocarcinoma carries an EGFR exon 20, HER2 insertion mutation in 2%, for which the first targeted therapy is trastuzumab deruxtecan, in patients already treated with platinum-based chemotherapy. Two orally administered selective c-MET inhibitors, capmatinib and tepotinib, were also approved after chemotherapy in adenocarcinoma carrying MET exon 14 skipping mutations of about 3%. Incorporating reflex testing with next-generation sequencing (NGS) expands personalized therapies by identifying guideline-recommended molecular alterations.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database.

OBJECTIVE: We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma. METHODS: Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms. RESULTS: We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification. CONCLUSION: The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

Lung adenocarcinoma discovered during the follow-up of lung-dominant connective tissue disease: a case report and literature review.

Interstitial lung disease (ILD) can lead to lung cancer, which brings great challenges to differential diagnosis and comprehensive treatment. However, the clinical features of lung-dominant connective tissue disease (LD-CTD) related ILD combined with lung cancer has not been validated. We report the case of an 80-year-old woman with LD-CTD treated regularly with nintedanib who presented progressive dyspnoea and hypoxemia after recurrent viral infections. Her chest computed tomography (CT) showed aggravated interstitial fibrosis in both lower lungs with moderate right pleural effusion. Clinicians should be alert to lung cancer in patients who are experiencing poor responsiveness to treatment or acute progression of ILD. The available literatures about the differential diagnosis of clinical manifestations, imaging, treatment and prognosis of LD-CTD are reviewed and discussed in this study.

miR-125b reverses cisplatin resistance by regulating autophagy via targeting RORA/BNIP3L axis in lung adenocarcinoma.

The platinum-based chemotherapy is one of the most frequently used treatment protocols for lung adenocarcinoma (LUAD), and chemoresistance, however, usually results in treatment failure and limits its application in the clinic. It has been shown that microRNAs (miRNAs) play a significant role in tumor chemoresistance. In this study, miR-125b was identified as a specific cisplatin (DDP)-resistant gene in LUAD, as indicated by the bioinformatics analysis and the real-time quantitative PCR assay. The decreased serum level of miR-125b in LUAD patients was correlated with the poor treatment response rate and short survival time. MiR-125b decreased the A549/DDP proliferation, and the multiple drug resistance- and autophagy-related protein expression levels, which were all reversed by the inhibition of miR-125b. In addition, xenografts of human tumors in nude mice were suppressed by miR-125b, demonstrating that through autophagy regulation, miR-125b could reverse the DDP resistance in LUAD cells, both in vitro and in vivo. Further mechanistic studies indicated that miR-125b directly repressed the expression levels of RORA and its downstream BNIP3L, which in turn inhibited autophagy and reversed chemoresistance. Based on these findings, miR-125b in combination with DDP might be an effective treatment option to overcome DDP resistance in LUAD.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy.

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma, a leading cause of cancer-related mortality, demands precise prognostic indicators for effective management. The presence of spread through air space (STAS) indicates adverse tumor behavior. However, comparative differences between 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography(PET)/computed tomography(CT) and CT in predicting STAS in lung adenocarcinoma remain inadequately explored. This retrospective study analyzes preoperative CT and 18F-FDG PET/CT features to predict STAS, aiming to identify key predictive factors and enhance clinical decision-making. METHODS: Between February 2022 and April 2023, 100 patients (108 lesions) who underwent surgery for clinical lung adenocarcinoma were enrolled. All these patients underwent 18F-FDG PET/CT, thin-section chest CT scan, and pathological biopsy. Univariate and multivariate logistic regression was used to analyze CT and 18F-FDG PET/CT image characteristics. Receiver operating characteristic curve analysis was performed to identify a cut-off value. RESULTS: Sixty lesions were positive for STAS, and 48 lesions were negative for STAS. The STAS-positive was frequently observed in acinar predominant. However, STAS-negative was frequently observed in minimally invasive adenocarcinoma. Univariable analysis results revealed that CT features (including nodule type, maximum tumor diameter, maximum solid component diameter, consolidation tumor ratio, pleural indentation, lobulation, spiculation) and all 18F-FDG PET/CT characteristics were statistically significant difference in STAS-positive and STAS-negative lesions. And multivariate logistic regression results showed that the maximum tumor diameter and SUVmax were the independent influencing factors of CT and 18F-FDG PET/CT in STAS, respectively. The area under the curve of maximum tumor diameter and SUVmax was 0.68 vs. 0.82. The cut-off value for maximum tumor diameter and SUVmax was 2.35 vs. 5.05 with a sensitivity of 50.0% vs. 68.3% and specificity of 81.2% vs. 87.5%, which showed that SUVmax was superior to the maximum tumor diameter. CONCLUSION: The radiological features of SUVmax is the best model for predicting STAS in lung adenocarcinoma. These radiological features could predict STAS with excellent specificity but inferior sensitivity.