The impact of the Cancer Genome Atlas on lung cancer

Prospective Upfront Next-Generation Sequencing for Advanced Non-Small Cell Lung Cancer: Real-World Outcomes from the Ion Chiricuță Oncology Institute

Upfront Next-Generation Sequencing (NGS) is increasingly recommended in advanced NSCLC to guide targeted therapy. This prospective single-center study in Romania evaluated routine, upfront NGS in advanced NSCLC at baseline (tissue and/or liquid) and progression (liquid). Baseline FoundationOne NGS (tissue/liquid) was performed in 119 consecutive stage IV NSCLC patients, along with PD-L1 immunohistochemistry (IHC, SP263). Liquid biopsy was repeated at progression. Turnaround time (TAT), the prevalence of actionable targets, and clinical utility were assessed. Patients were predominantly male (68.1%) with a median age of 62 years (range 30-86). Most had ECOG PS 0-1 (79%) and non-squamous histology (67.2%). Never-smokers accounted for 25.2%. The median TAT for the NGS results was 9 days (range 5-21). Overall, 671 genetic alterations were detected in 149 genes. The mean number of distinct mutations per patient dropped from 5.6 at baseline to 4.3 at progression. Tissue samples yielded more alterations (6 per patient) than baseline liquid biopsies (4.6). Squamous tumors had more alterations (7.1 vs. 4.8 in non-squamous), and the number of smokers exceeded that of never-smokers (6 vs. 4.5). TP53 was the most frequent (70.59%). Actionable variants were found in 74.8% of patients, though only 35.3% received personalized therapy, largely due to performance status deterioration, reimbursement, or trial availability barriers. Common targets in non-squamous tumors included EGFR (21%), KRAS G12C (11%), NF1 (11%), and ERBB2 (6%); in squamous tumors, common targets included NF1 (24%), PIK3CA (18%), and ERBB2 (8%). Among smokers, driver mutations were often NF1 (15%), PIK3CA (11%), KRAS G12C (9%), and ERBB2 (8%); never-smokers were dominated by EGFR (45%), NF1 (15%), and KRAS G12C (8%). TMB ≥ 10 mut/Mb was seen in 26.9%; no patients were MSI-H. PD-L1 TPS was <1% in 33% of patients, 1-49% in 20%, ≥50% in 18%, and unknown in 29%. Upfront NGS offers rapid, comprehensive genomic data, guiding tailored therapies and trials in advanced NSCLC. Liquid rebiopsy at progression further refines treatment decisions.

Deciphering the Role of CD14 in Helicobacter Pylori-associated Gastritis and Gastric Cancer: Combing Bioinformatics Analysis and Experiments

Background: Gastric cancer (GC) is the third leading cause of cancer-related death and is associated with high mortality and morbidity. Helicobacter pylori (HP) infection is the most important cause of GC. We aimed to identify the core genes of HP caused GC and further elucidate the underlying mechanisms. Methods: GC and HP associated gastritis (HPAG) gene expression data were sourced from Gene Expression Omnibus. Key genes affecting GC prognosis were identified using Cytoscape software. Patient groups were formed based on key gene expression, and the immune analyses were performed with R. MNU, derived from nitrite by HP, was given to GC mice (240ppm) for histology and fluorescence assays. For in vitro experiments, cells received MNU (20 μM) stimulation for 24 hours. Results: CD14 was the only key gene identified. A total of 412 GC patients were divided into CD14-high and CD14-low groups. The two groups showed significant differences in immune cell populations and immune checkpoints. In particular, there was a notable increase in M2 macrophages in GC patients with high CD14 expression (P <0.001). GC Patients with high CD14 expression exhibited a more pronounced immune response than those with low CD14 expression, and elevated CD14 expression positively correlated with the efficacy of CTLA4 therapy (P <0.05). These results indicated that CD14 expression was strongly correlated with the GC immune response. A noticeable increase in CD14 levels was observed in MNU-induced GC animals, cell models, and GC patients. In addition, the number of M2 macrophages was increased in MNU-induced GC mice. Conclusion: Reducing CD14 expression may increase the survival rate of GC patients through the modulation of immune responses. The complex mechanism of CD14's influence on prognosis deserves further investigation.

Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies

Background

Lung adenocarcinoma (LUAD) remains a significant global health challenge, with an urgent need for innovative predictive, preventive, and personalized medicine (PPPM) strategies to improve patient outcomes. This study leveraged multi-omics and machine learning approaches to uncover the prognostic roles of B cells in LUAD, thereby reinforcing the PPPM approach.

Methods

We integrated multi-omics data, including bulk RNA, ATAC-seq, single-cell RNA, and spatial transcriptomics sequencing, to characterize the B cell landscape in LUAD within the PPPM framework. Subsequently, we developed an integrative machine learning program that generated the Scissor+ related B cell score (SRBS). This score was validated in the training and validation sets, and its prognostic value was assessed along with clinical features to develop predictive nomograms. This study further assessed the role of SRBS and SRBS genes in response to immunotherapy and identified personalized drug targets for distinct risk subgroups, with gene expression verified experimentally to ensure tailored medical interventions.

Results

Our analysis identified 79 Scissor+ B cell genes linked to LUAD prognosis, supporting the predictive aspect of PPPM. The SRBS model, which utilizes multiple machine learning algorithms, performed excellently in predicting prognosis and clinical transformation, embodying the preventive and personalized aspects of PPPM. Multifactorial analysis confirmed that SRBS was an independent prognostic factor. We observed varying biological functions and immune cell infiltration in the tumor immune microenvironment (TIME) between the high- and low-SRBS groups, underscoring personalized treatment approaches. Notably, patients with elevated SRBS may exhibit resistance to immunotherapy but show increased sensitivity to chemotherapy and targeted therapies. Additionally, we found that LDHA, as an SRBS gene with significant clinical implications, may regulate the sensitivity of LUAD cells to cisplatin.

Conclusion

This study presents a B cell-associated gene signature that serves as a prognostic marker to facilitate personalized treatment for patients with LUAD, adhering to the principles of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00390-4.

Improving diagnostic capabilities in lung cancer through next-generation sequencing: a narrative review

Background and Objective

Lung neoplasia is the leading cause of cancer death worldwide, thus, early detection and accuracy in establishing a diagnosis is paramount. As a consequence of decades of basic and translational studies revealing the genetic basis of lung cancer, a paradigm shift has occurred toward a personalized approach to medicine whereby mutational analysis confers an opportunity for safer, and expedient treatment options. In this context, next-generation sequencing (NGS) has emerged as a vital technological advance, and has become increasingly established as a core method for rapidly and effectively identifying actionable mutations in lung cancer. For these reasons, an updated review of the literature across invasive and non-invasive diagnostic modalities in lung cancer is warranted to inform diagnostic approaches and prompt new investigations. The objective of the present review is to provide a focused update on applications of NGS in lung cancer diagnostics, with a special focus on tissue acquisition methodologies and mutational analysis.

Methods

The search strategy included a survey of the current literature from 2005 to 2024 in PubMed, Medline, Scopus, and Google Scholar. Eligible study types included original research, literature reviews (narrative and systematic), and observational studies. which encompassed findings pertinent to the lung cancer diagnostics, mutational analysis and lung cancer treatment overlapping with applications and use of NGS technologies.

Key Content and Findings

There are extensive and diverse advantages to the use of NGS in lung cancer diagnostics, especially when compared to traditional sequencing techniques including, speed, effectiveness, easy adoption in the context of analysis of samples prepared for lung cancer diagnosis. Advances in cell-free DNA reinforce the firm role of NGS in novel approaches.

Conclusions

NGS implementation is a crucial and beneficial technological leap in lung cancer diagnosis, especially given the environment of novel and established targeted and immune based therapies which require mutational testing. Its numerous benefits such as expedient results and reduced sample requirements will continue to ability optimize lung cancer outcomes by virtue of improved patient safety, reduction of unnecessary procedures, and provision of accurate results.

Identifying Effect Modification of Latent Population Characteristics on Risk Factors with a Sparse Varying Coefficient Regression

Leveraging observational data to understand the associations between risk factors and disease outcomes and conduct disease risk prediction is a common task in epidemiology. While traditional linear regression and other machine learning models have been extensively implemented for this task, the associations between risk factors and disease outcomes are typically deemed fixed. In many cases, however, such associations may vary by some underlying features of the individuals, which may involve certain subpopulation characteristics and environmental factors. While data for these latent features may not be available, the observed data on risk factors may have captured some proportion of the variation in these features. Thus extracting latent factors from risk factors and incorporating this effect modification into the model may better capture the underlying data structure and improve inference. We develop a novel regression model with some coefficients varying as functions of latent features extracted from the risk factors. We have demonstrated the superiority of our approach in various data settings via simulation studies. An application on a dataset for lung cancer patients from The Cancer Genome Atlas (TCGA) Program showed that our approach led to a 6% - 118% increase in (AUC-0.5) for distinguishing between different lung cancer stages compared to the classic lasso and elastic net regressions and identified interesting latent effect modifications associated with certain gene pathways.

Identification of the molecular subtype and prognostic characteristics of lung adenocarcinoma based on CD8+ T cell-related gene signature

BackgroundLung adenocarcinoma (LUAD) stands as a major respiratory malignancy with high mortality. With the advent of immunotherapy, new therapeutic avenues have emerged in cancer treatment.ObjectiveOur focus aimed at developing a CD8+ T cell-based immune gene prognostic model (CDIGPM) for LUAD, shedding light on the immunological aspects and the potential advantages of immunotherapy in distinct CDIGPM-defined LUAD categories.MethodsData from LUAD patients were extracted from the TCGA and GEO databases (GSE11969). The differentially expressed genes (DEGs) were intersected with immune genes from ImmPort and InnateDB, yielding 89 significant immune genes related to CD8+ T cells (CDIGs). Univariate Cox regression and LASSO regression analyses were performed on 10 hub CDIGs (ADM, CAV1, CTSL, HLA-DMB, HLA-DQA1, IGHM, PLSCR1, PTGDS, S100A16, and WFDC2). Furthermore, the immunological attributes and the immunotherapy efficacy in CDIGPM-defined categories were explored. Moreover, to support the findings of the bioinformatics analysis, fifteen LUAD patients' tumor and adjacent tissues were collected for qRT-PCR detection of CDIGPM-related genes.ResultsKaplan-Meier analysis revealed that the high-CDIGPM group exhibited significantly poorer overall survival (OS) trajectories, whereas the low-CDIGPM group showed more favorable OS trajectories, indicating a better prognosis. Age, tumor stage, and CDIGPM score were identified as independent prognostic factors. The high-CDIGPM group was enriched in pathways related to the cell cycle, focal adhesion, and cancer, while the low-CDIGPM group was associated with immune response-related pathways. The CDIGPM model effectively differentiated clinical subtypes in patients with LUAD. QRT-PCR detection of Clinical LUAD samples also validated the differentially expression of CDIGPM model related genes.ConclusionsThe study highlights the prognostic importance of CDIGs in LUAD using the CDIGPM model, linking age, stage and CDIGPM score to poor outcomes. The identified genes and pathways provide potential therapeutic targets, deepening our understanding of LUAD's molecular landscape.

Integration of the bulk transcriptome and single-cell transcriptome reveals efferocytosis features in lung adenocarcinoma prognosis and immunotherapy by combining deep learning

BACKGROUND

Efferocytosis (ER) refers to the process of phagocytic clearance of programmed dead cells, and studies have shown that it is closely related to tumor immune escape.

METHODS

This study was based on a comprehensive analysis of TCGA, GEO and CTRP databases. ER-related genes were collected from previous literature, univariate Cox regression was performed and consistent clustering was performed to categorize lung adenocarcinoma (LUAD) patients into two subgroups. Lasso regression and multivariate Cox regression analyses were used to construct ER-related prognostic features, and multiple immune infiltration algorithms were used to assess the correlation between the extracellular burial-related risk score (ERGRS) and tumor microenvironment (TME). And the key gene HAVCR1 was identified by deep learning, etc. Finally, pan-cancer analysis of the key genes was performed and in vitro experiments were conducted to verify the promotional effect of HAVCR1 on LUAD progression.

RESULTS

A total of 33 ER-related genes associated with the prognosis of LUAD were identified, and the prognostic signature of ERGRS was successfully constructed to predict the overall survival (OS) and treatment response of LUAD patients. The high-risk group was highly enriched in some oncogenic pathways, while the low-ERGRS group was highly enriched in some immune-related pathways. In addition, the high ERGRS group had higher TMB, TNB and TIDE scores and lower immune scores. The low-risk group had better immunotherapeutic response and less likelihood of immune escape. Drug sensitivity analysis revealed that BRD-K92856060, monensin and hexaminolevulinate may be potential therapeutic agents for the high-risk group. And ERGRS was validated in several cohorts. In addition, HAVCR1 is one of the key genes, and knockdown of HAVCR1 in vitro significantly reduced the proliferation, migration and invasion ability of lung adenocarcinoma cells.

CONCLUSION

Our study developed a novel prognostic signature of efferocytosis-related genes. This prognostic signature accurately predicted survival prognosis as well as treatment outcome in LUAD patients and explored the role of HAVCR1 in lung adenocarcinoma progression.

Widespread Adoption of Precision Anticancer Therapies After Implementation of Pathologist-Directed Comprehensive Genomic Profiling Across a Large US Health System

PURPOSE

Precision therapies and immunotherapies have revolutionized cancer care, with novel genomic biomarker-associated therapies being introduced into clinical practice rapidly, resulting in notable gains in patient survival. Despite this, there is significant variability in the utilization of tumor molecular profiling that spans the timing of test ordering, comprehensiveness of gene panels, and clinical decision support through therapy and trial recommendations.

METHODS

To standardize testing, we designed a pathologist-directed test ordering system at the time of diagnosis using a 523-gene DNA/RNA hybrid comprehensive genomic profiling (CGP) panel and extensive clinical decision support tools. To comprehensively characterize the clinical impact of this protocol, we developed a novel natural language processing (NLP)-based approach to extract clinical features from physician chart notes. We assessed test actionability rates, therapy choice, and outcomes across a set of 3,216 patients with advanced cancer.

RESULTS

We observed 49% of patients had at least one actionable genomic biomarker-driven-approved and/or guideline-recommended targeted or immunotherapy (IO) and 53% of patients would have been eligible for a precision therapy clinical trial from three large basket trials. When assessing CGP versus an in silico 50-gene panel, 67% of tumors compared with 33% harbored actionable alterations including clinical trials. Among patients with 6 months or more of follow-up, over 52% received a targeted therapy (TT) or IO, versus 32% who received conventional chemotherapy alone. Furthermore, patients receiving TT had significantly improved overall survival compared with patients receiving chemotherapy alone (P < .001).

CONCLUSION

Overall, these data represent a major shift in standard clinical practice toward molecularly guided treatments (targeted and immunotherapies) over conventional systemic chemotherapy. As guidelines continue to evolve and more precision therapeutics gain approval, we expect this gap to continue to widen.

Unraveling the immune landscape of lung adenocarcinoma: insights for tailoring therapeutic approaches

Lung adenocarcinoma (LUAD), a prevalent type of non-small cell lung cancer (NSCLC), was known for its diversity and intricate tumor microenvironment (TME). Comprehending the interaction among human immune-related genes (IRGs) and the TME is vital in the creation of accurate predictive models and specific treatments. We created a risk score based on IRGs and designed a nomogram to predict the prognosis of LUAD accurately. This involved a thorough examination of TME and the infiltration of immune cells in both high-risk and low-risk LUAD groups. Furthermore, the examination of the association between characteristic genes (BIRC5 and BMP5) and immune cells, along with immune checkpoints in the TME, was also conducted. The findings of our research unveiled unique immune profiles and interactions among individuals in the high- and low-risk categories, which contribute to variations in prognosis. LUAD demonstrated significant associations between BIRC5, BMP5, immune cells, and checkpoints, suggesting their involvement in disease advancement and resistance to medication. Furthermore, by correlating our findings with a multidrug database, we identified specific LUAD patient subsets that might benefit from tailored treatments. Our study establishes a groundbreaking prognostic model for LUAD, which not only underscores the importance of the immune context in LUAD but also paves the way for advancing precision medicine strategies in this complex malignancy.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

Introduction

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

BACKGROUND

Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.

METHODS

This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.

RESULTS

Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.

CONCLUSIONS

ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma

BACKGROUND

Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures.

METHODS

In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells.

RESULTS

Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients.

CONCLUSIONS

ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

Copper-binding protein modelling by single-cell transcriptome and Bulk transcriptome to predict overall survival in lung adenocarcinoma patients

Background: Copper and copper-binding proteins are key components of tumour progression as they play an important role in tumour invasion and migration, and abnormal accumulation of copper (Cu) may be intimately linked to with lung adenocarcinoma (LUAD). Methods: Data on lung adenocarcinoma were sourced from the Cancer Genome Atlas (TCGA) database and the National Centre for Biotechnology Information (GEO). 10x scRNA sequencing, which is from Bischoff P et al, was used for down-sequencing clustering and subgroup identification using TSNE. The genes for Copper-binding proteins (CBP) were acquired from the MSigDB database. LASSO-Cox analysis was subsequently used to construct a model for copper-binding proteins (CBPRS), which was then compared to lung adenocarcinoma models developed by others. External validation was carried out in the GSE31210 and GSE50081 cohorts. The effectiveness of immunotherapy was evaluated using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and the immune microenvironment between different risk groups were investigated. The CBPRS's key regulatory genes were screened using ROC diagnostic and KM survival curves. The differential expression of these genes was then verified by RT-qPCR. Results: The six CBP genes were identified as highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. The CBPRS demonstrated superior predictive ability compared to 11 previously published models. We constructed a column-line graph that includes CBPRS and clinical characteristics, which exhibits high predictive performance. Additionally, we observed significant differences in biological functions, mutational landscapes, and immune cell infiltration in the tumour microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both the high- and low-risk groups. These results suggest that the model has good predictive efficacy. Conclusions: The CBP model demonstrated good predictive performance, revealing characteristics of the tumour microenvironment. This provides a new method for assessing the efficacy of pre-immunisation and offers a potential strategy for future treatment of lung adenocarcinoma.

Macrophage-Related Gene Signatures for Predicting Prognosis and Immunotherapy of Lung Adenocarcinoma by Machine Learning and Bioinformatics

Background

In recent years, the immunotherapy of lung adenocarcinoma has developed rapidly, but the good therapeutic effect only exists in some patients, and most of the current predictors cannot predict it very well. Tumor-infiltrating macrophages have been reported to play a crucial role in lung adenocarcinoma (LUAD). Thus, we want to build novel molecular markers based on macrophages.

Methods

By non-negative matrix factorization (NMF) algorithm and Cox regression analysis, we constructed macrophage-related subtypes of LUAD patients and built a novel gene signature consisting of 12 differentially expressed genes between two subtypes. The gene signature was further validated in Gene-Expression Omnibus (GEO) datasets. Its predictive effect on prognosis and immunotherapy outcome was further evaluated with rounded analyses. We finally explore the role of TRIM28 in LUAD with a series of in vitro experiments.

Results

Our research indicated that a higher LMS score was significantly correlated with tumor staging, pathological grade, tumor node metastasis stage, and survival. LMS was identified as an independent risk factor for OS in LUAD patients and verified in GEO datasets. Clinical response to immunotherapy was better in patients with low LMS score compared to those with high LMS score. TRIM28, a key gene in the gene signature, was shown to promote the proliferation, invasion and migration of LUAD cell.

Conclusion

Our study highlights the significant role of gene signature in predicting the prognosis and immunotherapy efficacy of LUAD patients, and identifies TRIM28 as a potential biomarker for the treatment of LUAD.

A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in lung adenocarcinoma

BACKGROUND

One of the most prevalent malignancies in the world is lung adenocarcinoma (LUAD), with a large number of people dying from lung cancer each year. Anoikis has a crucial function in tumor metastasis, promoting cancer cell shedding and survival from the primary tumor site. However, the role of anoikis in LUAD is still unclear.

METHODS

The GeneCard database (https://www.genecards.org/) was utilized to obtain anoikis-related genes with correlation greater than 0.4. Differential analysis was employed to acquire differential genes. Univariate, multifactorial Cox analyses and the least absolute shrinkage and selection operator were then utilized to capture genes connected to overall survival time. These genes were used to build prognostic models. The predictive model was analyzed and visualized. Survival analysis was conducted on the model and risk scores were calculated. The TCGA samples were split into groups of low and high risk depending on risk scores. A Gene Expression Omnibus database sample was used for external verification. Immunization estimates were performed using ESTIMATE, CiberSort and single sample gene set enrichment analysis. The connection between the prognostic gene model and immune cells was analyzed. Drug susceptibility prediction analysis was performed. The clinical information for samples was extracted and analyzed.

RESULTS

We selected six genes related to anoikis in LUAD to construct a prognosis model (CDC25C, ITPRIP, SLCO1B3, CDX2, CSPG4 and PIK3CG). Compared with cases of high-risk scores, the overall survival of those with low risk was significantly elevated based on Kaplan-Meier survival analysis. Immune function analysis exhibited that different risk groups had different immune states. The results of ESTIMATE, CiberSort and single sample gene set enrichment analysis showed great gaps in immunization between patients in the two groups. The normogram of the risk score and the LUAD clinicopathological features was constructed. Principal component analysis showed that this model could effectively distinguish the two groups of LUAD patients.

CONCLUSIONS

We integrated multiple anoikis-related genes to build a prognostic model. This investigation demonstrates that anoikis-related genes can be used as a stratification element for fine therapy of individuals with LUAD.

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma

BACKGROUND

Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown.

METHODS

In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes.

RESULTS

Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD.

CONCLUSIONS

An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

TAGAP expression influences CD4+ T cell differentiation, immune infiltration, and cytotoxicity in LUAD through the STAT pathway: implications for immunotherapy

Background

T-cell Activation GTPase Activating Protein (TAGAP) plays a role in immune cell regulation. This study aimed to investigate TAGAP's expression and its potential impact on CD4+ T cell function and prognosis in lung adenocarcinoma (LUAD).

Methods

We analyzed TAGAP expression and its correlation with immune infiltration and clinical data in LUAD patients using multiple datasets, including The Cancer Genome Atlas (TCGA-LUAD), Gene Expression Omnibus (GEO), and scRNA-seq datasets. In vitro and in vivo experiments were conducted to explore the role of TAGAP in CD4+ T cell function, chemotaxis, and cytotoxicity.

Results

TAGAP expression was significantly lower in LUAD tissues compared to normal tissues, and high TAGAP expression correlated with better prognosis in LUAD patients. TAGAP was positively correlated with immune/stromal/ESTIMATE scores and immune cell infiltration in LUAD. Single-cell RNA sequencing revealed that TAGAP was primarily distributed in CD4+/CD8+ T cells. In vitro experiments showed that TAGAP overexpression enhanced CD4+ T cell cytotoxicity, proliferation, and chemotaxis. Gene Set Enrichment Analysis (GSEA) indicated that TAGAP was enriched in the JAK-STAT signaling pathway. In vivo experiments in a xenograft tumor model demonstrated that TAGAP overexpression suppressed tumor growth and promoted CD4+ T cell cytotoxicity.

Conclusions

TAGAP influences CD4+ T cell differentiation and function in LUAD through the STAT pathway, promoting immune infiltration and cytotoxicity. This study provides a scientific basis for developing novel LUAD immunotherapy strategies and exploring new therapeutic targets.

The GENIE BPC NSCLC Cohort: A Real-World Repository Integrating Standardized Clinical and Genomic Data for 1,846 Patients with Non-Small Cell Lung Cancer

PURPOSE

We describe the clinical and genomic landscape of the non-small cell lung cancer (NSCLC) cohort of the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC).

EXPERIMENTAL DESIGN

A total of 1,846 patients with NSCLC whose tumors were sequenced from 2014 to 2018 at four institutions participating in AACR GENIE were randomly chosen for curation using the PRISSMM data model. Progression-free survival (PFS) and overall survival (OS) were estimated for patients treated with standard therapies.

RESULTS

In this cohort, 44% of tumors harbored a targetable oncogenic alteration, with EGFR (20%), KRAS G12C (13%), and oncogenic fusions (ALK, RET, and ROS1; 5%) as the most frequent. Median OS (mOS) on first-line platinum-based therapy without immunotherapy was 17.4 months [95% confidence interval (CI), 14.9-19.5 months]. For second-line therapies, mOS was 9.2 months (95% CI, 7.5-11.3 months) for immune checkpoint inhibitors (ICI) and 6.4 months (95% CI, 5.1-8.1 months) for docetaxel ± ramucirumab. In a subset of patients treated with ICI in the second-line or later setting, median RECIST PFS (2.5 months; 95% CI, 2.2-2.8) and median real-world PFS based on imaging reports (2.2 months; 95% CI, 1.7-2.6) were similar. In exploratory analysis of the impact of tumor mutational burden (TMB) on survival on ICI treatment in the second-line or higher setting, TMB z-score harmonized across gene panels was associated with improved OS (univariable HR, 0.85; P = 0.03; n = 247 patients).

CONCLUSIONS

The GENIE BPC cohort provides comprehensive clinicogenomic data for patients with NSCLC, which can improve understanding of real-world patient outcomes.

Investigating the role of Kinesin family in lung adenocarcinoma via integrated bioinformatics approach

Lung cancer is the leading cause of mortality from cancer worldwide. Lung adenocarcinoma (LUAD) is a type of non-small cell lung cancer (NSCLC) with highest prevalence. Kinesins a class of motor proteins are shown to be involved in carcinogenesis. We conducted expression, stage plot and survival analyses on kinesin superfamily (KIF) and scrutinized the key prognostic kinesins. Genomic alterations of these kinesins were studied thereafter via cBioPortal. A protein-protein interaction network (PPIN) of selected kinesins and 50 closest altering genes was constructed followed by gene ontology (GO) term and pathway enrichment analyses. Multivariate survival analysis based on CpG methylation of selected kinesins was performed. Lastly, we conducted tumor immune infiltration analysis. Our results found KIF11/15/18B/20A/2C/4A/C1 to be significantly upregulated and correlated with poor survival in LUAD patients. These genes also showed to be highly associated with cell cycle. Out of our seven selected kinesins, KIFC1 showed the highest genomic alteration with highest number of CpG methylation. Also, CpG island (CGI) cg24827036 was discovered to be linked to LUAD prognosis. Therefore, we deduced that reducing the expression of KIFC1 could be a feasible treatment strategy and that it can be a wonderful individual prognostic biomarker. CGI cg24827036 can also be used as a therapy site in addition to being a great prognostic biomarker.

Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in lung adenocarcinoma

Copper-dependent cell death, called cuproptosis, is connected to tumor development, prognosis, and the immune response. Nevertheless, the function of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) remains unknown. This work used R software packages to classify the raw data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases of LUAD patients. Afterward, the connections of the various subgroups, clinical pathological traits, and immune infiltration (IMIF) features with the TME mutation status were explored. Ultimately, a nomogram and calibration curve were developed, aiming at enhancing the clinical application of CRG scores and estimating the survival probability of patients. Moreover, the relationships between cuproptosis and the molecular traits, immune cell infiltration of tumor tissue, prognosis, and clinical treatment of patients were investigated in this work. Subsequently, the CRG score was established to predict overall survival (OS), and its credible predictive ability in LUAD patients was identified. Afterward, a highly credible nomogram was created to contribute to the clinical viability of the CRG score. Furthermore, as demonstrated, gene signatures could be applied in assessing tumor immune cell infiltration, clinical traits, and prognosis. In addition, high tumor mutation burden, immunological activity, and significant survival probability were characterized by low CRG scores, and high CRG scores were related to immunosuppression and stromal pathway activation. The current work also discovered a predictive CRG-related signature for LUAD patients, probably contributing to TME trait clarification and more potent immunotherapy strategy exploration.

A Database of Lung Cancer-Related Genes for the Identification of Subtype-Specific Prognostic Biomarkers

The molecular subtype is critical for accurate treatment and follow-up in patients with lung cancer; however, information regarding subtype-associated genes is dispersed among thousands of published studies. Systematic curation and cross-validation of the scientific literature would provide a solid foundation for comparative genetic studies of the major molecular subtypes of lung cancer. Here, we constructed a literature-based lung cancer gene database (LCGene). In the current release, we collected and curated 2507 unique human genes, including 2267 protein-coding and 240 non-coding genes from comprehensive manual examination of 10,960 PubMed article abstracts. Extensive annotations were added to aid identification of differentially expressed genes, potential gene editing sites, and non-coding gene regulation. For instance, we prepared 607 curated genes with CRISPR knockout information in 43 lung cancer cell lines. Further comparison of these implicated genes among different subtypes identified several subtype-specific genes with high mutational frequencies. Common tumor suppressors and oncogenes shared by lung adenocarcinoma and lung squamous cell carcinoma, for example, exhibited different mutational frequencies and prognostic features, suggesting the presence of subtype-specific biomarkers. Our retrospective analysis revealed 43 small cell lung cancer-specific genes. Moreover, 52 tumor suppressors and oncogenes shared by lung adenocarcinoma and squamous cell carcinoma confirmed the different molecular mechanisms of these two cancer subtypes. The subtype-based genetic differences, when combined, may provide insight into subtype-specific biomarkers for genetic testing.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

RNA m6A methyltransferase METTL14 promotes the procession of non-small cell lung cancer by targeted CSF1R

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the most malignant cancer types, characterized by a poor prognosis. N6-methyladenosine (m6A) is a prevalent internal modification of mRNA. METTL14, an RNA methyltransferase that mediates m6A modification, is implicated in mRNA biogenesis. However, the biomechanism of METTL14 in NSCLC is not very clear.

METHODS

Here, immunohistochemical (IHC) assay was employed to detect METTL14 in NSCLC tissues. The biological functions of METTL14 were demonstrated using cell transfection, cell proliferation assay, cell clone formation assay, cell cycle analysis, cell death analysis, transwell and wound healing assays. Transcriptome and methylated RNA immunoprecipitation (MERIP)-sequencing were used to explore the pathways and potential mechanism of METTL14 in NSCLC. RNA sequencing, METTL14 rip-sequencing, and METTL14 merip-sequencing were conducted to identify the potential targets of METTL14.

RESULTS

METTL14 was significantly correlated with clinical pathological parameters of differentiation and M stage. Additionally, METTL14 promotes cell proliferation, induces cell death, and enhances cell migration and invasion in vitro. Transcriptome and MeRIP-sequencing reveal oncogenic mechanism of METTL14. RIP-sequencing highlights CSF1R and AKR1C1 as targets of METTL14. After validation with TCGA dataset, colony stimulating factor 1 receptor (CSF1R) showed significant positive coefficient with METTL14, and was presumed to be one target of METTl14 in lung cancer and verified by the cellular experiments.

CONCLUSION

In conclusion, our results revealed the clinical significance of m6A RNA modification atlas, the function, and molecular targets CSF1R of METTL14 in NSCLC cell lines. The RNA m6A methyltransferase METTL14 promotes the progression of NSCLC by targeted CSF1R.

Tumor microenvironment-associated lactate metabolism regulates the prognosis and precise checkpoint immunotherapy outcomes of patients with lung adenocarcinoma

BACKGROUND

Despite the wide clinical application of checkpoint inhibitor immunotherapy in lung adenocarcinoma, its limited benefit to patients remains puzzling to researchers. One of the mechanisms of immunotherapy resistance may be the dysregulation of lactate metabolism in the immunosuppressive tumor microenvironment (TME), which can inhibit dendritic cell maturation and prevent T-cell invasion into tumors. However, the key genes related to lactate metabolism and their influence on the immunotherapeutic effects in lung adenocarcinoma have not yet been investigated in depth.

METHODS

In this study, we first surveyed the dysregulated expression of genes related to lactate metabolism in lung adenocarcinoma and then characterized their biological functions. Using machine learning methods, we constructed a lactate-associated gene signature in The Cancer Genome Atlas cohort and validated its effectiveness in predicting the prognosis and immunotherapy outcomes of patients in the Gene Expression Omnibus cohorts.

RESULTS

A 7-gene signature based on the metabolomics related to lactate metabolism was found to be associated with multiple important clinical features of cancer and was an independent prognostic factor.

CONCLUSIONS

These results suggest that rather than being simply a metabolic byproduct of glycolysis, lactate in the TME can affect immunotherapy outcomes. Therefore, the mechanism underlying this effect of lactate is worthy of further study.

A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a remarkably heterogeneous and aggressive disease with dismal prognosis of patients. The identification of promising prognostic biomarkers might enable effective diagnosis and treatment of LUAD. Aberrant activation of epithelial-mesenchymal transition (EMT) is required for LUAD initiation, progression and metastasis. With the purpose of identifying a robust EMT-related gene signature (E-signature) to monitor the survival outcomes of LUAD patients. In The Cancer Genome Atlas (TCGA) database, least absolute shrinkage and selection operator (LASSO) analysis and cox regression analysis were conducted to acquire prognostic and EMT-related genes. A 4 EMT-related and prognostic gene signature, comprising dickkopf-like protein 1 (DKK1), lysyl oxidase-like 2 (LOXL2), matrix Gla protein (MGP) and slit guidance ligand 3 (SLIT3), was identified. By the usage of datum derived from TCGA database and Western blotting analysis, compared with adjacent tissue samples, DKK1 and LOXL2 protein expression in LUAD tissue samples were significantly higher, whereas the trend of MGP and SLIT3 expression were opposite. Concurrent with upregulation of epithelial markers and downregulation of mesenchymal markers, knockdown of DKK1 and LOXL2 impeded the migration and invasion of LUAD cells. Simultaneously, MGP and SLIT3 silencing promoted metastasis and induce EMT of LUAD cells. In the TCGA-LUAD set, receiver operating characteristic (ROC) analysis indicated that our risk model based on the identified E-signature was superior to those reported in literatures. Additionally, the E-signature carried robust prognostic significance. The validity of prediction in the E-signature was validated by the three independent datasets obtained from Gene Expression Omnibus (GEO) database. The probabilistic nomogram including the E-signature, pathological T stage and N stage was constructed and the nomogram demonstrated satisfactory discrimination and calibration. In LUAD patients, the E-signature risk score was associated with T stage, N stage, M stage and TNM stage. GSEA (gene set enrichment analysis) analysis indicated that the E-signature might be linked to the pathways including GLYCOLYSIS, MYC TARGETS, DNA REPAIR and so on. In conclusion, our study explored an innovative EMT based prognostic signature that might serve as a potential target for personalized and precision medicine.

Heterogeneous expression of ACE2, TMPRSS2, and FURIN at single-cell resolution in advanced non-small cell lung cancer

Purpose

Considering the high susceptibility of patients with advanced non-small cell lung cancer (NSCLC) to COVID-19, we explored the susceptible cell types and potential routes of SARS-CoV-2 infection in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by analyzing the expression patterns of the entry receptor angiotensin converting enzyme 2 (ACE2) and the spike (S) protein priming proteases transmembrane serine protease 2 (TMPRSS2) and FURIN.

Methods

Single-cell transcriptomic analysis of 14 LUSC and 12 LUAD samples was utilized to exhibit the heterogeneous expression of ACE2, TMPRSS2 and FURIN across different cell subsets and individuals.

Results

12 cell types and 33 cell clusters were identified from 26 cancer samples. ACE2, TMPRSS2 and FURIN were heterogeneously expressed across different patients. Among all cell types, ACE2, TMPRSS2 and FURIN were predominately expressed in cancer cells and alveolar cells, and lowly uncovered in other cells. Compared to LUSC, the protein priming proteases (TMPRSS2 and FURIN) were highly found in LUAD samples. However, ACE2 was not differentially expressed in cancer cells between the two cancer types. Moreover, ACE2, TMPRSS2, and FURIN expressions were not higher in any cell type of smokers than non-smokers.

Conclusion

Our research first revealed the heterogeneous expression of ACE2, TMPRSS2, and FURIN in different cell subsets of NSCLC and also across different individuals. These results provide insight into the specific cells targeted by SARS-CoV-2 (i.e., cancer cells and alveolar cells) in patients with advanced NSCLC, and indicate that smoking may be not an independent risk factor for NSCLC combined with COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04253-1.

Identification and Validation of a Novel Signature Based on NK Cell Marker Genes to Predict Prognosis and Immunotherapy Response in Lung Adenocarcinoma by Integrated Analysis of Single-Cell and Bulk RNA-Sequencing

Natural killer (NK) cells, the effectors of the innate immune system, have a remarkable influence on cancer prognosis and immunotherapy. In this study, a total of 1,816 samples from nine independent cohorts in public datasets were enrolled. We first conducted a comprehensive analysis of single-cell RNA-sequencing data of lung adenocarcinoma (LUAD) from the Gene Expression Omnibus (GEO) database and determined 189 NK cell marker genes. Subsequently, we developed a seven-gene prognostic signature based on NK cell marker genes in the TCGA LUAD cohort, which stratified patients into high-risk and low-risk groups. The predictive power of the signature was well verified in different clinical subgroups and GEO cohorts. With a multivariate analysis, the signature was identified as an independent prognostic factor. Low-risk patients had higher immune cell infiltration states, especially CD8⁺ T cells and follicular helper T cells. There existed a negative association between inflammatory activities and risk score, and the richness and diversity of the T-cell receptor (TCR) repertoire was higher in the low-risk groups. Importantly, analysis of an independent immunotherapy cohort (IMvigor210) revealed that low-risk patients had better immunotherapy responses and prognosis than high-risk patients. Collectively, our study developed a novel signature based on NK cell marker genes, which had a potent capability to predict the prognosis and immunotherapy response of LUAD patients.

The Polymorphism and Expression of EGFL7 and miR-126 Are Associated With NSCLC Susceptibility

Previous investigations have reported that microRNA-126 (miR-126) and its host gene, epidermal growth factor-like domain-containing protein 7 (EGFL7) are involved in lung cancer progression, suggesting EGFL7 and miR-126 play a joint role in lung cancer development. In this study, we analyzed the methylation-associated regulation of EGFL7 and miR-126 in non-small cell lung cancer (NSCLC) and further investigated the association between EGFL7/miR-126 polymorphisms and NSCLC susceptibility in the Han Chinese population. Based on our data, relative to those in adjacent normal tissue, both EGFL7 expression and miR-126 expression were decreased significantly in lung cancer tissue (P = 3x10^-4 and P < 1x10^-4), and the expression of EGFL7 mRNA and miR-126 was significantly correlated in both NSCLC tissue n = 46, r = 0.43, P = 0.003 and adjacent normal tissue n = 46, r = 0.37, P = 0.011. Differential methylation analysis indicated that methylation levels of multiple CG loci in EGFL7 were significantly higher in the lung cancer samples than in the normal samples (P < 0.01). Moreover, EGFL7 mRNA and miR-126 were significantly upregulated after treatment with the DNA demethylating agent 5-aza-2′-deoxycytidine (5-Aza-CdR) in lung cancer cell lines. In addition, the A allele of rs2297538 was significantly associated with a decreased NSCLC risk (OR = 0.68, 95% CI: 0.52~0.88), and the expression of EGFL7 and miR-126 was significantly lower in rs2297538 homozygous G/G tumor tissue than in A/G+A/A tumor tissue (P = 0.01 and P = 0.002). Our findings suggest that the expression of EGFL7 and miR-126 in NSCLC can be concomitantly downregulated through methylation and the EGFL7/miR-126 polymorphism rs2297538 is correlated with NSCLC risk. Together, these results provide new insights into the pathogenesis of NSCLC.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on B cell marker genes to predict prognosis and immunotherapy response in lung adenocarcinoma

As an essential component of the tumor microenvironment, B cells exist in all stages of tumor and exert important roles in anti-tumor immunity and shaping tumor development. We aimed to explore the expression profile of B cell marker genes and construct a prognostic signature based on these genes in Lung adenocarcinoma (LUAD). A total of 1268 LUAD patients from different cohorts were enrolled in this study. We performed an analysis of single-cell RNA-sequencing (scRNA-seq) data from Gene expression omnibus (GEO) database to identify B cell marker genes in LUAD. TCGA database was used to construct signature, and six cohorts from GEO database were used for validation. We also investigated the association between this signature and immunotherapy response. Based on 258 B cell marker genes identified by scRNA-seq analysis, a nine-gene signature was constructed for prognostic prediction in TCGA dataset, which classified patients into high-risk and low-risk groups according to overall survival. The multivariate analysis demonstrated that the signature was an independent prognostic factor. The signature's predictive power was verified in other six independent cohorts and different clinical subgroups. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. More importantly, risk scores of the signature were closely correlated with PD-L1, tumor mutation burden, neoantigens, and tumor immune dysfunction and exclusion score. Our study proposed a novel prognostic signature based on B cell marker genes for LUAD patients. The signature could effectively indicate LUAD patients' survival and serve as a predictor for immunotherapy.

T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

MOTIVATION

A major task in genetic studies is to identify genes related to human diseases and traits to understand functional characteristics of genetic mutations and enhance patient diagnosis. Compared with marginal analyses of individual genes, identification of gene pathways, i.e. a set of genes with known interactions that collectively contribute to specific biological functions, can provide more biologically meaningful results. Such gene pathway analysis can be formulated into a high-dimensional two-sample testing problem. Given the typically limited sample size of gene expression datasets, most existing two-sample tests tend to have compromised powers because they ignore or only inefficiently incorporate the auxiliary pathway information on gene interactions.

RESULTS

We propose T2-DAG, a Hotelling's T2-type test for detecting differentially expressed gene pathways, which efficiently leverages the auxiliary pathway information on gene interactions from existing pathway databases through a linear structural equation model. We further establish its asymptotic distribution under pertinent assumptions. Simulation studies under various scenarios show that T2-DAG outperforms several representative existing methods with well-controlled type-I error rates and substantially improved powers, even with incomplete or inaccurate pathway information or unadjusted confounding effects. We also illustrate the performance of T2-DAG in an application to detect differentially expressed KEGG pathways between different stages of lung cancer.

AVAILABILITY AND IMPLEMENTATION

The R (R Development Core Team, 2021) package T2DAG which implements the proposed T2-DAG test is available on Github at https://github.com/Jin93/T2DAG.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of Genome-Wide Alternative Splicing Profiling and Development of Potential Drugs in Lung Adenocarcinoma

Alternative splicing (AS) is significantly related to tumor development as well as a patient’s clinical characteristics. This study was designed to systematically analyze the survival-associated AS signatures in Lung adenocarcinoma (LUAD). Among 30,735 AS events in 9,635 genes, we found that there were 1,429 AS in 1,125 genes which were conspicuously related to the overall survival of LUAD patients. Then, according to the seven types of AS events, we established AS signatures and constructed a new combined prognostic model. The Kaplan-Meier curve results showed that seven types of AS signatures and the combined prognostic model could divide patients into distinct prognoses. The ROC curve shows that all eight AS signatures had powerful predictive properties with different AUCs ranging from 0.708 to 0.849. Additionally, the elevated risk scores were positively related to higher TNM stage and metastasis. Interestingly, AS events and splicing factors (SFs) network shed light on a meaningful connection between prognostic AS genes and corresponding SFs. Moreover, we found that the combined prognostic model signature has a higher predictive ability than the mRNA signature. Furthermore, tumors at high risk might evade immune recognition by decreasing the expression of antigen presentation genes. Finally, we predicted the three most significant small molecule drugs to inhibit LUAD. Among them, NVP-AUY922 had the lowest IC50 value and might become a potential drug to prolong a patient’s survival. In conclusion, our study established a potential prognostic signature for LUAD patients, revealed a splicing network between AS and SFs and possible immune escape mechanism, and provided several small-molecule drugs to inhibit tumorigenesis.

Knockdown of lncRNA NUTM2A-AS1 inhibits lung adenocarcinoma cell viability by regulating the miR-590-5p/METTL3 axis

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Long non-coding RNA (lncRNA) NUT family member 2A antisense RNA 1 (NUTM2A-AS1) is dysregulated in LUAD; however, its role in this disease remains unclear. The present study aimed to identify the underlying molecular mechanism of the effect of lncRNA NUTM2A-AS1 in LUAD by exploring whether lncRNA NUTM2A-AS1 could affect LUAD cell proliferation and apoptosis through the microRNA (miR)-590-5p/methyltransferase 3, N6-adenosine-methyltransferase complex catalytic subunit (METTL3) axis. miR-590-5p was predicted and verified as the direct target of NUTM2A-AS1 using bioinformatics analysis and a dual luciferase reporter assay. The expression levels of NUTM2A-AS1 and miR-590-5p in lung cancer cells, and the effects of NUTM2A-AS1 on cell viability and apoptosis were determined using MTT assays and flow cytometry, respectively. Reverse transcription-quantitative PCR analysis revealed that the expression levels of NUTM2A-AS1 were significantly upregulated, while those of miR-590-5p were significantly downregulated, in lung cancer cells compared with the control epithelial cells. NUTM2A-AS1 knockdown inhibited NCI-H23 cell viability and induced apoptosis by upregulating miR-590-5p expression. Moreover, the function and regulatory mechanism of miR-590-5p in LUAD were also investigated. It was determined that miR-590-5p could interact with METTL3, and further analysis of the expression levels of METTL3 in lung cancer cells demonstrated that METTL3 was significantly upregulated in NCI-H23 and A549 cells compared with the control cells. In addition, miR-590-5p inhibited NCI-H23 cell viability and induced apoptosis by downregulating METTL3 expression. In conclusion, the findings of the present study suggested that NUTM2A-AS1 knockdown may inhibit LUAD progression by regulating the miR-590-5p/METTL3 axis. These results may provide insight into the mechanisms underlying the tumorigenesis of LUAD and offer a new treatment strategy for the disease.

Phase 1, pharmacogenomic, dose-expansion study of pegargiminase plus pemetrexed and cisplatin in patients with ASS1-deficient non-squamous non-small cell lung cancer

Introduction

We evaluated the arginine‐depleting enzyme pegargiminase (ADI‐PEG20; ADI) with pemetrexed (Pem) and cisplatin (Cis) (ADIPemCis) in ASS1‐deficient non‐squamous non‐small cell lung cancer (NSCLC) via a phase 1 dose‐expansion trial with exploratory biomarker analysis.

Methods

Sixty‐seven chemonaïve patients with advanced non‐squamous NSCLC were screened, enrolling 21 ASS1‐deficient subjects from March 2015 to July 2017 onto weekly pegargiminase (36 mg/m²) with Pem (500 mg/m²) and Cis (75 mg/m²), every 3 weeks (four cycles maximum), with maintenance Pem or pegargiminase. Safety, pharmacodynamics, immunogenicity, and efficacy were determined; molecular biomarkers were annotated by next‐generation sequencing and PD‐L1 immunohistochemistry.

Results

ADIPemCis was well‐tolerated. Plasma arginine and citrulline were differentially modulated; pegargiminase antibodies plateaued by week 10. The disease control rate was 85.7% (n = 18/21; 95% CI 63.7%–97%), with a partial response rate of 47.6% (n = 10/21; 95% CI 25.7%–70.2%). The median progression‐free and overall survivals were 4.2 (95% CI 2.9–4.8) and 7.2 (95% CI 5.1–18.4) months, respectively. Two PD‐L1‐expressing (≥1%) patients are alive following subsequent pembrolizumab immunotherapy (9.5%). Tumoral ASS1 deficiency enriched for p53 (64.7%) mutations, and numerically worse median overall survival as compared to ASS1‐proficient disease (10.2 months; n = 29). There was no apparent increase in KRAS mutations (35.3%) and PD‐L1 (<1%) expression (55.6%). Re‐expression of tumoral ASS1 was detected in one patient at progression (n = 1/3).

Conclusions

ADIPemCis was safe and highly active in patients with ASS1‐deficient non‐squamous NSCLC, however, survival was poor overall. ASS1 loss was co‐associated with p53 mutations. Therapies incorporating pegargiminase merit further evaluation in ASS1‐deficient and treatment‐refractory NSCLC.

The association of TAP polymorphisms with non-small-cell lung cancer in the Han Chinese population

The host immune system plays a crucial role in multiple types of cancer, including non-small-cell lung cancer (NSCLC). Transporter associated with antigen processing (TAP) protein heterodimer complexes might promote intracellular antigen peptide binding with class I major histocompatibility complex (MHC-I) molecules, and in recent years, TAP1 and TAP2 have been reported to be associated with multiple cancer risks. In the current study, we investigated the association of single-nucleotide polymorphisms (SNPs) in TAP1 and TAP2 with NSCLC in a Han Chinese population. Six and seven TAP1 and TAP2 SNPs, respectively, were genotyped and analysed in healthy controls and NSCLC patients. Based on our data, none of the six SNPs in TAP1 is associated with NSCLC risk (P > 0.0038). However, rs2228396 alleles in TAP2 were significantly different between NSCLC patients and healthy controls, and the A allele might be associated with an increased risk of this cancer (P = 0.001, OR = 1.65, 95%CI: 1.23 ∼ 2.21). Moreover, the genotype frequencies of rs2228396 were significantly different between patients and healthy controls (P = 7 × 10^-4). Additionally, TAP2 rs241441 alleles exhibited a trend of difference between NSCLC patients and healthy controls, with the C allele possibly being associated with increased risk of NSCLC (P = 0.013; OR = 1.30, 95%CI: 1.06 ∼ 1.60). Moreover, the genotypes of rs241441 in TAP2 showed a significant difference between NSCLC patients and healthy controls (P = 1 × 10^-4). In haplotype analysis, the TAP2 SNP haplotype (CAC, TAP2*0102) was significantly associated with increased NSCLC risk in the Han Chinese population (P = 0.003; OR = 1.57, 95%CI: 1.17 ∼ 2.10). Our results indicate that TAP2 SNPs (rs2228396 and rs241441) have a potential role in NSCLC pathogenesis.

Identification of a 15 DNA Damage Repair-Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

BACKGROUND

Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment.

INTRODUCTION

To identify a reliable DDR-related gene signature as an independent predictor of LUAD.

METHODS

DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan-Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis.

RESULTS

Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 - 4, T2-T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients.

CONCLUSION

The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer

•

Poster presentation at the USCAP 108th Annual Meeting, March 16–21, 2019.

Background

We developed and validated a prognostic and predictive computational pathology risk score (CoRiS) using H&E stained tissue images from patients with early-stage non-small cell lung cancer (ES-NSCLC).

Methods

1330 patients with ES-NSCLC were acquired from 3 independent sources and divided into four cohorts D_1-4. D₁ comprised 100 surgery treated patients and was used to identify prognostic features via an elastic-net Cox model to predict overall and disease-free survival. CoRiS was constructed using the Cox model coefficients for the top features. The prognostic performance of CoRiS was evaluated on D₂ (N=331), D₃ (N=657) and D₄ (N=242). Patients from D₂ and D₃ which comprised surgery + chemotherapy were used to validate CoRiS as predictive of added benefit to adjuvant chemotherapy (ACT) by comparing survival between different CoRiS defined risk groups.

Findings

CoRiS was found to be prognostic on univariable analysis, D₂ (hazard ratio (HR) = 1.41, adjusted (adj.) P = .01) and D₃ (HR = 1.35, adj. P < .001). Multivariable analysis showed CoRiS was independently prognostic, D₂ (HR = 1.41, adj. P < .001) and D₃ (HR = 1.35, adj. P < .001), after adjusting for clinico-pathologic factors. CoRiS was also able to identify high-risk patients who derived survival benefit from ACT D₂ (HR = 0.42, adj. P = .006) and D₃ (HR = 0.46, adj. P = .08).

Interpretation

CoRiS is a tissue non-destructive, quantitative and low-cost tool that could potentially help guide management of ES-NSCLC patients.

Downregulation of m6A Reader YTHDC2 Promotes the Proliferation and Migration of Malignant Lung Cells via CYLD/NF-κB Pathway

Lung cancer is one of the most common types of carcinoma worldwide. Cigarette smoking is considered the leading cause of lung cancer. Aberrant expression of several YT521-B homology (YTH) family proteins has been reported to be closely associated with multiple cancer types. The present study aims to evaluate the function and regulatory mechanisms of the N6-methyladenosine (m6A) reader protein YTH domain containing 2 (YTHDC2) by in vitro, in vivo and bioinformatics analyses. The results revealed that YTHDC2 was reduced in lung cancer and cigarette smoke-exposed cells. Notably, bioinformatics and tissue arrays analysis demonstrated that decreased YTHDC2 was highly associated with smoking history, pathological stage, invasion depth, lymph node metastasis and poor outcomes. The in vivo and in vitro studies revealed that YTHDC2 overexpression inhibited the proliferation and migration of lung cancer cells as well as tumor growth in nude mice. Furthermore, YTHDC2 decreased expression was modulated by copy number deletion in lung cancer. Importantly, the cylindromatosis (CYLD)/NF-κB pathways were confirmed as the downstream signaling of YTHDC2, and this axis was mediated by m6A modification. The present results indicated that smoking-related downregulation of YTHDC2 was associated with enhanced proliferation and migration in lung cancer cells, and appeared to be regulated by DNA copy number variation. Importantly, YTHDC2 functions as a tumor suppressor through the CYLD/NF-κB signaling pathway, which is mediated by m6A modification.

SLC15A4 Serves as a Novel Prognostic Biomarker and Target for Lung Adenocarcinoma

BACKGROUND

SLC15A family members are known as electrogenic transporters that take up peptides into cells through the proton-motive force. Accumulating evidence indicates that aberrant expression of SLC15A family members may play crucial roles in tumorigenesis and tumor progression in various cancers, as they participate in tumor metabolism. However, the exact prognostic role of each member of the SLC15A family in human lung cancer has not yet been elucidated.

MATERIALS AND METHODS

We investigated the SLC15A family members in lung cancer through accumulated data from TCGA and other available online databases by integrated bioinformatics analysis to reveal the prognostic value, potential clinical application and underlying molecular mechanisms of SLC15A family members in lung cancer.

RESULTS

Although all family members exhibited an association with the clinical outcomes of patients with NSCLC, we found that none of them could be used for squamous cell carcinoma of the lung and that SLC15A2 and SLC15A4 could serve as biomarkers for lung adenocarcinoma. In addition, we further investigated SLC15A4-related genes and regulatory networks, revealing its core molecular pathways in lung adenocarcinoma. Moreover, the IHC staining pattern of SLC15A4 in lung adenocarcinoma may help clinicians predict clinical outcomes.

CONCLUSION

SLC15A4 could be used as a survival prediction biomarker for lung adenocarcinoma due to its potential role in cell division regulation. However, more studies including large patient cohorts are required to validate the clinical utility of SLC15A4 in lung adenocarcinoma.

hsa_circ_0008234 inhibits the progression of lung adenocarcinoma by sponging miR-574-5p

circRNAs are a novel type of noncoding RNA (ncRNA) that have been identified as an important regulator of gene expression and play a part in the progression of various diseases. However, the function of circ_0008234 in lung adenocarcinoma (LUAC) remains unknown. Through the GEO (Gene Expression Omnibus) database, circ_0008234 was first found to be downregulated in LUAC tissues. It could inhibit cell growth and accelerate apoptosis in vitro and in vivo. In terms of its possible mechanism, circ_0008234 mainly was present in the cytoplasm and competed with miR-574-5p to regulate RND3 (Rho family GTPase 3). Our results revealed that circ_0008234 inhibited the progression of LUAC through a competing endogenous RNA (ceRNA)-based mechanism and provided potential biomarkers and therapeutic targets for LUAC treatment.

Preoperative systemic immune-inflammation index predicts prognosis and guides clinical treatment in patients with non-small cell lung cancer

Objectives: The purpose of the present study was to evaluate the prognostic value of a systemic immune-inflammation index (SII) and the relationship between SII and the effectiveness of postoperative treatment in patients with non-small cell lung cancer (NSCLC).

Methods: A total of 538 patients diagnosed with NSCLC who had undergone curative surgery were retrospectively enrolled in the study. Clinicopathologic and laboratory variables were collected. SII was defined as neutrophil × platelet/lymphocyte counts. Both univariate and multivariate analyses were performed to analyze the prognostic value of these factors.

Results: The preoperative SII level was associated with sex, smoking history, histological type, lesion type, resection type, pathological stage, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), fibrinogen and bone metastasis (P<0.05). The univariate and multivariate analyses revealed that SII was an independent prognostic factor for disease-free survival (DFS, P=0.033) and overall survival (OS, P=0.020). Furthermore, the prognostic value of SII was also verified regardless of the histological type and pathological stage. The subgroup analysis demonstrated that patients with a high SII may benefit from adjuvant therapy (P=0.024 for DFS and P=0.012 for OS).

Conclusion: An increased preoperative SII may independently predict the poor DFS and OS in patients with resectable NSCLC. SII may help select NSCLC patients who might benefit from adjuvant chemotherapy.

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

Objective

To screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.

Methods

The GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.

Results

We identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.

Conclusion

This prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

The Outer Retinal Membrane Protein 1 Could Inhibit Lung Cancer Progression as a Tumor Suppressor

Some related reports indicate that the outer retinal membrane protein 1 (ROM1) functions importantly in the regulation of the biological process of tumor. Nevertheless, studies towards the role of ROM1 in lung cancer are few. Here, our data demonstrated that ROM1 displayed a relation with lung cancer tumorigenesis and development. In the Tumor Genome Atlas (TCGA) cohort, reduced ROM1 level was observed in lung cancer tissues, instead of normal tissues. After bioinformatics analysis, the data revealed that ROM1 level was associated with the tumor stage. Additional results indicated that highly expressed ROM1 exhibited a positive correlation with the overall survival rate, and ROM1 was probably a promising prognostic biomarker of lung cancer. Additionally, our results indicated that knocking out ROM1 could promote cell proliferation, migration, and invasion. Our data conclusively demonstrated that ROM1 modulated lung cancer tumorigenesis and development, as a prognosis and treatment biomarker.

Investigation on Potential Correlation Between Small Nuclear Ribonucleoprotein Polypeptide A and Lung Cancer

SNRPA (small nuclear ribonucleoprotein polypeptide A) gene is essential for the pre-mRNA splicing process. Using the available datasets of TCGA or GEO, we aimed at exploring the potential association between the SNRPA gene and lung cancer by several online tools (such as GEIPA2, MEXPRESS, Oncomine) and bioinformatics analysis software (R or GSEA). SNRPA was highly expressed in the tissues of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma tissue (LUSC), compared with control tissues. The high SNRPA expression was associated with a poor survival prognosis of LUAD cases, while the genetic alteration within SNRPA was linked to the overall survival prognosis of LUSC cases. There was a potential correlation between promoter methylation and the expression of SNRPA for LUAD. Compared with normal tissues, we observed a higher phosphorylation level at the S115 site of SNRPA protein (NP_004587.1) (p = 0.002) in the primary LUAD tissues. The potential ATR kinase of the S115 site was predicted. Besides, SNRPA expression in lung cancer was negatively correlated with the infiltration level of M2 macrophage but positively correlated with that of Follicular B helper T cells, in both LUAD and LUSC. The enrichment analysis of SNRPA-correlated genes showed that cell cycle and ubiquitin mechanism-related issues were mainly observed for LUAD; however, RNA splicing-related cellular issues were mainly for LUSC. In summary, the SNRPA gene was identified as a potential prognosis biomarker of lung cancer, especially lung adenocarcinoma, which sheds new light on the association between the spliceosomal complex component and tumorigenesis.

Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles

Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy. Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD. Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = -0.002* IDO1+0.031* CD274-0.069* CTLA4-0.517* CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts. Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.

Identification of an immune-related six-long noncoding RNA signature as a novel prognosis biomarker for adenocarcinoma of lung

BACKGROUND

Lung adenocarcinoma (LUAD) is a heterogeneous disease with high mortality. Close attention has been paid to immunotherapy in LUAD treatment. However, immunotherapy has produced different therapeutic effects because of immune heterogeneity. Long noncoding RNAs (lncRNAs) are survival prognostic indicators with functions in the immune process. The present study was designed to examine the predictive power of immune-related lncRNAs in LUAD prognosis and investigated potential molecular mechanisms.

METHODS

Transcriptome profiling and LUAD sample clinical information were retrieved from online database. The immune-related lncRNAs signature was identified by Cox regression. Survival analysis was used to verify the validity of the prognosis model. Then, possible biological functions were predicted and the abundance of infiltrating immune cells in LUAD samples were further analyzed.

RESULTS

An immune-associated lncRNAs signature was established by combining six lncRNAs. Patients with LUAD were stratified into high- and low-risk groups using the six lncRNAs signature. Patients in different risk levels had significantly different prognoses (P<0.001), and the immune-associated lncRNAs signature was identified as an independent prognostic factor for LUAD. The functions of the lncRNA signature were confirmed as ubiquitin mediated proteolysis and signal sequence binding. The lncRNA signature negatively correlates with B-cell immune infiltration.

CONCLUSION

A reliable immune-related lncRNAs prognosis model for LUAD was identified. lncRNAs played a vital role in the tumor immune process and were associated with the LUAD prognosis. Research of lncRNAs in B-cell immune infiltration could provide new insight into the immunotherapy of LUAD.

Towards image-based cancer cell lines authentication using deep neural networks

Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.

Interaction screening by Kendall's partial correlation for ultrahigh-dimensional data with survival trait

MOTIVATION

In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging.

RESULTS

In this article, we propose an inverse probability-of-censoring weighted Kendall's tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall's partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall's partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers.

AVAILABILITY AND IMPLEMENTATION

R-package 'IPCWK' is available to implement this method, together with a reference manual describing how to perform the 'IPCWK' package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of a novel gene expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on TCGA and GEO databases

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of clinical significance. This study aimed to identify novel molecular markers associated with LUAD prognosis.

MATERIALS AND METHODS

RNA sequencing data from the Cancer Genome Atlas (TCGA) database of LUAD tumors and paired normal tissues, and microarray data from the Gene Expression Omnibus (GEO) database were obtained. In the TCGA dataset, differentially expressed (DE) genes were identified by comparing gene expression between early-stage tumors and normal tissue, as well as between advanced-stage and early-stage tumors. A risk score was developed using a weighted linear combination of individual dysregulated protein-coding genes that was associated with overall survival (OS). The prognostic value of the risk score was evaluated using Kaplan-Meier and multivariate Cox analysis. The gene signature was further validated using independent datasets from GEO.

RESULTS

Among the 68 identified DE genes, 19 were individually associated with OS in univariate analyses. A risk score was constructed for each patient based on the coefficients in multivariate Cox model and normalized expression levels of these 19 genes. LUAD patients with a low risk score had a significantly better survival than those with a high risk score (log-rank P < 0.0001). After adjusting for age, sex, clinical stage, smoking history, and treatments, the patients with a low risk score had a 81 % decreased risk for death, compared to those with a high risk score (hazard ratio 0.19, 95 % confidence interval 0.097-0.36). The significant association of the risk score with OS in LUAD patients was further validated in three independent GEO datasets.

CONCLUSION

A novel 19-gene prognostic signature based on gene expression was identified in LUAD patients. The findings further improve the understanding of LUAD prognostication and have the potential to facilitate risk-stratified disease management.

Deregulated microRNAs Are Associated with Patient Survival and Predicted to Target Genes That Modulate Lung Cancer Signaling Pathways

(1) Background: Although the advances in diagnostic and treatment strategies, lung cancer remains the leading cause of cancer-related deaths, worldwide, with survival rates as low as 16% in developed countries. Low survival rates are mainly due to late diagnosis and the lack of effective treatment. Therefore, the identification of novel, clinically useful biomarkers is still needed for patients with advanced disease stage and poor survival. Micro(mi)RNAs are non-coding RNAs and potent regulators of gene expression with a possible role as diagnostic, prognostic and predictive biomarkers in cancer. (2) Methods: We applied global miRNA expression profiling analysis using TaqMan® arrays in paired tumor and normal lung tissues (n = 38) from treatment-naïve patients with lung adenocarcinoma (AD; n = 23) and lung squamous cell carcinoma (SCC; n = 15). miRNA target genes were validated using The Cancer Genome Atlas (TCGA) lung AD (n = 561) and lung SCC (n = 523) RNA-Seq datasets. (3) Results: We identified 33 significantly deregulated miRNAs (fold change, FC ≥ 2.0 and p < 0.05) in tumors relative to normal lung tissues, regardless of tumor histology. Enrichment analysis confirmed that genes targeted by the 33 miRNAs are aberrantly expressed in lung AD and SCC, and modulate known pathways in lung cancer. Additionally, high expression of miR-25-3p was significantly associated (p < 0.05) with poor patient survival, when considering both tumor histologies. (4) Conclusions: miR-25-3p may be a potential prognostic biomarker in non-small cell lung cancer. Genes targeted by miRNAs regulate EGFR and TGFβ signaling, among other known pathways relevant to lung tumorigenesis.