Intratumor Heterogeneity in Early Lung Adenocarcinoma

Designing SERS nanotags for profiling overexpressed surface markers on single cancer cells: A review

This review focuses on the chemical design and the use of Surface-Enhanced Raman Scattering (SERS)-active nanotags for measuring surface markers that can be overexpressed at the surface of single cancer cells. Indeed, providing analytical tools with true single-cell measurements capabilities is capital, especially since cancer research is increasingly leaning toward single-cell analysis, either to guide treatment decisions or to understand complex tumor behaviour including the single-cell heterogeneity and the appearance of treatment resistance. Over the past two decades, SERS nanotags have triggered significant interest in the scientific community owing their advantages over fluorescent tags, mainly because SERS nanotags resist photobleaching and exhibit sharper signal bands, which reduces possible spectral overlap and enables the discrimination between the SERS signals and the autofluorescence background from the sample itself. The extensive efforts invested in harnessing SERS nanotags for biomedical purposes, particularly in cancer research, highlight their potential as the next generation of optical labels for single-cell studies. The review unfolds in two main parts. The first part focuses on the structure of SERS nanotags, detailing their chemical composition and the role of each building block of the tags. The second part explores applications in measuring overexpressed surface markers on single-cells. The latter encompasses studies using single nanotags, multiplexed measurements, quantitative information extraction, monitoring treatment responses, and integrating phenotype measurements with SERS nanotags on single cells isolated from complex biological matrices. This comprehensive review anticipates SERS nanotags to persist as a pivotal technology in advancing single-cell analytical methods, particularly in the context of cancer research and personalized medicine.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

Background

Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets.

Methods

According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared.

Results

Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients.

Conclusions

Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

Targeting HER3 to overcome EGFR TKI resistance in NSCLC

Receptor tyrosine kinases (RTKs) play a crucial role in cellular signaling and oncogenic progression. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) have become the standard treatment for advanced non-small cell lung cancer (NSCLC) patients with EGFR-sensitizing mutations, but resistance frequently emerges between 10 to 14 months. A significant factor in this resistance is the role of human EGFR 3 (HER3), an EGFR family member. Despite its significance, effective targeting of HER3 is still developing. This review aims to bridge this gap by deeply examining HER3's pivotal contribution to EGFR TKI resistance and spotlighting emerging HER3-centered therapeutic avenues, including monoclonal antibodies (mAbs), TKIs, and antibody-drug conjugates (ADCs). Preliminary results indicate combining HER3-specific treatments with EGFR TKIs enhances antitumor effects, leading to an increased objective response rate (ORR) and prolonged overall survival (OS) in resistant cases. Embracing HER3-targeting therapies represents a transformative approach against EGFR TKI resistance and emphasizes the importance of further research to optimize patient stratification and understand resistance mechanisms.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention.

Significance

This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

Molecular characterization based on tumor microenvironment-related signatures for guiding immunotherapy and therapeutic resistance in lung adenocarcinoma

Background: Although the role of tumor microenvironment in lung adenocarcinoma (LUAD) has been explored in a number of studies, the value of TME-related signatures in immunotherapy has not been comprehensively characterized. Materials and Methods: Consensus clustering was conducted to characterize TME-based molecular subtypes using transcription data of LUAD samples. The biological pathways and immune microenvironment were assessed by CIBERSORT, ESTIMATE, and gene set enrichment analysis. A TME-related risk model was established through the algorithms of least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). Results: Four TME-based molecular subtypes including C1, C2, C3, and C4 were identified, and they showed distinct overall survival, genomic characteristics, DNA methylation pattern, immune microenvironment, and biological pathways. C1 had the worst prognosis and high tumor proliferation rate. C3 and C4 had higher enrichment of anti-tumor signatures compared to C1 and C2. C4 had evidently low enrichment of epithelial-mesenchymal transition (EMT) signature and tumor proliferation rate. C3 was predicted to be more sensitive to immunotherapy compared with other subtypes. C1 is more sensitive to chemotherapy drugs, including Docetaxel, Vinorelbine and Cisplatin, while C3 is more sensitive to Paclitaxel. A five-gene risk model was constructed, which showed a favorable performance in three independent datasets. Low-risk group showed a longer overall survival, more infiltrated immune cells, and higher response to immunotherapy than high-risk group. Conclusion: This study comprehensively characterized the molecular features of LUAD patients based on TME-related signatures, demonstrating the potential of TME-based signatures in exploring the mechanisms of LUAD development. The TME-related risk model was of clinical value to predict LUAD prognosis and guide immunotherapy.

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

BACKGROUND

Recent studies have emphasized the close relationship between macrophages and tumor immunity, and the prognosis of lung adenocarcinoma (LUAD) patients is intimately linked to this. Nonetheless, the prognostic signature and classification of different immune patterns in LUAD patients based on the macrophages is largely unexplored.

METHODS

Two sc-RNAseq datasets of LUAD patients were collected and reprocessed. The differentially expressed genes (DEGs) related to macrophages between LUAD tissues and normal lung tissues were then identified. Based upon the above genes, three distinct immune patterns in the TCGA-LUAD cohort were identified. The ssGSEA and CIBERSORT were applied for immune profiling and characterization of different subtypes. A four-gene prognostic signature for LUAD patients was established based on the DEGs between the subtypes using stepwise multi-Cox regression. TCGA-LUAD cohort was used as training set. Five GEO-LUAD datasets and an independent cohort containing 112 LUAD samples were used for validation. TIDE (tumor immune dysfunction and exclusion) and drug sensitivity analyses were also performed.

RESULTS

Macrophage-related differentially expressed genes were found out using the publicly available scRNA-seq data of LUAD. Three different immune patterns which were proved to have distinct immune infiltration characteristics in the TCGA-LUAD cohort were recognized based on the above macrophage-related genes. Thereafter, 174 DEGs among the above three different immune patterns were figured out; on the basis of this, a four-gene prognostic signature was constructed. This signature distinguished the prognosis of LUAD patients well in various GSE datasets as well as our independent cohort. Further analyses revealed that patients which had a higher risk score also accompanied with a lower immune infiltration level and a worse response to several immunotherapy biomarkers.

CONCLUSION

This study highlighted that macrophage were significantly associated with TME diversity and complexity. The four-gene prognostic signature could be used for predicting outcomes and immune landscapes for patients with LUAD.

Comprehensive analysis of the cuproptosis-related model to predict prognosis and indicate tumor immune infiltration in lung adenocarcinoma

Background

Cuproptosis is a novel form of programmed cell death termed as Cu-dependent cytotoxicity. However, the roles of cuproptosis-associated genes (CAGs) in lung adenocarcinoma (LUAD) have not been explored comprehensively.

Methods

We obtained CAGs and utilized consensus molecular clustering by “non-negative matrix factorization (NMF)” to stratify LUAD patients in TCGA (N = 511), GSE13213 (N = 117), and GSE31210 (N = 226) cohorts. The ssGSEA and CIBERSORT algorithms were used to evaluate the relative infiltration levels of immune cell types in tumor microenvironment (TME). The risk score based on CAGs was calculated to predict patients’ survival outcomes.

Results

We identified three cuproptosis-associated clusters with different clinicopathological characteristics. We found that the cuproptosis-associated cluster with the worst survival rates exhibited a high enrichment of activated CD4/8+ T cells. In addition, we found that the cuproptosis-associated risk score could be used for patients’ prognosis prediction and provide new insights in immunotherapy of LUAD patients. Eventually, we constructed a nomogram-integrated cuproptosis-associated risk score with clinicopathological factors to predict overall survival in LUAD patients, with 1-, 3-, and 5-year area under curves (AUCs) being 0.771, 0.754, and 0.722, respectively, all of which were higher than those of the TNM stage.

Conclusions

In this study, we uncovered the biological function of CAGs in the TME and its correlations with clinicopathological parameters and patients’ prognosis in LUAD. These findings could provide new angles for immunotherapy of LUAD patients.

Prognostic Significance of ANGPTL4 in Lung Adenocarcinoma: A Meta-Analysis Based on Integrated TCGA and GEO Databases

Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. Recent studies have found that angiopoietin-like 4 (ANGPTL4) is abnormally expressed in many tumors, so it can serve as a potential prognostic marker and therapeutic target. However, its prognostic value in LUAD remains unclear. We downloaded RNA sequence data for LUAD from The Cancer Genome Atlas (TCGA) database, methylation data from the University of California Santa Cruz genome database, and clinical information. R software (version 4.1.1) was applied to analyze the ANGPTL4 expression in LUAD and nontumor samples, and the correlation with clinical characteristics to assess its prognostic and diagnostic value. In addition, we analyzed the relationship between the ANGPTL4 expression and methylation levels. Tumor IMmune Estimation Resource (TIMER) tool was taken for immune infiltration analysis, and two Gene Expression Omnibus (GEO) datasets were combined for meta-analysis. Finally, differentially expressed genes (DEGs) related to ANGPTL4 were analyzed to clarify its function. As shown in our results, ANGPTL4 was upregulated in LUAD and was an independent risk factor for the diagnosis and prognosis of LUAD. The general methylation level and eight ANGPTL4 methylation sites were significantly negatively correlated with the ANGPTL4 expression. Furthermore, we found that B cell infiltration was negatively correlated with ANGPTL4 expression and was an independent risk factor. Meta-analysis showed that the high expression of ANGPTL4 was closely associated with a poor prognosis. 153 DEGs, including the matrix metalloproteinase family, the chemokines subfamily, and the collagen family, were correlated with ANGPTL4. In this study, we found that ANGPTL4 was significantly elevated in LUAD and was closely associated with the development and poor prognosis of LUAD, suggesting that ANGPTL4 may be a prognostic biomarker and a potential therapeutic target for LUAD.

Dual-receptor-targeted nanomedicines: emerging trends and advances in lung cancer therapeutics

Cancer is the leading cause of mortality worldwide. Among all cancer types, lung cancer is recognized as the most lethal and highly metastatic. The application of targeted nanomedicine loaded with anticancer drugs is highly desirable for successful lung cancer treatment. However, due to the heterogenicity and complexity of lung cancer, the therapeutic effectiveness of a single receptor targeting nanomedicine is unfortunately limited. Therefore, the concept of dual-receptor-targeted nanomedicine is an emerging trend for the advancement in lung cancer therapeutics. In this review, the authors discuss various single- and dual-receptor-targeted nanomedicines that have been developed for lung cancer treatment. Furthermore, the authors also discussed all the types of receptors that can be utilized in combination for the development of dual-receptor-targeted nanomedicines.

Evaluating the Prognostic and Therapeutic Potentials of the Proteasome 26S Subunit, ATPase (PSMC) Family of Genes in Lung Adenocarcinoma: A Database Mining Approach

This study explored the prognostic and therapeutic potentials of multiple Proteasome 26S Subunit, ATPase (PSMC) family of genes (PSMC1-5) in lung adenocarcinoma (LUAD) diagnosis and treatment. All the PSMCs were found to be differentially expressed (upregulated) at the mRNA and protein levels in LUAD tissues. The promoter and multiple coding regions of PSMCs were reported to be differentially and distinctly methylated, which may serve in the methylation-sensitive diagnosis of LUAD patients. Multiple somatic mutations (alteration frequency: 0.6–2%) were observed along the PSMC coding regions in LUAD tissues that could assist in the high-throughput screening of LUAD patients. A significant association between the PSMC overexpression and LUAD patients’ poor overall and relapse-free survival (p < 0.05; HR: >1.3) and individual cancer stages (p < 0.001) was discovered, which justifies PSMCs as the ideal targets for LUAD diagnosis. Multiple immune cells and modulators (i.e., CD274 and IDO1) were found to be associated with the expression levels of PSMCs in LUAD tissues that could aid in formulating PSMC-based diagnostic measures and therapeutic interventions for LUAD. Functional enrichment analysis of neighbor genes of PSMCs in LUAD tissues revealed different genes (i.e., SLIRP, PSMA2, and NUDSF3) previously known to be involved in oncogenic processes and metastasis are co-expressed with PSMCs, which could also be investigated further. Overall, this study recommends that PSMCs and their transcriptional and translational products are potential candidates for LUAD diagnostic and therapeutic measure discovery.

Constructing a Prognostic Gene Signature for Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network Analysis and Single-Cell Analysis

Purpose

Lung adenocarcinoma (LUAD) has a high degree of intratumor heterogeneity. Advanced single-cell RNA sequencing (scRNA-seq) technologies have offered tools to analyze intratumor heterogeneity, which improves the accuracy of identifying biomarkers based on single-cell expression data, and thus helps in predicting prognosis of cancer patients and assisting decision-makings for cancer treatment.

Patients and Methods

ScRNA-seq data containing two LUAD and two para-cancerous tissue samples were included to identify different cell clusters in tumor tissues. To identify the most relevant modules and important cell subpopulations (clusters) in LUAD tissues, weighted gene co-expression network analysis (WGCNA) was performed. Subsequently, LUAD molecular subtypes were constructed by unsupervised consensus clustering based on genes in key modules. Using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model of LUAD was established.

Results

A total of 14 cell clusters belonging to 10 cell types in LUAD were identified. The turquoise module was the most relevant to LUAD among all the modules; cluster 10 (C10, lung epithelial cells) was found to be the most strongly associated with the turquoise module. LUAD samples were divided into two groups of distinct molecular subtypes. Based on the 165 shared genes between the turquoise module and C10, 511 DEGs between the two molecular subtypes were obtained, and five of them were selected to construct the gene signature, which was validated to be an independent prognostic marker of LUAD.

Conclusion

Fourteen cell clusters co-existed in LUAD, which contributed to its intratumor heterogeneity. Two molecular subtypes of LUAD were identified and a five-gene signature was developed and validated to be significantly associated with prognostic and clinical characteristics of LUAD patients.

Genetic Clonality as the Hallmark Driving Evolution of Non-Small Cell Lung Cancer

Simple Summary

Limited knowledge about NSCLC evolution has affected therapeutic strategies for many decades. The application of NGS-based techniques to studies on ITH has provided genetic insight into the contribution of clonality primary seeding, as well as to distant dissemination. To date, multiregional ITH affects accurate diagnosis and treatment decisions and is considered the main hallmark of anticancer therapy failure. Understanding the evolutionary trajectories that drive the metastatic process is critical for improving treatment strategies for this deadly condition. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC, highlighting that wide genetic analyses may reveal the phylogenetic lineages of NSCLC evolution.

Abstract

Data indicate that many driver alterations from the primary tumor of non-small cell lung cancer (NSCLC) are predominantly shared across all metastases; however, disseminating cells may also acquire a new genetic landscape across their journey. By comparing the constituent subclonal mutations between pairs of primary and metastatic samples, it is possible to derive the ancestral relationships between tumor clones, rather than between tumor samples. Current treatment strategies mostly rely on the theory that metastases are genetically similar to the primary lesions from which they arise. However, intratumor heterogeneity (ITH) affects accurate diagnosis and treatment decisions and it is considered the main hallmark of anticancer therapy failure. Understanding the genetic changes that drive the metastatic process is critical for improving the treatment strategies of this deadly condition. Application of next generation sequencing (NGS) techniques has already created knowledge about tumorigenesis and cancer evolution; however, further NGS implementation may also allow to reconstruct phylogenetic clonal lineages and clonal expansion. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC. We highlight that wide genetic analyses may reveal the phylogenetic trajectories of NSCLC evolution, and may pave the way to better management of follow-up and treatment.

Identification of an Immune Classification and Prognostic Genes for Lung Adenocarcinoma Based on Immune Cell Signatures

Objective

Current advances in immunotherapy requires accurate tumor sub-classification due to the heterogeneity of lung adenocarcinoma (LUAD). This study aimed to develop a LUAD sub-classification system based on immune cell signatures and identified prognostic gene markers.

Methods

Signatures related to the prognosis of TCGA-LUAD and 4 GSE cohorts were screened and intersected from 184 previously published immune cell signatures. The LUAD samples in the TCGA were clustered by ConsensusClusterPlus. Molecular characteristics, immune characteristics and sensitivity to immunotherapies/chemotherapies were compared. LDA score was established through Linear Discriminant Analysis (LDA). Co-expression module was constructed by Weighted Gene Co-Expression Network Analysis (WGCNA).

Results

Four LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in prognosis among the four subtypes were observed. The IS1 subtype with the worst prognosis showed the highest number of TMB, mutant genes, IFN γ score, angiogenesis score and immune score. Twenty co-expression modules were generated by WGCNA. Blue module, sky blue module and light yellow module were significantly correlated with LUAD prognosis. The hub genes (CCDC90B, ARNTL2, RIPK2, SMCO2 and ADA and NBN) showing great prognostic significance were identified from the blue module. A total of 8 hub genes (NLRC3, CLEC2D, GIMAP5, CXorf65, PARP15, AKNA, ZC3H12D, and ARRDC5) were found in the light yellow module. Except for CXorf65, the expression of the other seven genes were significantly correlated with LUAD prognosis.

Conclusion

This study determined four LUAD subtypes with different molecular and immune characteristics and 13 genes closely related to the prognosis of LUAD. The current findings could help understand the heterogeneity of LUAD immune classes.

Establishing a Macrophage Phenotypic Switch-Associated Signature-Based Risk Model for Predicting the Prognoses of Lung Adenocarcinoma

Background

Tumor-associated macrophages are important components of the tumor microenvironment, and the macrophage phenotypic switch has been shown to correlate with tumor development. However, the use of a macrophage phenotypic switch-related gene (MRG)-based prognosis signature for lung adenocarcinoma (LADC) has not yet been investigated.

Methods

In total, 1,114 LADC cases from two different databases were collected. The samples from TCGA were used as the training set (N = 490), whereas two independent datasets (GSE31210 and GSE72094) from the GEO database were used as the validation sets (N = 624). A robust MRG signature that predicted clinical outcomes of LADC patients was identified through multivariate COX and Lasso regression analysis. Gene set enrichment analysis was applied to analyze molecular pathways associated with the MRG signature. Moreover, the fractions of 22 immune cells were estimated using CIBERSORT algorithm.

Results

An eight MRG-based signature comprising CTSL, ECT2, HCFC2, HNRNPK, LRIG1, OSBPL5, P4HA1, and TUBA4A was used to estimate the LADC patients’ overall survival. The MRG model was capable of distinguishing high-risk patients from low-risk patients and accurately predict survival in both the training and validation cohorts. Subsequently, the eight MRG-based signature and other features were used to construct a nomogram to better predict the survival of LADC patients. Calibration plots and decision curve analysis exhibited good consistency between the nomogram predictions and actual observation. ROC curves displayed that the signature had good robustness to predict LADC patients’ prognostic outcome.

Conclusions

We identified a phenotypic switch-related signature for predicting the survival of patients with LADC.

Clinical Application Perspectives of Lung Cancers 3D Tumor Microenvironment Models for In Vitro Cultures

Despite the enormous progress and development of modern therapies, lung cancer remains one of the most common causes of death among men and women. The key element in the development of new anti-cancer drugs is proper planning of the preclinical research phase. The most adequate basic research exemplary for cancer study are 3D tumor microenvironment in vitro models, which allow us to avoid the use of animal models and ensure replicable culture condition. However, the question tormenting the scientist is how to choose the best tool for tumor microenvironment research, especially for extremely heterogenous lung cancer cases. In the presented review we are focused to explain the key factors of lung cancer biology, its microenvironment, and clinical gaps related to different therapies. The review summarized the most important strategies for in vitro culture models mimicking the tumor–tumor microenvironmental interaction, as well as all advantages and disadvantages were depicted. This knowledge could facilitate the right decision to designate proper pre-clinical in vitro study, based on available analytical tools and technical capabilities, to obtain more reliable and personalized results for faster introduction them into the future clinical trials.

Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma

OBJECTIVES

To develop and validate the imbalanced data correction based PET/CT radiomics model for predicting lymph node metastasis (LNM) in clinical stage T1 lung adenocarcinoma (LUAD).

METHODS

A total of 183 patients (148/35 non-metastasis/LNM) with pathologically confirmed LUAD were retrospectively included. The cohorts were divided into training vs. validation cohort in a ratio of 7:3. A total of 487 radiomics features were extracted from PET and CT components separately for radiomics model construction. Four clinical features and seven PET/CT radiological features were extracted for traditional model construction. To balance the distribution of majority (non-metastasis) class and minority (LNM) class, the imbalance-adjustment strategies using ten data re-sampling methods were adopted. Three multivariate models (denoted as Traditional, Radiomics, and Combined) were constructed using multivariable logistic regression analysis, where the combined model incorporated all of the significant clinical, radiological, and radiomics features. One hundred times repeated Monte Carlo cross-validation was used to assess the application order of feature selection and imbalance-adjustment strategies in the machine learning pipeline. Prediction performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC) and Geometric mean score (G-mean).

RESULTS

A total of 2 clinical parameters, 2 radiological features, 3 PET, and 5 CT radiomics features were significantly associated with LNM. The combined model with Edited Nearest Neighbors (ENN) re-sampling methods showed strong prediction performance than traditional model or radiomics model with the AUC of 0.94 (95%CI = 0.86-0.97) vs. 0.89 (95%CI = 0.79-0.93), 0.92 (95%CI = 0.85-0.97), and G-mean of 0.88 vs. 0.82, 0.80 in the training cohort, and the AUC of 0.75 (95%CI = 0.57-0.91) vs. 0.68 (95%CI = 0.36-0.83), 0.71 (95%CI = 0.48-0.83) and G-mean of 0.76 vs. 0.64, 0.51 in the validation cohort. The combination of performing feature selection before data re-sampling obtains a better result than the reverse combination (AUC 0.76 ± 0.06 vs. 0.70 ± 0.07, p<0.001).

CONCLUSIONS

The combined model (consisting of age, histological type, C/T ratio, MATV, and radiomics signature) integrated with ENN re-sampling methods had strong lymph node metastasis prediction performance for imbalance cohorts in clinical stage T1 LUAD. Radiomics signatures extracted from PET/CT images could provide complementary prediction information compared with traditional model.

Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma

Immune checkpoint genes (ICGs) play pivotal roles in tumor immune microenvironment (TIME), and thus, targeting them represents a promising strategy for cancer immunotherapy. However, the genetic landscape of ICGs in lung adenocarcinoma (LUAD) is still unknown. Herein, we comprehensively evaluated the ICG expression profiles of 1439 LUAD samples and linked ICG expression patterns with infiltration of immune cells, clinical features, and response to immune checkpoint blockade (ICB). The ICGscore was developed to quantify ICG expression patterns of individual patient by principal component analysis algorithms. Three distinct ICG expression patterns and three ICG-related genomic clusters were determined, which were implicated in different clinical outcomes, level of immune infiltrates, and biological process. LUAD patients were subdivided into high- and low-ICGscore subgroups. Patients with higher ICGscore were characterized by favorable survival outcomes, increased immune cell infiltration, and enhanced expression of ICGs. Further analysis revealed that lower ICGscore was associated with greater tumor mutation loads and higher mutation rates of TTN, KEAP1, and ZFHX4. High ICGscore has the potential to be a robust indicator in clinical benefit of immunotherapy. Taken together, unraveling the ICG expression patterns will advance our understanding of heterogeneity of TIME and guides more effective immunotherapeutic strategies in LUAD.

Prognostic Significance and Therapeutic Target of CXC Chemokines in the Microenvironment of Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is one of the most important subtypes of lung cancer and has a high morbidity and mortality. Inflammatory CXC chemokines in tumor microenvironment can stimulate tumor growth, invasion, and metastasis, affecting the prognosis of patients. However, the differential expression profiles, prognostic values, and specific mechanisms of the CXC chemokine family in LUAD have not been clarified.

Methods

Transcriptome expression profile data were extracted from TIMER and TCGA. GEPIA was used to compare the relationship between CXC chemokines and clinicopathologic parameters. The prognostic analysis was performed using a Kaplan–Meier curve in GEPIA. LinkedOmics and TRRUST were applied to conduct the enrichment analysis of the regulatory networks containing the kinase targets, miRNA targets, and transcriptional factor targets. The characteristics of immune infiltration and immune-related clinical outcomes were evaluated with TIMER algorithms. Single-cell RNA sequencing localization analysis of genes as prognostic biomarkers were performed by PanglaoDB.

Results

Nine differentially expressed genes were identified in LUAD compared to normal tissues. Aberrant expression of CXCL2 (P =0.0017), CXCL13 (P= 0.0271), CXCL16 (P= 0.016), and CXCL17 (P= 2.14e-5) was significantly correlated with clinical cancer stage. Furthermore, patients with low gene transcription of CXCL 7 (P = 0.017) and high expression of CXCL 17 (P = 0.00045) had a better prognosis in LUAD. We also found that immune cell infiltration was significantly correlated with LUAD microenvironment mediated by CXC chemokines. Cox proportional hazard model test was conducted and indicated that B cell infiltration could prolong the survival of the LUAD patients. CXCL17 exerted anti-tumors effect through pulmonary alveolar type II cells according to single-cell analysis.

Conclusion

Our research identified the aberrant expression profiles and prognostic biomarkers of CXC chemokines in LUAD. This detailed analysis of the regulatory factor networks for CXC chemokine gene expression may provide novel insights for selecting potential immunotherapeutic targets.

Slug and Vimentin downregulation at the metastatic site is associated with Skip-N2 metastasis of lung adenocarcinoma

OBJECTIVE

Lung cancer displays heterogeneity both in the tumor itself and in its metastatic regions. One interesting behavior of the tumor is known as Skip N2 metastasis, which N2 lymph nodes contain tumor cells while N1 are clean. In this study, mRNA levels of epithelial mesenchymal transition (EMT) related genes in skip N2 and normal N2 involvements of non-small cell lung cancer tissues were investigated to evaluate the possible molecular background that may contribute to the pathogenesis of Skip N2 metastasis.

MATERIALS AND METHODS

Eighty-three surgically resected and paraffin embedded lymph node samples of lung cancer patients were analyzed in this study, which 40 of them were Skip N2. N2 tissues were sampled from 50% tumor containing areas and total RNA was extracted. mRNA levels for 18S, E-cadherin, Vimentin, ZEB1 and SLUG were analyzed via qPCR and E-cadherin and vimentin protein levels via immunohistochemistry (IHC). Bioinformatic analysis were adopted using online datasets to evaluate significantly co-expressed genes with SLUG in lung cancer tissue samples.

RESULTS

Skip-N2 patients who had adenocarcinoma subtype had better survival rates. Comparative analysis of PCR results indicated that Skip N2 tumor tissues had increased E-Cadherin/Vimentin ratio and ZEB1 mRNA expression, and significantly decreased levels of SLUG. E-cadherin IHC staining were higher in Skip N2 and Vimentin were in Non-Skip N2. TP63 had a strong correlation with SLUG expression in the bioinformatics analyses.

CONCLUSION

The results indicate that, at molecular level, Skip N2 pathogenesis has different molecular background and regulation of SLUG expression may orchestrate the process.

Identification of Novel Subtypes in Lung Adenocarcinoma: Evidence from Gene Set Variation Analysis in Tumor and Adjacent Nontumor Samples

In patients with lung adenocarcinoma (LUAD), the prognostic role of adjacent nontumor tissues is still unknown. Alterations in the activity of immunologic and hallmark gene sets in adjacent nontumor tissues may have a potential influence on cell proliferation of normal lung cell after pulmonary lobectomy. We sought to discover LUAD subgroups and prognostic gene sets based on changes in gene set activity in tumor and adjacent nontumor tissues. Firstly, we used gene set variation analysis (GSVA) to characterize the activity changes of 4922 gene sets in LUAD and nontumor samples. Luckily, we identified three novel LUAD subtypes using the nonnegative matrix factorization (NMF) algorithm. In detailed, patients with subtype-3 had a favorable prognosis, but subtypes 1 and 2 had a bad prognosis. In addition, patients with subtype-3 in the validation cohort also lived longer. Meanwhile, using the LASSO-Cox algorithm, we discovered 15 prognostic gene sets in tumors (T gene sets) and two prognostic gene sets in adjacent nontumors (N gene sets). Interestingly, genes from N gene sets were related with immune response in nontumor tissues, but genes from T gene sets were correlated with DNA damaging and repairing in tumor tissues. These findings highlighted the possibility of a stronger immune response in nearby nontumor tissues. In conclusion, our study established a theoretical foundation for selecting therapy strategy for LUAD patients that should be guided by changes in activity in tumor and adjacent nontumor tissues, particularly after pulmonary lobectomy.

Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.

Analysis of the Single-Cell Heterogeneity of Adenocarcinoma Cell Lines and the Investigation of Intratumor Heterogeneity Reveals the Expression of Transmembrane Protein 45A (TMEM45A) in Lung Adenocarcinoma Cancer Patients

Simple Summary

Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related deaths worldwide. Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). Human NSCLC adenocarcinoma cells A549, H1975, and H1650 were studied at single-cell resolution for the expression pattern of 13 markers: GLUT1, MCT4, CA9, TMEM45A, CD66, CD274, CD24, CD326, pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The intra- and inter-cell-line heterogeneity of A549, H1975, and H1650 cells were demonstrated through hypoxic modeling. Additionally, human primary lung adenocarcinoma, and non-involved healthy lung tissue were homogenized to prepare a single-cell suspension for CyTOF analysis. The single-cell heterogeneity was confirmed using unsupervised viSNE and FlowSOM analysis. Our results also show, for the first time, that TMEM45A is expressed in lung adenocarcinoma.

Abstract

Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.

Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

PURPOSE

In the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.

METHODS

A total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.

RESULTS

A total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.

CONCLUSIONS

A prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients’ LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients’ prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.

Intertumoural Heterogeneity and Branch Evolution of Synchronous Multiple Primary Lung Adenocarcinomas by Next-Generation Sequencing Analysis

Objectives

Multiple primary lung cancers (MPLCs) are an increasingly well-known clinical phenomenon, but there is a lack of high-level evidence for their optimal clinical diagnosis and therapeutic approaches. Thus, we analysed genetic variation to determine the intertumoural heterogeneity and branch evolution of synchronous multiple primary lung adenocarcinomas.

Methods

We performed multiplex mutational sequencing on 93 synchronous multiple primary lung adenocarcinoma lesions from 42 patients who underwent surgical resection.

Results

The high discordance rate of mutation was 92.9% (n=39) between tumours in individual patients. EGFR, TP53 and KRAS mutations were detected in 57 (61.3%), 19 (20.4%) and 11 (11.8%) of the 93 tumours, respectively. 16 cases of multiple primary lung adenocarcinomas simultaneously harboured EGFR mutations and TP53 mutations. Matching mutations between paired tumours were observed in 1 (2.4%) patient for P20. The genotypes were all EGFR L858R mutations, but the pathological type of P20T1 was lepidic predominant, and P20T2 was adenocarcinoma in situ. In the phylogenetic tree, genetic variations were divided into trunk, shared and branch subtypes. Branch mutations accounted for 91.09% of variations in sMPLA, while the ratio of trunk (4.95%) and shared (3.96%) variations was significantly lower.

Conclusions

Remarkable intertumoural heterogeneity and frequent branch mutations were found in synchronous multiple primary lung adenocarcinomas.

Overexpression and diagnostic significance of INTS7 in lung adenocarcinoma and its effects on tumor microenvironment

BACKGROUND

Lung cancer is the leading cause of death worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. INTS7, one of the subunits of the integrator complex, is upregulated in several tumors. Thus, we aimed to investigate the expression profile and clinical significance of INTS7 in LUAD.

METHODS

The expression profile of INTS7 was tested in TCGA database and clinical specimens. ROC curve was used to detect the diagnostic value of INTS7, CEA and INTS7 combined with CEA. Kaplan-Meier analysis was used to analyze the prognostic value of INTS7. Differentially expressed genes (DEGs) related to INTS7 were analyzed, and functional enrichment analysis was used to explore the potential mechanisms related to DEGs. The correlations between INTS7 and tumor-infiltrating immune cells, immune scores, stromal scores, and immune checkpoints were explored. Finally, the relationship between INTS7 expression and sensitivity to molecular-targeted therapy was examined.

RESULTS

Data from TCGA database showed that INTS7 mRNA expression was substantially upregulated in LUAD, the AUC values of INTS7 for diagnosing LUAD were >0.8, combined detection of INTS7 and CEA could improve the diagnostic efficiency and early stage patients with high expression of INTS7 showed shorter overall survival. IHC analysis of clinical samples further verified the overexpression of INTS7 protein and confirmed the diagnostic value of INTS7 in LUAD, especially for patients at advanced stages with the AUC >0.8. A total of 192 DEGs were identified and DEGs were primarily involved in cell cycle, inflammatory response, and immune response. Moreover, INTS7 expression was negatively correlated with memory B cells, regulatory T cells (Treg), monocytes, resting myeloid dentritic cells and activated mast cells infiltration, and positively correlated with naive B cells, T follicular helper cells (Tfh), activated myeloid dentritic cells and neutrophils infiltration. In addition, patients with high expression of INTS7 showed less expression of immune checkpoints and exhibited less sensitivity to molecular-targeted drugs.

CONCLUSION

INTS7 is a potential diagnostic biomarker for LUAD. And its expression level may correlate with tumor microenvireoment, immunotherapy responsiveness, and molecular-targeted therapy responsiveness in LUAD.

Targeting USP22 with miR‑30‑5p to inhibit the hypoxia‑induced expression of PD‑L1 in lung adenocarcinoma cells

Lung cancer is one of the most common forms of cancer and accounts for a significant proportion of all cancer‑related deaths. Lung adenocarcinoma (LUAD) accounts for approximately 40% of all cases of lung cancer. In recent years, new developments in both the diagnosis and treatment of LUAD have been achieved. Unfortunately, the prognosis remains poor for patients with malignant LUAD. Hypoxia is a common characteristic of solid tumors and induce the immune evasion by increasing the expression of programmed cell death‑ligand‑1 (PD‑L1) in the tumor. In this study, it was predicted that ubiquitin‑specific peptidase 22 (USP22) is the direct target of the microRNA (miR)‑30‑5p family, including miR‑30a‑5p, miR‑30b‑5p, miR‑30c‑5p, miR‑30d‑5p and miR‑30e‑5p. Furthermore, the binding of USP22 with the miR‑30‑5p family was confirmed by luciferase assay. In addition, it was demonstrated that targeting USP22 via the miR‑30‑5p family inhibited the induction of PD‑L1 expression in hypoxic conditions, thus preventing activated T cells from killing LUAD cells. Our results indicated that miR‑30a‑5p, miR‑30b‑5p, miR‑30c‑5p, miR‑30d‑5p and miR‑30e‑5p represent new targets for the treatment of LUAD.

N6 -methyladenosine (m6 A) RNA modification of G protein-coupled receptor 133 increases proliferation of lung adenocarcinoma

Lung adenocarcinoma (LUAD) accounts for almost 40% of lung cancers, leading to significant associated morbidity and mortality rates. However, the mechanism of LUAD tumorigenesis remains far from clear. Here, we scanned down‐regulated genes involved in LUAD sourced from The Cancer Genome Atlas and Gene Expression Omnibus data and focused on G protein‐coupled receptor 133 (GPR133). We offer compelling evidence that GPR133 was expressed at low levels in the setting of LUAD, and higher expression was positively related to a better prognosis among patients with LUAD. Functionally, GPR133 inhibited cell proliferation and tumor growth in vitro and in vivo. Regarding the mechanism, flow cytometry assays and western blot assays showed that GPR133 enhanced p21 and decreased cyclin B1 expression, thus triggering LUAD cells at G2/M‐phase arrest. Consistent with this, we evaluated the expression levels of cell‐cycle biomarkers and found that bioinformatics analysis combined with N⁶‐methyladenosine (methylation at the N6 position in adenosine) RNA immunoprecipitation‐qPCR assay indicated that GPR133 expression was down‐regulated by this modification. Moreover, we observed that methyltransferase‐like 3 was impaired in LUAD, and that it is able to significantly increase levels of GPR133 by enhancing its RNA stability. In conclusion, we found that GPR133 expression was down‐regulated in LUAD via N⁶‐methyladenosine modification. Increasing GPR133 levels could suppress LUAD cell proliferation and tumor growth.

CTNNB1 S37C mutation causing cells proliferation and migration coupled with molecular mechanisms in lung adenocarcinoma

Background

This study aimed to investigate the potential cytological effects and molecular mechanisms of β-catenin (CTNNB1) S37C mutation in lung adenocarcinoma (LUAD).

Methods

CTNNB1 with S37C mutation were transfected into LUAD cell lines. The expression of β-catenin were determined using Western blot. Cell proliferation and migration were detected using cell counting kit-8 (CCK-8) assay and wound healing assay, respectively. Transcriptome sequencing was performed on LUAD cells with CTNNB1 S37C mutation (CTNNB1 mutation group) and LUAD cells without treatment (Control group), followed by the screening of differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) analysis were performed for the DEGs. Finally, the expression of key DEGs were validated by quantitative real-time PCR (qRT-PCR).

Results

CTNNB1 with S37C mutation was successful expressed in 2 cell lines. Cells proliferation and migration were significantly promoted in mutation group in comparison with that of Control group (P<0.05). A total of 180 DEGs were revealed between Control and CTNNB1 mutation groups. These DEGs were mainly enriched in extracellular matrix function and nicotine addiction pathway. PPI network contained 51 DEGs and 45 interactions. PTPRD, GNG7 and CNTN1 were hub genes in PPI network with higher degree. CGB5 interacted with PTPRU, while IGFBP3 showed interaction with MMP1. Results of qRT-PCR confirmed the expression of several key DEGs in transcriptome analysis.

Conclusions

CTNNB1 S37C mutation contributed the LUAD cells proliferation and migration. PTPRD, IGFBP-3, MMP1 and PTPRU might play roles in the effect of CTNNB1 S37C mutation in LUAD.

The role of EGFR-TKIs as adjuvant therapy in EGFR mutation-positive early-stage NSCLC: A meta-analysis

BACKGROUND

The role of adjuvant epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is not clear in early-stage nonsmall-cell lung cancer (NSCLC) patients. This meta-analysis aims to compare the efficacy and safety of EGFR-TKIs as adjuvant therapy with chemotherapy or placebo in NSCLC patients harboring EGFR mutations.

PATIENTS AND METHODS

Pubmed, Embase, and Cochrane databases were searched for randomized controlled trials. The hazard ratio (HR) of disease-free survival (DFS) and overall survival (OS) as well as the risk ratio (RR) of severe adverse events were merged.

RESULTS

Seven articles from five studies from 1843 records, a total of 1227 patients, were included in the analysis. The HR for DFS was 0.38 (95% confidence interval [CI] 0.22-0.63), in favor of EGFR-TKIs. However, no significant benefit of OS was seen (HR = 0.61, 95% CI 0.31-1.22). Treatment benefit was more pronounced in patients with advanced disease stage and longer duration of medication, EGFR exon 19 deletion mutation, and treatment with third-generation EGFR-TKIs. Adjuvant targeted therapy may cause few adverse events compared with chemotherapy (RR = 0.28, 95% CI 0.09-0.94). The possibility of severe adverse events for the first-generation drugs was significantly lower than for third-generation drugs.

CONCLUSION

In EGFR mutation-positive patients with stage IB-IIIA NSCLC, compared with adjuvant chemotherapy or placebo, adjuvant EGFR-TKIs should effectively improve the patient's DFS, but not effectively improve OS. Disease stage, treatment duration, mutation types, and therapeutic drugs could affect the degree of benefit. Adjuvant EGFR-TKIs had more favorable tolerability than chemotherapy, especially with the usage of first-generation drugs.

Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside

Background

Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD).

Methods

The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram.

Results

In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873).

Conclusions

We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes.

Independent prognostic implications of RRM2 in lung adenocarcinoma

Background: Ribonucleoside-diphosphate reductase subunit M2 (RRM2) is the catalytic subunit of ribonucleotide reductase and modulates the enzymatic activity, which is essential for DNA replication and repair. However, the role of RRM2 in lung adenocarcinoma (LUAD) remains unclear.

Methods: In this study, we explored the expression pattern and prognostic value of RRM2 in LUAD across TCGA, GEO, Oncomine, UALCAN, PrognoScan, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via Cox analyses. LinkedOmics and GEPIA2 were applied to investigate co-expression and functional networks associated with RRM2. Besides, we used TIMER to assess the correlation between RRM2 and the main six types of tumor-infiltrating immune cells. Lastly, the correlations between immune signatures of immunomodulators, chemokines, and 28 tumor-infiltrating lymphocytes (TILs) and RRM2 were examined by tumor purity-corrected partial Spearman's rank correlation coefficient through TIMER portal.

Results:RRM2 was found upregulated in tumor tissues in TCGA-LUAD, and validated in multiple independent cohorts. Moreover, whether in TCGA or other cohorts, high RRM2 expression was found to be associated with poor survival. Cox analyses showed that high RRM2 expression was an independent risk factor for overall survival, disease-specific survival, and progression-free survival of LUAD. Functional network analysis suggested that RRM2 regulates RNA transport, oocyte meiosis, spliceosome, ribosome biogenesis in eukaryotes, and cellular senescence signaling through pathways involving multiple cancer-related kinases and E2F family. Also, RRM2 expression correlated with infiltrating levels of B cells, CD4+ T cells, and neutrophils. Subsequent analysis found that B cells and dendritic cells could predict the outcome of LUAD. B cells were identified as an independent risk factor among six types of immune cells through Cox analyses. At last, the correlation analysis showed RRM2 correlated with 67.68% (624/922) of the immune signatures we performed.

Conclusion: Our research showed that RRM2 could independently predict the prognosis of LUAD and was associated with immune infiltration. In particular, the tight relationship between RRM2 and B cell marker genes are the potential epicenter of the immune response and one of the critical factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of RRM2 in LUAD.

Independent Prognostic Potential of GNPNAT1 in Lung Adenocarcinoma

Background

Glucosamine-Phosphate N-Acetyltransferase 1 (GNPNAT1) is a critical enzyme in the biosynthesis of uridine diphosphate-N-acetylglucosamine. It has many important functions, such as protein binding, monosaccharide binding, and embryonic development and growth. However, the role of GNPNAT1 in lung adenocarcinoma (LUAD) remains unclear.

Methods

In this study, we explored the expression pattern and prognostic value of GNPNAT1 in LUAD across TCGA and GEO databases and assessed its independent prognostic value via Cox analysis. LinkedOmics and GEPIA2 were applied to investigate coexpression and functional networks associated with GNPNAT1. The TIMER web tool was deployed to assess the correlation between GNPNAT1 and the main six types of tumor-infiltrating immune cells. Besides, the correlations between GNPNAT1 and the LUAD common genetic mutations, TMB, and immune signatures were examined.

Results

GNPNAT1 was validated upregulated in tumor tissues in TCGA-LUAD and GEO cohorts. Moreover, in both TCGA and GEO cohorts, high GNPNAT1 expression was found to be associated with poor overall survival. Cox analysis showed that high GNPNAT1 expression was an independent risk factor for LUAD. Functional network analysis suggested that GNPNAT1 regulates cell cycle, ribosome, proteasome, RNA transport, and spliceosome signaling through pathways involving multiple cancer-related kinases and E2F family. In addition, GNPNAT1 correlated with infiltrating levels of B cells, CD4+ T cells, and dendritic cells. B cells and dendritic cells could predict the outcome of LUAD, and B cells and CD4+ T cells were significant independent risk factors. The TMB and mutations of KRAS, EGFR, STK11, and TP53 were correlated with GNPNAT1. At last, the correlation analysis showed GNPNAT1 correlated with most of the immune signatures we performed.

Conclusion

Our findings showed that GNPNAT1 was correlated to the prognosis and immune infiltration of LUAD. In particular, the tight relationship between GNPNAT1 and B cell marker genes may be the epicenter of the immune response and one of the key factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of GNPNAT1 in LUAD.

Sauchinone: a prospective therapeutic agent-mediated EIF4EBP1 down-regulation suppresses proliferation, invasion and migration of lung adenocarcinoma cells

Lung adenocarcinoma (LUAD) is the top prevalent histological kind of lung cancer worldwide. Recent evidences have demonstrated that Sauchinone plays an anticancer role in tumor cell invasion and migration. Therefore, we performed this investigation to explain the potential role of Sauchinone in LUAD as well as the potential mechanism involved. Cell counting kit 8 (CCK-8) and transwell experiments were implemented to measure the proliferative, invasive and migratory abilities of LUAD cells. qRT-PCR and Western blot were performed to detect the transfection efficiency of si-EIF4EBP1s. Additionally, Western blot was also implemented to evaluate the effect of Sauchinone on EIF4EBP1 expression level as well as cell cycle-related proteins. Our findings showed that Sauchinone remarkably suppressed the proliferative ability of LUAD cells in a dose-dependent and time-dependent manner. EIF4EBP1 was a candidate target gene of Sauchinone. EIF4EBP1 expression was increased in LUAD tissues, and its high expression induced a poorer prognosis of LUAD patients. EIF4EBP1 expression was positively associated with cell cycle in LUAD. Sauchinone treatment attenuated EIF4EBP1 expression and cell cycle-related protein levels. Knockdown of EIF4EBP1 repressed the proliferation, invasion and migration of LUAD cells; furthermore, Sauchinone stimulation enforced its inhibitory effect. Meanwhile, the treatment of Sauchinone intensified the arrest of cell cycle induced by EIF4EBP1 knockdown. To sum up, our discovery indicated that Sauchinone exerts an anticancer role through down-regulating EIF4EBP1 and mediating cell cycle in LUAD.