Prognosis Of Non-small Cell Lung Cancer Patients Research Articles

GTPase GPN3 facilitates cell proliferation and migration in non-small cell lung cancer by impeding clathrin-mediated endocytosis of EGFR

Small GTPases play a critical role as regulatory molecules in signaling transduction and various cellular processes, contributing to the development of human diseases, including cancers. GPN3, an evolutionarily conserved member of the GPN-loop GTPase subfamily classified in 2007 according to its structure, has limited knowledge regarding its cellular functions and molecular mechanisms. In this study, we demonstrate that GPN3 interacts with clathrin light chain A (CLTA), a vesicle coat protein, as well as clathrin-mediated endocytosis associated modulators AP2B1 and AP2S1. Upregulation of GPN3 leads to the inhibition of clathrin-coated pit invagination. Furthermore, we discovered that GPN3 interacts with the epidermal growth factor receptor (EGFR) and regulates the co-localization of EGFR and CLTA, as well as the localization of EGFR in early endosomes upon EGF stimulation. As a result, this leads to a decrease in endocytic levels of EGFR and an increase in the accumulation of EGFR on the cell membrane surface, thereby prolonging activation of EGFR signaling. The functional effects exerted by GPN3 are dependent on cellular levels of GTP abundance. Furthermore, our findings indicate that aberrant overexpression of GPN3 is observed in non-small cell lung cancer (NSCLC) tissues compared to adjacent normal tissues, and high expression levels of GPN3 are associated with poor prognosis for NSCLC patients. Collectively, these findings reveal that GPN3 acts as an oncogene promoting cell proliferation and migration in NSCLC through regulation of clathrin-dependent EGFR endocytosis. These results suggest that targeting GPN3 could serve as a novel prognostic biomarker and therapeutic strategy for NSCLC treatment.

Genetic variants of FER and SULF1 in the fibroblast-related genes are associated with non-small-cell lung cancer survival.

Fibroblasts are important components in the tumor microenvironment and can affect tumor progression and metastasis. However, the roles of genetic variants of the fibroblast-related genes (FRGs) in the prognosis of non-small-cell lung cancer (NSCLC) patients have not been reported. Therefore, we investigated the associations between 26,544 single nucleotide polymorphisms (SNPs) in 291 FRGs and survival of NSCLC patients from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. In Cox regression multivariable analysis, we found that 661 SNPs were associated with NSCLC overall survival (OS). Then we validated these SNPs in another independent replication dataset of 984 patients from the Harvard Lung Cancer Susceptibility (HLCS) Study. Finally, we identified two independent SNPs (i.e., FER rs7716388 A>G and SULF1 rs11785839 G>C) that remained significantly associated with NSCLC survival with hazards ratios (HRs) of 0.87 (95% confidence interval [CI] = 0.77-0.98, p = 0.018) and 0.88 (95% CI = 0.79-0.99, p = 0.033), respectively. Combined analysis for these two SNPs showed that the number of protective alleles was associated with better OS and disease-specific survival. Expression quantitative trait loci analysis indicated that the FER rs7716388 G allele was associated with the up-regulation of FER mRNA expression levels in lung tissue. Our results indicated that these two functional SNPs in the FRGs may be prognostic biomarkers for the prognosis of NSCLC patients, and the possible mechanism may be through modulating the expression of their corresponding genes.

SEC14L2 regulates the transport of cholesterol in non-small cell lung cancer through SCARB1

BackgroundInhibiting cholesterol metabolism has shown great potential in non-small cell lung cancer (NSCLC). However, the regulatory mechanism of the lipid metabolism key factor Sect. 14-like lipid binding 2 (SEC14L2) in NSCLC remains unclear. This study investigates the effects of differentially expressed genes related to cholesterol metabolism on the development of NSCLC.MethodsCox regression and survival analysis were performed to screen cholesterol metabolism-related genes and predict survival prognosis in NSCLC patients. The proliferation and migration of NSCLC cells were assessed by CCK-8, EdU, colony formation and wound-healing assay. Cholesterol depletion and rescue trials were used to evaluate the effect of SEC14L2 on cholesterol transport in NSCLC cells. IF and Co-IP were used to analyze the targeting relationship between SEC14L2 and scavenger receptor class B member 1 (SCARB1).ResultsSEC14L2 was a key gene related to prognosis in NSCLC patients and was highly expressed in A549 and Calu-1 cells. Subsequent studies demonstrated that knockdown of SEC14L2 significantly reduced the proliferation and migration of NSCLC cells, resulting in inhibited tumor growth. Furthermore, both in vitro and in vivo experiments indicated that SEC14L2 regulated cholesterol uptake. Silencing SEC14L2 partially counteracted the promotion of cholesterol content by MβCD-chol in A549 and Calu-1 cells. We then verified that there was a protein interaction between SEC14L2 and SCARB1.ConclusionSEC14L2 promoted cholesterol uptake in NSCLC cells by up-regulating SCARB1 expression, thereby promoting NSCLC development.

TFAP2A drives non-small cell lung cancer (NSCLC) progression and resistance to targeted therapy by facilitating the ESR2-mediated MAPK pathway

Cancer is among the leading causes of death related diseases worldwide, and lung cancer has the highest mortality rate in the world. Transcription factors (TFs) constitute a class of structurally and functionally intricate proteins. Aberrant expression or functional deficiencies of transcription factors may give rise to abnormal gene expression, contributing to various diseases, including tumours. In this study, we propose to elucidate the potential role and mechanism of TFAP2A in NSCLC. We found that TFAP2A levels were significantly greater in tumour tissues than para-tumour tissues, and high expression of TFAP2A was associated with poor prognosis in NSCLC patients. Additionally, TFAP2A overexpression promoted NSCLC progression both in vivo and in vitro. Mechanistically, ESR2 is a potential target regulated by TFAP2A and that TFAP2A can bind to the promoter region of ESR2. Furthermore, the overexpression of both TFAP2A and ESR2 in NSCLC cells was associated with the overactivation of MAPK signalling, and the combination of PHTPP and osimertinib had a synergistic effect on suppressing tumour growth.

Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer and poses a serious threat to human health. Due to the advances in lung cancer screening, more and more clinical T1 NSCLC defined as a tumor with a maximum diameter of 3cm surrounded by lung tissue or visceral pleura have been detected and have achieved favorable treatment outcomes, greatly improving the prognosis of NSCLC patients. However, the preoperative lymph node staging and intraoperative lymph node dissection patterns of operable clinical T1 NSCLC are still subject to much disagreement, as well as the heterogeneity between primary tumors and metastatic lymph nodes poses a challenge in designing effective treatment strategies. This article comprehensively describes the clinical risk factors of clinical T1 NSCLC lymph node metastasis, and its invasive and non-invasive prediction, focusing on the genetic heterogeneity between the primary tumor and the metastatic lymph nodes, which is significant for a thoroughly understanding of the biological behavior of early-stage NSCLC.

Development and application of a predictive model for survival and drug therapy based on COVID-19-related lncRNAs in non-small cell lung cancer.

Numerous studies have substantiated the pivotal role of long non-coding RNAs (lncRNAs) in the progression of non-small cell lung cancer (NSCLC) and the prognosis of afflicted patients. Notably, individuals with NSCLC may exhibit heightened vulnerability to the novel coronavirus disease (COVID-19), resulting in a more unfavorable prognosis subsequent to infection. Nevertheless, the impact of COVID-19-related lncRNAs on NSCLC remains unexplored. The aim of our study was to develop an innovative model that leverages COVID-19-related lncRNAs to optimize the prognosis of NSCLC patients. Pertinent genes and patient data were procured from reputable databases, including TCGA, Finngen, and RGD. Through co-expression analysis, we identified lncRNAs associated with COVID-19. Subsequently, we employed univariate, LASSO, and multivariate COX regression techniques to construct a risk model based on these COVID-19-related lncRNAs. The validity of the risk model was assessed using KM analysis, PCA, and ROC. Furthermore, functional enrichment analysis was conducted to elucidate the functional pathways linked to the identified lncRNAs. Lastly, we performed TME analysis and predicted the drug sensitivity of the model. Based on risk scores, patients were categorized into high- and low-risk subgroups, revealing distinct clinicopathological factors, immune pathways, and chemotherapy sensitivity between the subgroups. Four COVID-19-related lncRNAs (AL161431.1, AC079949.1, AC123595.1, and AC108136.1) were identified as potential candidates for constructing prognostic prediction models for NSCLC. We also observed a positive correlation between risk score and MDSC, exclusion, and CAF. Additionally, two immune pathways associated with high-risk and low-risk subgroups were identified. Our findings further support the association between COVID-19 infection and neuroactive ligand-receptor interaction, as well as steroid metabolism in NSCLC. Moreover, we identified several highly sensitive chemotherapy drugs for NSCLC treatment. The developed model holds significant value in predicting the prognosis of NSCLC patients and guiding treatment decisions.

Circulating Tumor cells and multiple indicators combined to identify the risk of poorer prognosis in patients with resected non-small cell lung cancer

BackgroundSurgical resection is an important treatment option for patients with non-small cell lung cancer (NSCLC). However, recurrence and survival rates remain a cause of concern. To further improve prognosis, more studies have focused on liquid biopsy, which has significant value as a prognostic factor for defining the risk stratification of postoperative NSCLC patients. This study aimed to identify circulating tumor cells (CTCs) as biomarkers that indicate a poor prognosis, combined with multiple indicators to determine prognostic risks in advance and develop individualized treatment strategies.MethodsBetween November 2015 and August 2018, 65 radical resected patients with NSCLC were analyzed. Preoperative CTCs were collected, and follow-up lasted until August 2023. Overall survival (OS) and disease-free survival (DFS) were the primary outcomes.ResultsWith an 11 CTC unit threshold, the high preoperative CTC level group had worse OS and DFS than the low-level group, suggesting that preoperative CTC levels have prognostic value. Time-dependent receiver operating characteristic (ROC) curves also showed satisfactory predictive efficiency of CTCs. Univariate analysis revealed that preoperative CTC levels were significantly associated with increasing risks for OS and DFS. Moreover, we combined CTCs and multiple indicators to provide a reference for a group at high risk of adverse outcomes.ConclusionsCTCs serve as feasible biomarkers for predicting postoperative prognosis in NSCLC patients. The combination of hematological, radiological, and pathological features could be valuable tools to guide postoperative management and treatment decisions in these patients. A multimodal prognostic approach is important for the clinical evaluation of lung cancer.

Development of an AI model for predicting hypoxia status and prognosis in non-small cell lung cancer using multi-modal data.

Prognosis prediction is crucial for non-small cell lung cancer (NSCLC) treatment planning. While tumor hypoxia significantly impacts patient outcomes, identifying hypoxic genomic markers remains challenging. This study sought to identify hypoxic computed tomography (CT) radiomic features and create an artificial intelligence (AI) model for NSCLC through the integration of multi-modal data. In total, 452 NSCLC patients were enrolled in this study, including patients from The Second Affiliated Hospital of Soochow University (SC, n=112), The Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), the radiogenomics dataset (n=130), and the Gene Expression Omnibus (GEO) datasets (GSE19188: n=82, and GSE87340: n=54). Hypoxia status was classified using optimized cut-off values of hypoxia enrichment scores, which were calculated through single-sample gene set enrichment analysis (ssGSEA) of hypoxic genes. Radiomic features were extracted using three-dimensional (3D)-Slicer software. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify hypoxic CT radiomic features. A model named ssuBERT (semantic structured unit embedded in Bidirectional Encoder Representations from Transformers) was developed to analyze electronic health records (EHRs). An AI model for overall survival prediction was constructed by integrating CT radiomic features, ssuBERT features, and clinical data, and evaluated using five-fold cross-validation. Higher hypoxia levels were correlated with worse survival outcomes. Twenty-eight radiomic features showed significant discriminatory power in detecting hypoxia status with an area under the curve (AUC) of 0.8295. The ssuBERT model achieved a weighted accuracy of 0.945 in recognizing semantic structured units in EHRs. The EHR model exhibited superior predictive performance among the single-modal models with an AUC of 0.7662. However, the multi-modal AI model had the highest average AUC of 0.8449 and an F1 score of 0.7557. The AI model demonstrated potential in predicting NSCLC patient prognosis through multi-modal data integration, warranting further validation.

Construction of a cuproptosis-tricarboxylic acid cycle-associated lncRNA model to predict the prognosis of non-small cell lung cancer.

In cuproptosis, excess copper ions induce cell death via fatty acylation in the tricarboxylic acid (TCA) cycle. However, the effects of cuproptosis-TCA-related long non-coding RNAs (lncRNAs) on the clinical prognosis of non-small cell lung cancer (NSCLC) and the associated tumor microenvironment remain unclear. The purpose of this study is to use cuproptosis-TCA related lncRNAs to predict the prognosis of NSCLC. Molecular signature databases and cuproptosis-related publications were made use of identifying cuproptosis-TCA-related genes. They were identified based on Pearson correlation analysis. The prognostic features associated with these lncRNAs were evaluated using the absolute contraction and selection operator and a receiver operating characteristic curve analysis. Additionally, downstream functional enrichment and immunoinfiltration were analyzed to examine the immunotherapeutic responses of patients with NSCLC. Eleven cuproptosis-TCA-associated lncRNAs were identified. A high-risk group was compared with a low-risk group based on risk scores, and the high-risk group had a significantly lower overall survival (OS). We established a prognostic risk profile, and based on these characteristics and clinical staging, a nomogram was constructed. An analysis of functional enrichment revealed the involvement of pathways associated with cellular and humoral immunity and fatty acylation. Risk scores differed significantly based on immune cells and pathways (antigen-presenting cell co-stimulation). Moreover, TP53, TTN, and MUC16 mutation status were strongly associated with risk scores, with patients identified as having a higher risk of NSCLC being more responsive to immunotherapy. Eleven cuproptosis-TCA-associated lncRNAs can be used to predict the prognosis of NSCLC patients, thereby providing a new theoretical basis for immunotherapy.

Concomitant ALK Fusion and TP53/EGFR Mutation Lead to Adverse Prognostic Outcome.

Lung cancer treatment has evolved at the molecular level. Detecting the presence of driver genes in lung cancer fundamentally alters the choice of therapeutic regimens and the outcome of this disease. ALK fusion mutation is one of the most important mutations in nonsmall cell lung cancer (NSCLC). Also, it often has other coexisting mutation types. TP53 is the most common coexisting mutation type, whereas the EGFR/ALK coexisting mutation type is extremely rare. There is still no definite conclusion about the impact of the multimutation and best treatment options for NSCLC patients with advanced multimutation. In this study, we report three cases of NSCLC with ALK fusion mutations as well as ALK combined with TP53 mutations and ALK combined with EGFR mutations. Combining cases from our oncology center and previous literature, we found that NSCLC patients with coexisting ALK fusion mutations and other mutations have poorer response to targeted therapy and poorer prognosis, and we also compared the efficacy rates of various types of coexisting mutations for different treatment regimens. Therefore, this review can help to evaluate the prognosis of NSCLC patients with coexisting mutations and the efficacy of targeted therapies and to find more favorable treatment options for patients with this type of coexisting mutations.

Treatment patterns and clinical outcomes of resectable central non-small cell lung cancer patients undergoing sleeve lobectomy: a large-scale, single-center, real-world study.

In the treatment of central-type non-small cell lung cancer (NSCLC), sleeve lobectomy (SL) has emerged as the surgical treatment of choice over pneumonectomy (PN). This retrospective study evaluates the clinical profiles and prognostic elements impacting survival and recurrence rates in patients who underwent SL. We retrospectively analyzed 288 patients who underwent SL from January 2010 to December 2023. Survival analysis was performed using the Kaplan-Meier method, and survival curves were subsequently drawn. Factors predicting SL outcomes were investigated through univariate and multivariable Cox regression analyses. Univariate and multivariable analyses consistently demonstrated significant variations in overall survival (OS) and disease-free survival (DFS) among subgroups receiving neoadjuvant therapy (NT), which also stood out as independent prognostic factors. Patients undergoing NT showed enhanced OS [hazard ratio (HR) =0.4652, 95% confidence interval (CI): 0.3042-0.7116, P=0.004] and DFS (HR =0.5182, 95% CI: 0.3243-0.8279, P=0.01). Earlier pT stages were associated with better prognosis (P<0.05). Significant differences in both OS and DFS were noted across pN stages, with earlier stages indicating improved prognosis; this was a significant independent factor for DFS (P<0.001). Similar significant trends were observed across pathological Tumor-Node-Metastasis (pTNM) stages, with earlier stages linked to better outcomes. Additionally, body mass index (BMI) was identified as an independent prognostic factor for both OS and DFS. Clinical T stage independently influenced DFS. No significant prognostic disparities were observed in other clinical characteristics (P>0.05). NT significantly improves the prognosis for NSCLC patients undergoing SL. Pathological staging is proven to be more indicative of prognosis than clinical staging. Understanding the staging of lymph nodes (LNs) is crucial for predicting the long-term recurrence risk in patients with NSCLC who undergo SL treatment. Mediastinal and hilar LN dissection is especially important in minimizing this risk and improving prognosis.

Prognostic Value of STMN1 Expression in Non-small Cell Lung Cancer: A Meta-analysis

Lung cancer is one of the malignant tumors with the highest morbidity and mortality rates worldwide, seriously threatening human health. Non-small cell lung cancer (NSCLC) accounts for more than 85% of all lung cancer cases. STMN1 is a microtubule depolymerizing protein widely present in the cytoplasm and its expression level is associated with the prognosis of NSCLC patients. Through meta-analysis, this study aimed to investigate the predictive value of the expression level of STMN1 for the prognosis of lung cancer and screen for tumor markers with high sensitivity and specificity to optimize the whole-process management of lung cancer patients. The PubMed, The Cochrane Library, Embase, WanFang and CNKI databases were searched from the inception to Sep 6, 2024 for relevant literature. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS) score. The hazard ratio (HR) with 95%CI was combined to assess the relationship between STMN1 expression and prognostic factors. The prognostic indicators included the overall survival (OS) and disease-free survival (DFS). All statistical analysis was conducted by the STATA 17.0 software. A total of 5 high-quality studies (NOS score≥6 points) involving 754 patients were enrolled. The pooled results demonstrated that overexpression of STMN1 was significantly related to worse OS (HR=2.28, 95%CI: 1.79-2.91, P<0.001) and DFS (HR=2.14, 95%CI: 1.45-3.17, P<0.001). Overexpression of STMN1 was a risk factor for poor prognosis of NSCLC patients. Overexpression of STMN1 is a poor prognostic factor in NSCLC patients. STMN1 may serve as a prognostic biomarker for NSCLC patients. However, more researches are still needed to verify the above findings.

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach

BackgroundLung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients.MethodsLaboratory indices of the NSCLC patients at their initial visit were collected for quality control and exploratory analysis. By comparing the levels of the above indices between the survival and death groups, the statistically significant indices were selected for subsequent machine learning modeling. Ten machine learning algorithms were then employed to develop the predictive models with survival and recurrence as outcomes, respectively. Moreover, regression models were constructed using the random survival forest algorithm by incorporating the survival time dimension. Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application.Results682 patients were enrolled according to the inclusion and exclusion criteria. The preliminary comparison results revealed that except for fast blood glucose, CD3+T cell proportion, NK cell proportion, and CA72-4, there were significant statistical differences in other tumor markers, inflammation, metabolism, and immune-related indices between the survival and death groups (p < 0.01). Subsequently, indices with statistical differences were incorporated into machine learning modeling and evaluation. The results showed that among the ten prognostic models constructed using survival status as the outcome, the neural network model obtained the best predictive performance, with accuracy, sensitivity, specificity, AUC, and precision values of 0.993, 0.987, 1.000, 0.994, and 1.000, respectively. The corresponding SHAP16 algorithm revealed that the top five variables in terms of importance were interleukin6 (IL-6), soluble interleukin2 receptor (sIL-2R), cholesterol, CEA, and Cy211, respectively. The random survival forest model also confirmed the critical role of CEA, sIL-2R, and IL-6 in predicting the prognosis of NSCLC patients. A decision tree model with seven cut-off points based on the above three indices was eventually built for clinical application.ConclusionThe neural network model exhibited ideal predictive performance in the survival status of NSCLC patients, and the decision tree model constructed based on selected important variables was conducive to rapid bedside prognosis assessment and decision-making.

Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality worldwide. Immune checkpoint inhibitors (ICIs) have emerged as a crucial treatment option for patients with advanced NSCLC. However, only a subset of patients experience clinical benefit from ICIs. Therefore, identifying biomarkers that can predict response to ICIs is imperative for optimising patient selection. Hematoxylin and eosin (H&E) images of NSCLC patients were obtained from the local cohort (n = 106) and The Cancer Genome Atlas (TCGA) (n = 899). We developed an ICI-related pathological prognostic signature (ir-PPS) based on H&E stained histopathology images to predict prognosis in NSCLC patients treated with ICIs using deep learning. To accomplish this, we employed a modified ResNet model (ResNet18-PG), a widely-used deep learning architecture well-known for its effectiveness in handling complex image recognition tasks. Our modifications include a progressive growing strategy to improve the stability of model training and the use of the AdamW optimiser, which enhances the optimisation process by adjusting the learning rate based on training dynamics. The deep learning model, ResNet18-PG, achieved an area under the receiver operating characteristic curve (AUC) of 0.918 and a recall of 0.995 on the local cohort. The ir-PPS effectively risk-stratified NSCLC patients. Patients in the low-risk group (n = 40) had significantly improved progression-free survival (PFS) after ICI treatment compared to those in the high-risk group (n = 66, log-rank P = 0.004, hazard ratio (HR) = 3.65, 95%CI: 1.75-7.60). The ir-PPS demonstrated good discriminatory power for predicting 6-month PFS (AUC = 0.750), 12-month PFS (AUC = 0.677), and 18-month PFS (AUC = 0.662). The low-risk group exhibited increased expression of immune checkpoint molecules, cytotoxicity-related genes, an elevated abundance of tumour-infiltrating lymphocytes, and enhanced activity in immune stimulatory pathways. The ir-PPS signature derived from H&E images using deep learning could predict ICIs prognosis in NSCLC patients. The ir-PPS provides a novel imaging biomarker that may help select optimal candidates for ICIs therapy in NSCLC.

Serum Exosomal MiR-874 as a Potential Biomarker for Nonsmall Cell Lung Cancer Diagnosis and Prognosis

AbstractLung cancer, primarily nonsmall cell lung cancer (NSCLC), is a leading cause of cancer-related fatalities globally. Due to late detection, the 5-year survival rate for NSCLC remains low. Therefore, the current research aimed to assess the diagnostic and prognostic value of serum exosomal miR-874 levels in NSCLC patients. This study involved 161 NSCLC patients and 80 control subjects. Blood samples were collected from all participants, and serum exosomal MiR-874 levels were quantified using quantitative reverse transcription-polymerase chain reaction. The study revealed a significant decrease in MiR-874 levels among NSCLC patients compared to controls. The receiver operating characteristic analysis demonstrated the diagnostic value of serum exosomal MiR-874 in effectively distinguishing NSCLC patients from controls.Furthermore, associations were observed between serum exosomal MiR-874 expression and adverse clinical factors such as young age, male sex, smoking, high tumor grade, squamous cell carcinoma histopathology, advanced tumor stage, and lymphatic involvement. Patients with high levels of MiR-874 had significantly longer overall and disease-free survival compared to those with lower levels. The study demonstrates that levels of serum exosomal miR-874 are considerably lower in NSCLC patients, indicating its potential as a diagnostic biomarker. The study's findings suggest that the expression of MiR-874 may predict the prognosis of NSCLC patients based on clinical features.

ABCG2 Gene Expression in Non-Small Cell Lung Cancer.

Background/Objectives: ATP-binding cassette subfamily G member 2 [ABCG2/breast cancer resistance protein (BCRP)] contributes to mechanisms of multidrug resistance (MDR) and is a marker of side population (SP) cells in human cancers. The primary objective of this study was to investigate the impact of ABCG2 gene expression on the non-small cell lung cancer (NSCLC) development, course of cancer disease, and patient prognosis using publicly available data. Obtained results were supplemented with assessment of ABCG2 expression in blood of NSCLC patients. Methods: The dataset of lung cancer was analyzed utilizing the TIMER 2.0, UALCAN, TNMplot, MEXPRESS, cBioPortal, MethSurv, KM Plotter, STRING, and ShinyGO 0.80 databases. Blood samples from 50 patients were assessed using the real-time PCR method. Results: The ABCG2 gene was expressed at a low level in NSCLC, and did not correlate with clinical aggressiveness of lung cancer. Higher ABCG2 expression improved overall survival, but only in LUAD. In addition, CpG sites located on the CpG island affecting the NSCLC patient's prognosis were indicated. In the case of our own laboratory results, the study did not reveal any changes in the ABCG2 expression levels in blood collected from patients at different time points during the diagnostic-therapeutic procedure. In the in silico analysis, most ABCG2 protein interactors were associated with the development of drug resistance. Conclusions: ABCG2 appears to have a particularly significant impact on the survival of patients with lung cancer and on the effect of immunotherapy related to immune cell infiltration. Presented findings may support personalized medicine strategies based on bioinformatics findings.

Multiregional transcriptomic profiling provides improved prognostic insight in localized non-small cell lung cancer

Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function—taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.

Glycolysis proved to have a prognostic value in lung cancer; however, to identify glycolysis-related genomic markers is expensive and challenging. This study aimed at identifying glycolysis-related computed tomography (CT) radiomics features to develop a deep-learning prognostic model for non-small cell lung cancer (NSCLC). The study included 274 NSCLC patients from cohorts of The Second Affiliated Hospital of Soochow University (SZ; n=64), the Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), and the Gene Expression Omnibus dataset (n=136). Initially, the glycolysis enrichment scores were evaluated using a single-sample gene set enrichment analysis, and the cut-off values were optimized to investigate the prognostic potential of glycolysis genes. Radiomic features were then extracted using LIFEx software. The least absolute reduction and selection operator (LASSO) algorithm was employed to determine the glycolytic CT radiomics features. A deep-learning prognostic model was constructed by integrating CT radiomics and clinical features. The biological functions of the model were analyzed by incorporating RNA sequencing data. Kaplan-Meier curves indicated that elevated glycolysis levels were associated with poorer survival outcomes. The LASSO algorithm identified 11 radiomic features that were then selected for inclusion in the deep-learning model. They have shown significant discrimination capability in assessing glycolysis status, achieving an area under the curve value of 0.8442. The glycolysis-based radiomics deep-learning model was named the DeepGR model. This model was able to effectively predict the clinical outcomes of NSCLC patients with AUCs of 0.8760 and 0.8259 in the SZ and TCGA cohorts, respectively. High-risk DeepGR scores were strongly associated with poor overall survival, resting memory CD4+ T cells, and a high response to programmed cell death protein 1 immunotherapy. The DeepGR model effectively predicted the prognosis of NSCLC patients.

Prognostic significance of bone metastasis and clinical value of bone radiotherapy in metastatic non-small cell lung cancer receiving PD-1/PD-L1 inhibitors: results from a multicenter, prospective, observational study.

Bone metastasis (BoM) is a prevalent occurrence in patients with non-small cell lung cancer (NSCLC), significantly impacting prognosis and diminishing both survival rates and patients' quality of life. More and more studies have demonstrated that immunotherapy can improve the prognosis of NSCLC patients with bone metastases. Previous investigations pertaining to BoM in NSCLC have generally suffered from small sample sizes, absence of propensity score matching (PSM) to equate baseline characteristics, and an omission of the examination of patterns of treatment failure. This study aims to evaluate the prognostic significance of BoM and potential clinical value of bone radiation in metastatic NSCLC patients receiving immunotherapy. Metastatic NSCLC patients receiving programmed cell death protein 1/programmed cell death-ligand 1 (PD-1/PD-L1) inhibitors from three academic centers were enrolled in a prospective, observational trial (https://clinicaltrials.gov/study/NCT04766515) and those with measurable disease and adequate follow-up were retrospectively reviewed. Propensity score matched (PSM) patients with and without BoM were included in this study. Treatment efficacy, pattern of failure and clinical value of bone radiotherapy were extensively evaluated. A total of 544 out of 1,451 immunotherapy-treated NSCLC patients were included after PSM, including 272 with BoM and 272 without. Patients with baseline BoM had a median progression-free survival (PFS) of 7.8 months [95% confidence interval (CI): 7.0-8.7], lower than those without it (9.5 months; 95% CI: 8.9-10.0) (P<0.001). Patients with baseline BoM had a median overall survival (OS) of 14.5 months (95% CI: 12.6-16.4), lower than those without 27.6 months (95% CI: 25.1-30.1) (P<0.001). Patients with BoM also had lower objective response rate than those without it (11.1% vs. 15.8%, P<0.001). Initial disease progression in the bone was more common in those with BoM (56.5%) compared to those without it (31.7%) (P<0.001). Meanwhile, among patients with BoM, no significant difference of PFS was found between those receiving bone radiation or not, possibly due to a dominant use of palliative radiotherapy. Baseline BoM correlated with worse prognosis and palliative bone radiation did not improve PFS in metastatic NSCLC patients receiving PD-1/PD-L1 inhibitors.

Exploring the impact of HDL and LMNA gene expression on immunotherapy outcomes in NSCLC: a comprehensive analysis using clinical & gene data.

Analyzing the impact of peripheral lipid levels on the efficacy of immune checkpoint inhibitor therapy in non-small cell lung cancer (NSCLC) patient populations and exploring whether it can serve as a biomarker for broadening precise selection of individuals benefiting from immunotherapy. We retrospectively collected clinical data from 201 cases of NSCLC patients receiving immune checkpoint inhibitor therapy. The clinical information included biochemical indicators like total cholesterol, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). We utilized machine learning algorithms and Cox proportional hazards regression models to investigate independent predictors for both short-term and long-term efficacy of immunotherapy. Additionally, we concurrently developed a survival prediction model. Analyzing the Genes of Patients with Treatment Differences to Uncover Mechanisms. Correlation analysis revealed a significant positive association between HDL and ORR, DCR, and PFS. T-test results indicated that the high-HDL group exhibited higher DCR (81.97% vs. 45.57%) and ORR (61.48% vs. 16.46%). Kruskal-Wallis test showed that the high-HDL group had a longer median PFS (11 months vs. 6 months). Utilizing six machine learning algorithms, we constructed models to predict disease relief and stability. The model built using the random forest algorithm demonstrated superior performance, with AUC values of 0.858 and 0.802. Furthermore, both univariate and multivariate Cox analyses identified HDL and LDL as independent risk factors for predicting PFS. In patients with poor immunotherapy response, there is upregulation of BCL2L11, AKT1, and LMNA expression. HDL and LDL are independent factors influencing the survival prognosis of NSCLC patients undergoing immune checkpoint inhibitor therapy. HDL is expected to become new biomarkers for predicting the immunotherapy efficacy in patients with NSCLC. In patients with poor immunotherapy response, upregulation of the LMNA gene leads to apoptosis resistance and abnormal lipid metabolism.