Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma.

BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

Metabolic reprogramming-driven homologous recombination and TCA cycle dysregulation contribute to poor prognoses in lung adenocarcinoma.

Increasing evidence has shown that homologous recombination (HR) and metabolic reprogramming are essential for cellular homeostasis. These two processes are independent as well as closely intertwined. Nevertheless, they have rarely been reported in lung adenocarcinoma (LUAD). We analysed the genomic, immune microenvironment and metabolic microenvironment features under different HR activity states. Using cell cycle, EDU and cell invasion assays, we determined the impacts of si-SHFM1 on the LUAD cell cycle, proliferation and invasion. The levels of isocitrate dehydrogenase (IDH) and α-ketoglutarate dehydrogenase (α-KGDH) were determined by ELISA in the NC and si-SHFM1 groups of A549 cells. Finally, cell samples were used to extract metabolites for HPIC-MS/MS to analyse central carbon metabolism. We found that high HR activity was associated with a poor prognosis in LUAD, and HR was an independent prognostic factor for TCGA-LUAD patients. Moreover, LUAD samples with a high HR activity presented low immune infiltration levels, a high degree of genomic instability, a good response status to immune checkpoint blockade therapy and a high degree of drug sensitivity. The si-SHFM1 group presented a significantly higher proportion of cells in the G0/G1 phase, lower levels of DNA replication, and significantly lower levels of cell migration and both TCA enzymes. Our current results indicated that there is a strong correlation between HR and the TCA cycle in LUAD. The TCA cycle can promote SHFM1-mediated HR in LUAD, raising their activities, which can finally result in a poor prognosis and impair immunotherapeutic efficacy.

Development and validation of a preoperative CT­based radiomics nomogram to differentiate tuberculosis granulomas from lung adenocarcinomas: an external validation study.

BACKGROUND: An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute tomography (CE-CT) to preoperatively differentiate tuberculosis granuloma from lung adenocarcinoma appearing as solitary pulmonary solid nodules (SPSN). METHODS: This retrospective study analyzed 143 patients with lung adenocarcinoma (mean age: 62.4 ± 6.5 years; 54.5% female) and 137 patients with tuberculosis granulomas (mean age: 54.7 ± 8.2 years; 29.2% female) from two centers between March 2015 and June 2020. The training and internal validation cohorts included 161 and 69 patients (7:3 ratio) from center No.1, respectively. The external testing cohort included 50 patients from center No.2. Clinical factors and conventional radiological characteristics were analyzed to build independent predictors. Radiomics features were extracted from each CT-volume of interest (VOI). Feature selection was performed using univariate and multivariate logistic regression analysis, as well as the least absolute shrinkage and selection operator (LASSO) method. A clinical model was constructed with clinical factors and radiological findings. Individualized radiomics nomograms incorporating clinical data and radiomics signature were established to validate the clinical usefulness. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve analysis with the area under the receiver operating characteristic curve (AUC). RESULTS: One clinical factor (CA125), one radiological characteristic (enhanced-CT value) and nine radiomics features were found to be independent predictors, which were used to establish the radiomics nomogram. The nomogram demonstrated better diagnostic efficacy than any single model, with respective AUC, accuracy, sensitivity, and specificity of 0.903, 0.857, 0.901, and 0.807 in the training cohort; 0.933, 0.884, 0.893, and 0.892 in the internal validation cohort; 0.914, 0.800, 0.937, and 0.735 in the external test cohort. The calibration curve showed a good agreement between prediction probability and actual clinical findings. CONCLUSION: The nomogram incorporating clinical factors, radiological characteristics and radiomics signature provides additional value in distinguishing tuberculosis granuloma from lung adenocarcinoma in patients with a SPSN, potentially serving as a robust diagnostic strategy in clinical practice.

CBX4 plays a bidirectional role in transcriptional regulation and lung adenocarcinoma progression.

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide. Understanding the dysregulated epigenetics governing LUAD progression is pivotal for identifying therapeutic targets. CBX4, a chromobox protein, is reported to be upregulated in LUAD. This study highlights the dual impact of CBX4 on LUAD proliferation and metastasis through a series of rigorous in vitro and in vivo experiments. Further investigation into the underlying mechanism through high-throughput ChIP-seq and RNA-seq reveals that CBX4 functions in promoting LUAD proliferation via upregulating PHGDH expression and subsequent serine biosynthesis, while concurrently suppressing LUAD metastasis by inhibiting ZEB2 transcription. CBX4 facilitates PHGDH transcription through the interaction with GCN5, inducing heightened histone acetylation on the PHGDH promoter. Simultaneously, the inhibition of ZEB2 transcription involves CBX4-mediated recruitment of canonical PRC1 (cPRC1), establishing H2K119ub on the ZEB2 promoter. These findings underscore CBX4's pivotal role as a regulator of LUAD progression, emphasizing its diverse transcriptional regulatory functions contingent upon interactions with specific epigenetic partners. Understanding the nuanced interplay between CBX4 and epigenetic factors sheds light on potential therapeutic avenues in LUAD.

PM2.5 regulates the progression of lung adenocarcinoma through the axis of HCG18, miR-195 and ATG14.

Relevant studies have indicated the association of HCG18 with tumour occurrence and progression. In this study, we observed that PM2.5 can enhance the growth of lung adenocarcinoma cells by modulating the expression of HCG18. Further investigations, including overexpression and knockout experiments, elucidated that HCG18 suppresses miR-195, which in turn upregulates the expression of ATG14, resulting in the upregulation of autophagy. Consequently, exposure to PM2.5 leads to elevated HCG18 expression in lung tissues, which in turn increases Atg14 expression and activates autophagy pathways through inhibition of miR-195, thereby contributing to oncogenesis.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.

BACKGROUND: Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. OBJECTIVE: This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. DESIGN: A retrospective case control, diagnostic accuracy study. METHODS: This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. RESULTS: The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. CONCLUSION: This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

A new tool to predict aggressive lung cancer types before surgeryWe developed a tool to help doctors determine whether lung cancer is one of the more dangerous types, called micropapillary (MPP) or solid (SOL) patterns, before surgery. These patterns can be more harmful and spread quickly, so knowing they are there can help doctors plan the best treatment. We looked at the cases of 273 lung cancer patients who had surgery and found that 61 of them had these aggressive cancer types. To predict these patterns, we used a computer process known as logistic regression, analyzing CT scan details, health information, and blood tests for cancer markers. Based on CT scans, our tool was very good at predicting whether these patterns were present in two patient groups. However, predictions using only basic health information like the size of the tumor and whether the patient smoked needed to be more accurate. We found a way to make our predictions even better. Combining all information into one chart, known as a nomogram, significantly improved our ability to predict these dangerous cancer patterns. This combined chart could be a big help for doctors. It gives them a clearer picture of the cancer's aggressiveness before surgery, which can guide them to choose the best treatment options. This approach aims to offer a better understanding of the tumor, leading to more tailored and effective treatments for patients facing lung cancer.

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma.

Recent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using "DESeq2", "survival," "ConsensusClusterPlus," "GSVA," "estimate," "glmnet," "clusterProfiler," "rms," and "pRRophetic" R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

Clinical relevance of somatic mutations in Chinese lung adenocarcinoma and their prognostic implications for survival.

BACKGROUND: To comprehensively elucidate the genomic and mutational features of lung cancer cases, and lung adenocarcinoma (LUAD), it is imperative to conduct ongoing investigations into the genomic landscape. In this study, we aim to analyze the somatic mutation profile and assessed the significance of these informative genes utilizing a retrospective LUAD cohort. METHODS: A total of 247 Chinese samples were analyzed to exhibit the tumor somatic genomic alterations in patients with LUAD. The Cox regression analysis was employed to identify prognosis-related genes and establish a predictive model for stratifying patients with LUAD. RESULTS: In the Dianjiang People's Hospital (DPH) cohort, the top five frequent mutated genes were (Epidermal growth factor receptor) EGFR (68%), TP53 (30%), RBM10 (13%), LRP1B (9%), and KRAS (9%). Of which, EGFR is a mostly altered driver gene, and most mutation sites are located in tyrosine kinase regions. Oncogene pathway alteration and mutation signature analysis demonstrated the RTK-RAS pathway alteration, and smoking was the main carcinogenic factor of the DPH cohort. Furthermore, we identified 34 driver genes in the DPH cohort, including EGFR (68%), TP53 (30.4%), RBM10 (12.6%), KRAS (8.5%), LRP1B (8.5%), and so on, and 45 Clinical Characteristic-Related Genes (CCRGs) were found to closely related to the clinical high-risk factors. We developed a Multiple Parameter Gene Mutation (MPGM) risk model by integrating critical genes and oncogenic pathway alterations in LUAD patients from the DPH cohort. Based on publicly available LUAD datasets, we identified five genes, including BRCA2, Anaplastic lymphoma kinase (ALK), BRAF, EGFR, and Platelet-Derived Growth Factor Receptor Alpha (PDGFRA), according to the multivariable Cox regression analysis. The MPGM-low group showed significantly better overall survival (OS) compared to the MPGM-high group (p < 0.0001, area under the curve (AUC) = 0.754). The robust performance was validated in 55 LUAD patients from the DPH cohort and another LUAD dataset. Immune characteristics analysis revealed a higher proportion of primarily DCs and mononuclear cells in the MPGM-low risk group, while the MPGM-high risk group showed lower immune cells and higher tumor cell infiltration. CONCLUSION: This study provides a comprehensive genomic landscape of Chinese LUAD patients and develops an MPGM risk model for LUAD prognosis stratification. Further follow-up will be performed for the patients in the DPH cohort consistently to explore the resistance and prognosis genetic features.

The expression characteristic and prognostic role of Siglec-15 in lung adenocarcinoma.

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has been identified as an immune suppressor and a promising candidate for immunotherapy of cancer management. However, the association between Siglec-15 expression and clinicopathological features of lung adenocarcinoma (LUAD), especially the prognostic role, is not fully elucidated. In this present study, a serial of bioinformatics analyses in both tissue and cell levels were conducted to provide an overview of Siglec-15 expression. Real-time quantitative PCR (qPCR) test, western blotting assay, and immunohistochemistry (IHC) analyses were conducted to evaluate the expression of Siglec-15 in LUAD. Survival analysis and Kaplan-Meier curve were employed to describe the prognostic parameters of LUAD. The results of bioinformatics analyses demonstrated the up-regulation of Siglec-15 expression in LUAD. The data of qPCR, western blotting, and IHC analyses further proved that the expression of Siglec-15 in LUAD tissues was significantly increased than that in noncancerous tissues. Moreover, the expression level of Siglec-15 protein in LUAD was substantially associated with TNM stage. LUAD cases with up-regulated Siglec-15 expression, positive N status, and advance TNM stage suffered a critical unfavorable prognosis. In conclusion, Siglec-15 could be identified as a novel prognostic biomarker in LUAD and targeting Siglec-15 may provide a promising strategy for LUAD immunotherapy.

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures.

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model's utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma.

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

GTSE1: A potential prognostic and diagnostic biomarker in various tumors including lung adenocarcinoma.

OBJECTIVE: This research was aimed to comprehensively investigate the expression levels, diagnostic and prognostic implications, and the relationship with immune infiltration of G2 and S phase-expressed-1 (GTSE1) across 33 tumor types, including lung adenocarcinoma (LUAD), through gene expression profiling. METHODS: GTSE1 mRNA expression data together with clinical information were acquired from Xena database of The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) database for this study. The Wilcoxon rank-sum test was used to detect differences in GTSE1 expression between groups. The ability of GTSE1 to accurately predict cancer status was evaluated by calculating the area under the curve (AUC) value for the receiver operating characteristic curve. Additionally, we investigated the predictive value of GTSE1 in individuals diagnosed with neoplasms using univariate Cox regression analysis as well as Kaplan-Meier curves. Furthermore, the correlation between GTSE1 expression and levels of immune infiltration was assessed by utilizing the Tumor Immune Estimate Resource (TIMER) database to calculate the Spearman rank correlation coefficient. Finally, the pan-cancer analysis findings were validated by examining the association between GTSE1 expression and prognosis among patients with LUAD. RESULTS: GTSE1 exhibited significantly increased expression levels in a wide range of tumor tissues in contrast with normal tissues (p < 0.05). The expression of GTSE1 in various tumors was associated with clinical features, overall survival, and disease-specific survival (p < 0.05). In immune infiltration analyses, a strong correlation of the level of immune infiltration with the expression of GTSE1 was observed. Furthermore, GTSE1 demonstrated good discriminative and diagnostic value for most tumors. Additional experiments confirmed the relationship between elevated GTSE1 expression and unfavorable prognosis in individuals diagnosed with LUAD. These findings indicated the crucial role of GTSE1 expression level in influencing the development and immune infiltration of different types of tumors. CONCLUSIONS: GTSE1 might be a potential biomarker for the prognosis of pan-cancer. Meanwhile, it represented a promising target for immunotherapy.

Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways.

Somatic evolution selects cancer cell phenotypes that maximize survival and proliferation in dynamic environments. Although cancer cells are molecularly heterogeneous, we hypothesized convergent adaptive strategies to common host selection forces can be inferred from patterns of epigenetic and genetic evolutionary selection in similar tumors. We systematically investigated gene mutations and expression changes in lung adenocarcinomas with no common driver genes (n = 313). Although 13,461 genes were mutated in at least one sample, only 376 non-synonymous mutations evidenced positive evolutionary selection with conservation of 224 genes, while 1736 and 2430 genes exhibited ≥ two-fold increased and ≥ 50% decreased expression, respectively. Mutations under positive selection are more frequent in genes with significantly altered expression suggesting they often "hardwire" pre-existing epigenetically driven adaptations. Conserved genes averaged 16-fold higher expression in normal lung tissue compared to those with selected mutations demonstrating pathways necessary for both normal cell function and optimal cancer cell fitness. The convergent LUAD phenotype exhibits loss of differentiated functions and cell-cell interactions governing tissue organization. Conservation with increased expression is found in genes associated with cell cycle, DNA repair, p53 pathway, epigenetic modifiers, and glucose metabolism. No canonical driver gene pathways exhibit strong positive selection, but extensive down-regulation of membrane ion channels suggests decreased transmembrane potential may generate persistent proliferative signals. NCD LUADs perform niche construction generating a stiff, immunosuppressive microenvironment through selection of specific collagens and proteases. NCD LUADs evolve to a convergent phenotype through a network of interconnected genetic, epigenetic, and ecological pathways.

Prognostic and immunotherapeutic implications of bilirubin metabolism-associated genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancer and accompanies high mortality rates. While the role of bilirubin metabolism in cancer is recognized, its specific impact on LUAD and patient response to immunotherapy needs to be elucidated. This study aimed to develop a prognostic signature of bilirubin metabolism-associated genes (BMAGs) to predict outcomes and efficacy of immunotherapy in LUAD. We analysed gene expression data from The Cancer Genome Atlas (TCGA) to identify survival-related BMAGs and construct a prognostic model in LUAD. The prognostic efficacy of our model was corroborated by employing TCGA-LUAD and five Gene Expression Omnibus datasets, effectively stratifying patients into risk-defined cohorts with marked disparities in survival. The BMAG signature was indeed an independent prognostic determinant, outperforming established clinical parameters. The low-risk group exhibited a more favourable response to immunotherapy, highlighted by increased immune checkpoint expression and immune cell infiltration. Further, somatic mutation profiling differentiated the molecular landscapes of the risk categories. Our screening further identified potential drug candidates preferentially targeting the high-risk group. Our analysis of critical BMAGs showed the tumour-suppressive role of FBP1, highlighting its suppression in LUAD and its inhibitory effects on tumour proliferation, migration and invasion, in addition to its involvement in cell cycle and apoptosis regulation. These findings introduce a potent BMAG-based prognostic indicator and offer valuable insights for prognostication and tailored immunotherapy in LUAD.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.

OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS: A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS: By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS: Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma.

AIMS: To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance. METHODS: We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated. RESULTS: 147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy. CONCLUSION: The driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma.

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing "danger signals" to stimulate the body's immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.

Development and validation of a model for predicting upstage in minimally invasive lung adenocarcinoma in Chinese people.

BACKGROUND: Sublobar resection for ground-glass opacity became a recommend surgery choice supported by the JCOG0804/JCOG0802/JCOG1211 results. Sublobar resection includes segmentectomy and wedge resection, wedge resection is suitable for non-invasive lesions, but in clinical practice, when pathologists are uncertain about the intraoperative frozen diagnosis of invasive lesions, difficulty in choosing the appropriate operation occurs. The purpose of this study was to analyze how to select invasive lesions with clinic-pathological characters. METHODS: A retrospective study was conducted on 134 cases of pulmonary nodules diagnosed with minimally invasive adenocarcinoma by intraoperative freezing examination. The patients were divided into two groups according to intraoperative frozen results: the minimally invasive adenocarcinoma group and the at least minimally invasive adenocarcinoma group. A variety of clinical features were collected. Chi-square tests and multiple regression logistic analysis were used to screen out independent risk factors related to pathological upstage, and then ROC curves were established. In addition, an independent validation set included 1164 cases was collected. RESULTS: Independent risk factors related to pathological upstage were CT value, maximum tumor diameter, and frozen result of AL-MIA. The AUC of diagnostic mode was 71.1% [95%CI: 60.8-81.3%]. The independent validation included 1164 patients, 417 (35.8%) patients had paraffin-based pathology of invasive adenocarcinoma. The AUC of diagnostic mode was 75.7% [95%CI: 72.9-78.4%]. CONCLUSIONS: The intraoperative frozen diagnosis was AL-MIA, maximum tumor diameter larger than 15 mm and CT value is more than - 450Hu, highly suggesting that the lung GGO was invasive adenocarcinoma which represent a higher risk to recurrence. For these patients, sublobectomy would be insufficient, lobectomy or complementary treatment is encouraged.

Down-regulation of KLRB1 is associated with increased cell growth, metastasis, poor prognosis, as well as a dysfunctional immune microenvironment in LUAD.

Killer cell lectin-like receptor B1 (KLRB1) is implicated in cancer progression and immunity. In this study, we aimed to evaluate the expression levels of KLRB1 in lung adenocarcinoma (LUAD) and analyze the relationship between KLRB1 expression levels, LUAD progression, and the tumor immune microenvironment. KLRB1 levels in LUAD were analyzed using data from the TCGA and XENA databases. Additionally, the diagnostic values of KLRB1 were analyzed in patients with LUAD. Survival and meta-analyses were employed to investigate the relationship between KLRB1 levels and other prognostic factors in patients with LUAD. Bioinformatics and cellular experiments were used to understand the functions and mechanisms of KLRB1. In addition, correlation analysis was used to investigate the relationship between KLRB1 levels and the immune microenvironment in LUAD. Reduced KLRB1 expression in LUAD was found to positively correlate with tumor size, distant metastasis, pathological stage, age, overall survival, diagnostic value, and disease-specific survival in patients with LUAD (P < 0.05). Conversely, increased KLRB1 expression was found to positively correlate with the overall survival and disease-specific survival in patients with LUAD (P < 0.05). We also found that the overexpression of KLRB1 can inhibit the proliferation, migration, and invasion of LUAD cells and promote apoptosis. KLRB1 was involved in immune cell differentiation, NF-kB, PD-L1, and PD-1 checkpoint pathways and others. Additionally, KLRB1 expression was linked to tumor purity, stromal, immune, and estimate scores, the levels of immune cells including B cells, CD8+ T cells, and CD4+ T cells, and immune cell markers in LUAD. Reduced KLRB1 expression has a significant positive correlation with diagnosis, poor prognosis, and immunity to cancer in patients with LUAD. KLRB1 inhibited cell proliferation and migration in patients with LUAD. These results suggest that KLRB1 may serve as a potential therapeutic target in patients with LUAD.