Lung Adenocarcinoma Cohort Research Articles

Transcriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors

BackgroundLung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients’ outcome.MethodsWe applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets.ResultsThrough our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity.ConclusionThe dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion.

Post-Transcriptional Modifications to miRNAs Undergo Widespread Alterations, Creating a Unique Lung Adenocarcinoma IsomiRome.

MicroRNAs (miRNAs) modulate the expression of oncogenes and tumor suppressor genes, functioning as significant epigenetic regulators in cancer. IsomiRs are miRNA molecules that have undergone small modifications during miRNA processing. These modifications can alter an isomiR's binding stability with mRNA targets, and certain isomiRs have been implicated in the development of specific cancers. Still, the isomiRomes of many tissues, including the lung, have not been characterized; Methods: In this study, we analyzed small RNA sequencing data for three cohorts of lung adenocarcinoma (LUAD) and adult non-malignant lung (ANL) samples. We quantified isomiR expression and found 16 A-to-I edited isomiRs expressed in multiple cohorts, as well as 213 5' isomiRs, 128 3' adenylated isomiRs, and 100 3' uridylated isomiRs. Rates of A-to-I editing at editing hotspots correlated with mRNA expression of the editing enzymes ADAR and ADARB1, which were both observed to be deregulated in LUAD. LUAD samples displayed lower overall rates of A-to-I editing and 3' adenylation than ANL samples. Support vector machines and random forest models were trained on one cohort to distinguish ANL and stage I/II LUAD samples using reads per million (RPM) and frequency data for different types of isomiRs. Models trained on A-to-I editing rates at editing hotspots displayed high accuracy when tested on the other two cohorts and compared favorably to classifiers trained on miRNA expression alone; Conclusions: We have identified isomiRs in the human lung and found that their expression differs between non-malignant and tumor tissues, suggesting they hold potential as cancer biomarkers.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study

IntroductionMolecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. MethodsThis retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. ResultsModels trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DiscussionModel explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. ConclusionAlthough deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

Defining the Soluble and Extracellular Vesicle Protein Compartments of Plasma Using In-Depth Mass Spectrometry-Based Proteomics.

Plasma-derived extracellular vesicles (pEVs) are a potential source of diseased biomarker proteins. However, characterizing the pEV proteome is challenging due to its relatively low abundance and difficulties in enrichment. This study presents a streamlined workflow to identify EV proteins from cancer patient plasma using minimal sample input. Starting with 400 μL of plasma, we generated a comprehensive pEV proteome using size exclusion chromatography (SEC) combined with HiRIEF prefractionation-based mass spectrometry (MS). First, we compared the performance of HiRIEF and long gradient MS workflows using control pEVs, quantifying 2076 proteins with HiRIEF. In a proof-of-concept study, we applied SEC-HiRIEF-MS to a small cohort (12) of metastatic lung adenocarcinoma (LUAD) and malignant melanoma (MM) patients. We also analyzed plasma samples from the same patients to study the relationship between plasma and pEV proteomes. We identified and quantified 1583 proteins in cancer pEVs and 1468 proteins in plasma across all samples. While there was substantial overlap, the pEV proteome included several unique EV markers and cancer-related proteins. Differential analysis revealed 30 DEPs in LUAD vs the MM group, highlighting the potential of pEVs as biomarkers. This work demonstrates the utility of a prefractionation-based MS for comprehensive pEV proteomics and EV biomarker discovery. Data are available via ProteomeXchange with the identifiers PXD039338 and PXD038528.

Integrative transcriptome analysis reveals the molecular events underlying impaired T-cell responses in EGFR-mutant lung cancer

EGFR mutations are critical oncogenic drivers in lung adenocarcinoma (LUAD). However, the mechanisms by which they impact the tumor microenvironment (TME) and tumor immunity are unclear. Furthermore, the reasons underlying the poor response of EGFR-mutant (EGFR-MU) LUADs to immunotherapy with PD-1/PD-L1 inhibitors are unknown. Utilizing single-cell RNA (sc-RNA) and bulk RNA sequencing datasets, we conducted high-dimensional weighted gene coexpression network analysis to identify key genes and immune-related pathways contributing to the immunosuppressive TME. EGFR-MU cancer cells downregulated MHC class I genes to evade CD8+ cytotoxic T cells, expressed substantial levels of MHC class II molecules, and engaged with CD4+ regulatory T cells (Tregs). EGFR-MU tumors may recruit Tregs primarily through the CCL17/CCL22/CCR4 axis, leading to a Treg-enriched TME. High levels of MHC class II-positive cancer-associated fibroblasts and tumor endothelial cells were found within EGFR-MU tumors. Owing to the absence of costimulatory factors, they may inhibit rather than activate the tumor antigen-specific CD4+ T-cell response, contributing further to immune suppression. Multiplex immunohistochemistry analyses in a LUAD cohort confirmed increased expression of MHC class II molecules in cancer cells and fibroblasts in EGFR-MU tumors. Our research elucidates the highly immunosuppressive TME in EGFR-MU LUAD and suggests potential targets for effective immunotherapy.

PARP4 interacts with hnRNPM to regulate splicing during lung cancer progression

BackgroundThe identification of cancer driver genes from sequencing data has been crucial in deepening our understanding of tumor biology and expanding targeted therapy options. However, apart from the most commonly altered genes, the mechanisms underlying the contribution of other mutations to cancer acquisition remain understudied. Leveraging on our whole-exome sequencing of the largest Asian lung adenocarcinoma (LUAD) cohort (n = 302), we now functionally assess the mechanistic role of a novel driver, PARP4.MethodsIn vitro and in vivo tumorigenicity assays were used to study the functional effects of PARP4 loss and mutation in multiple lung cancer cell lines. Interactomics analysis by quantitative mass spectrometry was conducted to identify PARP4’s interaction partners. Transcriptomic data from cell lines and patient tumors were used to investigate splicing alterations.ResultsPARP4 depletion or mutation (I1039T) promotes the tumorigenicity of KRAS- or EGFR-driven lung cancer cells. Disruption of the vault complex, with which PARP4 is commonly associated, did not alter tumorigenicity, indicating that PARP4’s tumor suppressive activity is mediated independently. The splicing regulator hnRNPM is a potentially novel PARP4 interaction partner, the loss of which likewise promotes tumor formation. hnRNPM loss results in splicing perturbations, with a propensity for dysregulated intronic splicing that was similarly observed in PARP4 knockdown cells and in LUAD cohort patients with PARP4 copy number loss.ConclusionsPARP4 is a novel modulator of lung adenocarcinoma, where its tumor suppressive activity is mediated not through the vault complex—unlike conventionally thought, but in association with its novel interaction partner hnRNPM, thus suggesting a role for splicing dysregulation in LUAD tumorigenesis.

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.Graphical

ROS1 fusions in resected stage I-III adenocarcinoma: Results from the European Thoracic Oncology Platform Lungscape project

BackgroundROS1 fusion is a relatively low prevalence (0.6–2.0%) but targetable driver in lung adenocarcinoma (LUAD). Robust and low-cost tests, such as immunohistochemistry (IHC), are desirable to screen for patients potentially harboring this fusion. The aim was to investigate the prevalence of ROS1 fusions in a clinically annotated European stage I-III LUAD cohort using IHC screening with the in vitro diagnostics (IVD)-marked clone SP384, followed by confirmatory molecular analysis in pre-defined subsets. MethodsResected LUADs constructed in tissue microarrays, were immunostained for ROS1 expression using SP384 clone in a ready-to-use kit and Ventana immunostainers. After external quality control, analysis was performed by trained pathologists. Staining intensity of at least 2+ (any percentage of tumor cells) was considered IHC positive (ROS1 IHC + ). Subsequently, ROS1 IHC + cases were 1:1:1 matched with IHC0 and IHC1 + cases and subjected to orthogonal ROS1 FISH and RNA-based testing. ResultsThe prevalence of positive ROS1 expression (ROS1 IHC + ), defined as IHC 2+/3+, was 4 % (35 of 866 LUADs). Twenty-eight ROS1 IHC + cases were analyzed by FISH/RNA-based testing, with only two harboring a confirmed ROS1 gene fusion, corresponding to a lower limit for the prevalence of ROS1 gene fusion of 0.23 %. They represent a 7 % probability of identifying a fusion among ROS1 IHC + cases. Both confirmed cases were among the only four with sufficient material and H-score ≥ 200, leading to a 50 % probability of identifying a ROS1 gene fusion in cases with an H-score considered strongly positive. All matched ROS1 IHC- (IHC0 and IHC1 + ) cases were also found negative by FISH/RNA-based testing, leading to a 100 % probability of lack of ROS1 fusion for ROS1 IHC- cases. ConclusionsThe prevalence of ROS1 fusion in an LUAD stage I-III European cohort was relatively low. ROS1 IHC using SP384 clone is useful for exclusion of ROS1 gene fusion negative cases.

A heterogeneous tumor immune microenvironment of uncommon epidermal growth factor receptor mutant non-small cell lung cancer.

Common epidermal growth factor receptor (EGFR) mutations are usually not considered for immunotherapy in non-small cell lung cancer (NSCLC) due to poor efficacy. However, whether uncommon EGFR mutations are suitable for immunotherapy has not been thoroughly studied. Thus, we explored the tumor immune microenvironment (TME) features in uncommon EGFR mutant NSCLC. In this study, a total of 41 patients with EGFR mutations were included, the majority (85.4%) of whom were stage I. Among them, 22 patients harbored common mutations, while 19 patients presented with uncommon mutations. Compared with common mutations, uncommon mutations exhibited more infiltrating T cells and fewer M2 macrophages, upregulated expression of antigen processing and a presentation pathway. Unsupervised clustering based on the mIF profile identified two classes with heterogeneous TME in uncommon mutations. Class 1 featured the absence of PD-1+ cytotoxic T cell infiltration, and class 2 displayed a hotter TME because of the downregulated expression of hypoxia (p < 0.001), oxidative phosphorylation (p = 0.009), and transforming growth factor beta signaling (p = 0.01) pathways as well as increased expression of CTLA4 (p = 0.001) and PDCD1 (p = 0.004). The association of CTLA4 and PDCD1 with TME profiles was validated in a TCGA lung adenocarcinoma cohort with uncommon EGFR mutations. Our study reveals the distinct and heterogeneous TME features in uncommon EGFR mutant NSCLC.

Abstract PR013: Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality

Abstract The mutual exclusivity of somatic variants in cancer suggests that current somatic variants can exert antagonistic epistatic effects on the selection of new mutations, contributing to a complex landscape of evolutionary trajectories. However, quantifying pairwise and higher-order epistatic effects—which are essential to estimation of the trajectory of likely cancer genotypes—has been a challenge. We have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation, dependent on somatic cancer genotype. We coupled the continuous-time Markov chain model with an approach that deconvolutes the underlying mutation rates and selection intensities across trajectories of oncogenesis. By elucidating the routes of variant evolution and the strengths of selective forces at play, our approach lays the groundwork for quantifying antagonistic epistatic effects of specific somatic genotypes. We applied our approach to report the variant mutation rates, selection intensities, and consequent substitution rates for lung adenocarcinoma, comparing smoker and nonsmoker tumor cohorts. We describe the distinct influences of smoking on underlying mutation rates and on physiology affecting the selective regime of lung tissue. Inference of the most likely routes of site-specific variant evolution, as well as estimation of the selection strength operating on each step along the route represents a key component for prioritization, development, and implementation of personalized cancer therapies that target synthetic lethality. Citation Format: Jorge A. Alfaro-Murillo, Krishna Dasari, Jeffrey P. Townsend. Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR013.

Identification and validation of GIMAP family genes as immune-related prognostic biomarkers in lung adenocarcinoma

BackgroundThe GIMAP family genes play a key role in immune function. Increasing evidence suggests that GIMAP genes were implicated in the tumorigenesis of lung adenocarcinoma (LUAD). This study aimed to investigate the clinical significance of GIMAP family genes in LUAD. MethodsIn this study, we explored the expression, mutation, prognostic value of GIMAP family genes and the correlation with immune microenvironment in LUAD. We further investigated the relationship between GIMAP family genes expression and immunotherapy response in GEO LUAD and melanoma cohorts. ResultsAmong the GIMAP family genes, the expression levels of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly lower in LUAD tumor tissues than normal tissues. Most GIMAP genes were closely related to age, tumor grade and T stage, but not significantly related to sex, N stage and M stage. In the overall population, patients with high expression of GIMAP family genes had a significant longer overall survival (OS). GO and KEGG enrichment analysis showed that GIMAP family genes were highly enriched in immune-related biological process. The expression of GIMAP family genes was positively correlated with immune cell infiltration and immune checkpoint molecules. Furthermore, high expression of GIMAP family genes were correlated with therapeutic response to immunotherapy in LUAD and melanoma patients. ConclusionIn this study, we identified that GIMAP family genes were significantly associated with immune cell infiltration and immune checkpoint molecules. They potentially play a critical role in anti-tumor immunity and serve as immunotherapy biomarkers.

Clinical significance and immune landscape of a novel immune cell infiltration-based prognostic model in lung adenocarcinoma

Tumor-infiltrating immune cells (TICs) play a central role in the tumor microenvironment, which can reflect the host anti-tumor immune response. However, few studies have explored TICs in predicting the prognosis of lung adenocarcinoma (LUAD). In our study, we enrolled 2470 LUAD patients from TCGA and GEO databases, and the normalized enrichment scores for 65 immune cell types were quantified for each patient. An immune-related risk score (IRRS) was built on the basis of 17 selected TICs using LASSO regression analysis, and the results showed that high-risk patients were correlated with shorter survival time for the LUAD cohorts. Correlation analyses between IRRS and clinical characteristics were also evaluated to validate the clinical use of IRRS. In addition, we analyzed the differences in the distribution of immune cell infiltration and immunoregulatory gene expression, which may facilitate individual immunotherapy. Based on the above result, we conclude that IRRS can act as a powerful predictor for risk stratification and prognosis prediction, and may facilitate the decision-making process for LUAD patients.

Comprehensive landscape of junctional genes and their association with overall survival of patients with lung adenocarcinoma.

Junctional proteins are involved in tumorigenesis. Therefore, this study aimed to investigate the association between junctional genes and the prognosis of patients with lung adenocarcinoma (LUAD). Transcriptome, mutation, and clinical data were retrieved from The Cancer Genome Atlas (TCGA). "Limma" was used to screen differentially expressed genes. Moreover, Kaplan-Meier survival analysis was used to identify junctional genes associated with LUAD prognosis. The junctional gene-related risk score (JGRS) was generated based on multivariate Cox regression analysis. An overall survival (OS) prediction model combining the JGRS and clinicopathological properties was proposed using a nomogram and further validated in the Gene Expression Omnibus (GEO) LUAD cohort. To our knowledge, this study is the first to demonstrate the correlation between the mRNA levels of 14 junctional genes (CDH15, CDH17, CDH24, CLDN6, CLDN12, CLDN18, CTNND2, DSG2, ITGA2, ITGA8, ITGA11, ITGAL, ITGB4, and PKP3) and clinical outcomes of patients with LUAD. The JGRS was generated based on these 14 genes, and a higher JGRS was associated with older age, higher stage levels, and lower immune scores. Thus, a prognostic prediction nomogram was proposed based on the JGRS. Internal and external validation showed the good performance of the prediction model. Mechanistically, JGRS was associated with cell proliferation and immune regulatory pathways. Mutational analysis revealed that more somatic mutations occurred in the high-JGRS group than in the low-JGRS group. The association between junctional genes and OS in patients with LUAD demonstrated by our "TCGA filtrating and GEO validating" model revealed a new function of junctional genes.

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.

PCDH11X mutation as a potential biomarker for immune checkpoint therapies in lung adenocarcinoma.

Immune checkpoint inhibitors (ICIs) have achieved impressive success in lung adenocarcinoma (LUAD). However, the response to ICIs varies among patients, and predictive biomarkers are urgently needed. PCDH11X is frequently mutated in LUAD, while its role in ICI treatment is unclear. In this study, we curated genomic and clinical data of 151 LUAD patients receiving ICIs from three independent cohorts. Relations between PCDH11X and treatment outcomes of ICIs were examined. A melanoma cohort collected from five published studies, a pan-cancer cohort, and non-ICI-treated TCGA-LUAD cohort were also examined to investigate whether PCDH11X mutation is a specific predictive biomarker for LUAD ICI treatment. Among the three ICI-treated LUAD cohorts, PCDH11X mutation (PCDH11X-MUT) was associated with better clinical response compared to wild-type PCDH11X (PCDH11X-WT). While in ICI-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and the non-ICI-treated TCGA-LUAD cohort, no significant differences in overall survival (OS) were observed between the PCDH11X-MUT and PCDH11X-WT groups. PCDH11X mutation was associated with increased PD-L1 expression, tumor mutation burden (TMB), neoantigen load, DNA damage repair (DDR) mutations, and hot tumor microenvironment in TCGA-LUAD cohort. Our findings suggested that the PCDH11X mutation might serve as a specific biomarker to predict the efficacy of ICIs for LUAD patients. Considering the relatively small sample size of ICI-treated cohorts, future research with larger cohorts and prospective clinical trials will be essential for validating and further exploring the role of PCDH11X mutation in the context of immunotherapy outcomes in LUAD. KEY MESSAGES: PCDH11X mutation is associated with better clinical response compared to wild type PCDH11X in three ICIs-treated LUAD cohorts. In ICIs-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and non-ICIs-treated TCGA-LUAD cohortsPCDH11X mutation is not associated with better clinical response, suggesting PCDH11X mutation might be a specific biomarker to predict the efficacy of ICIs treatment for LUAD patients. PCDH11X mutation is associated with increased PD-L1 expression, tumor mutation burden, and neoantigen load in TCGA-LUAD cohort. PCDH11X mutation is associated with hot tumor microenvironment in TCGA-LUAD cohort.

Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis

BackgroundPrevious studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear.MethodsZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR.ResultsGenetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers.ConclusionsOur results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.

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

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. 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. 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. 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.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Identification and Functional Characterization of Essential Genes Related to Gefitinib Sensitivity in Lung Adenocarcinoma.

This study aimed to determine the molecular markers related to gefitinib sensitivity for guiding the prognosis of lung adenocarcinoma (LUAD) and providing new evidence for promoting the precise treatment of LUAD. Lung adenocarcinoma (LUAD) is a prevalent lung cancer subtype with inferior survival outcomes. However, gefitinib is the first molecular targeted drug approved by Food and Drug Administration (FDA) to treat advanced LUAD. Gefitinib sensitivity-related gene targets for LUAD are rarely studied. This study was designed to probe the potential molecular markers related to the sensitivity of gefitinib in LUAD. The gene expression profiles of LUAD cells in the Genomics of Drug Sensitivity in Cancer (GDSC) database were used for Weighted Gene Co-expression Network Analysis (WGCNA) to select the modules most related to gefitinib sensitivity. The Cancer Genome Atlas (TCGA) database was used to compare the expression of LUAD and para-cancerous tissues. Differentially expressed genes (DEGs) were then filtered and intersected with the highly linked genes in the module relevant to gefitinib sensitivity. Univariate Cox regression analysis was conducted to identify prognostically related genes to LUAD. The correlation between genes and drug IC50 was calculated by Spearman correlation analysis. Quantitative RT-PCR and immunofluorescence detection validated hub genes FAM13B and PFKP expressions. Among the 10 modules divided by WGCNA, the module with the most significant positive correlation and the most significant negative correlation with gefitinib sensitivity were found. FAM13B, PFKP, FGD3, RNASE1, MUC16, GJB5, and GJB3 were hub genes related to gefitinib sensitivity in LUAD. Significantly, the low expressed FAM13 in LUAD tissues positively correlated with immune response. At the same time, overexpressed PFKP in the LUAD cohort was related to an unfavorable prognosis, cell proliferation, and cell cycle. We also found that FAM13B and PFKP expressions were enhanced in LUAD cell lines. This study identified 7 critical genes related to gefitinib sensitivity in LUAD. Functionally, genes positively correlated with gefitinib sensitivity might regulate the progression of LUAD through the immune, cell cycle, and metabolic pathways and showed potential effects in predicting sensitivity to different drugs. These findings help offer a theoretical direction for personalized treatment of LUAD.

Prognostic Cell Death Index for Lung Adenocarcinoma: A Comprehensive Transcriptome-Based Analysis of Twelve Programmed Cell Death Pattern Genes.

Lung adenocarcinoma (LUAD) is a prominent contributor to global cancer mortality, characterized by constrained prognosis. This study aimed to develop a novel prognostic indicator, the Cell Death Index (CDI), utilizing twelve programmed cell death (PCD) pattern genes, to predict the immune infiltration and prognosis in LUAD patients. We collected PCD-related genes and identified prognostic PCD genes in the Cancer Genome Atlas (TCGA)-LUAD dataset, and made rigorous validation in the Clinical Proteomic Tumor Analysis Consortium (CPTAC)-LUAD cohorts. CDI was calculated using a multivariable Cox regression model. Functional enrichment and tumor microenvironment were evaluated. Drug sensitivity prediction and nomogram development were performed to assess CDI's potential value. The results revealed 10 PCD genes (ERO1A, CDK5R1, TRIM6, DNASE2B, ITPRIP, MRGPRX2, FGA, NDUFA13, NLRP2, and CD68) significantly associated with LUAD prognosis. The CDI was constructed and showed high accuracy in predicting patient survival with C-index values of 0.801 and 0.794 in the prognosis cohort and validation cohort, respectively. CDI is also indicative of variations in biological functions, tumor microenvironment, and immune cell infiltration including neutrophils, activated mast cells, activated dendritic cells, M0 macrophages, resting natural killer cells, γδT cells, and activated memory CD4+T cells. Furthermore, drug sensitivity analysis indicated potential targeted strategies. The CDI, based on PCD genes, serves as a robust prognostic tool for LUAD, offering profound insights into tumor biology, immune response, and personalized treatment strategies. This study underscores the pivotal role of PCD mechanisms in LUAD pathogenesis and identifies potential therapeutic targets.