Smoker and non-smoker lung adenocarcinoma is characterized by distinct tumor immune microenvironments

Is Lobectomy Actually Worse Than Segmentectomy for All Stage I Non-Small Cell Lung Cancer?

INTRODUCTION

The recent results of the JCOG 0802 and CALGB 140503 studies suggest that segmentectomy should be considered instead of lobectomy for patients with peripheral <2 cm node-negative non-small cell lung cancer (NSCLC). This study aimed to test this hypothesis in a retrospective analysis of a larger dataset of patients with stage I NSCLC recorded in the Surveillance, Epidemiology, and End Results database.

METHODS

Patients with all stage I NSCLC (≤4 cm in size) who underwent either segmentectomy or lobectomy from 2000 to 2017 were analyzed. The primary endpoints were overall survival and lung cancer-specific survival, while the secondary endpoints were the 30-day and 90-day mortality.

RESULTS

Overall, 32,673 patients treated by lobectomy and 2166 patients treated by segmentectomy were included in the initial data collection. After 1:1 propensity score matching (PSM), 2016 patients in each group were enrolled in the final analysis with well-balanced baseline characteristics. After PSM, there was no difference between segmentectomy and lobectomy for all stage IA NSCLC (≤3 cm in size) in both overall survival and lung cancer-specific survival (hazard ratio: 0.87 [0.74-1.02], P value: 0.09 and hazard ratio: 0.81 [0.4-1.03], P value: 0.09, respectively). Furthermore, lobectomy had higher 30-day mortality than segmentectomy: 1.1% versus 2.1%, P value: 0.01. However, this difference was not significant for 90-day mortality, even after PSM (3.9% versus 3.0%, P value: 0.17).

CONCLUSIONS

We found no evidence to support the use of lobectomy rather than segmentectomy in stage IA NSCLC in terms of either overall or lung cancer-specific long-term survival. The choice of lobectomy may also be detrimental to early postoperative recovery.

Deep learning identifies histopathologic changes in bladder cancers associated with smoke exposure status

Smoke exposure is associated with bladder cancer (BC). However, little is known about whether the histologic changes of BC can predict the status of smoke exposure. Given this knowledge gap, the current study investigated the potential association between histology images and smoke exposure status. A total of 483 whole-slide histology images of 285 unique cases of BC were available from multiple centers for BC diagnosis. A deep learning model was developed to predict the smoke exposure status and externally validated on BC cases. The development set consisted of 66 cases from two centers. The external validation consisted of 94 cases from remaining centers for patients who either never smoked cigarettes or were active smokers at the time of diagnosis. The threshold for binary categorization was fixed to the median confidence score (65) of the development set. On external validation, AUC was used to assess the randomness of predicted smoke status; we utilized latent feature presentation to determine common histologic patterns for smoke exposure status and mixed effect logistic regression models determined the parameter independence from BC grade, gender, time to diagnosis, and age at diagnosis. We used 2,000-times bootstrap resampling to estimate the 95% Confidence Interval (CI) on the external validation set. The results showed an AUC of 0.67 (95% CI: 0.58-0.76), indicating non-randomness of model classification, with a specificity of 51.2% and sensitivity of 82.2%. Multivariate analyses revealed that our model provided an independent predictor for smoke exposure status derived from histology images, with an odds ratio of 1.710 (95% CI: 1.148-2.54). Common histologic patterns of BC were found in active or never smokers. In conclusion, deep learning reveals histopathologic features of BC that are predictive of smoke exposure and, therefore, may provide valuable information regarding smoke exposure status.

Circulating levels of galectin-9 are a potential biomarker of survival in advanced non-small-cell lung cancer

BACKGROUND

The immune system is recognized to have therapeutic potential to destroy cancer cells. Soluble T-cell immunoglobulin mucin domain-3 (sTIM-3) and its ligand galectin 9 (Gal-9) cause suppression of cytokine production, cell cycle arrest and cell death. sTIM-3 and Gal-9 levels may have prognostic implications in non-small-cell lung cancer (NSCLC) patients.

METHODS

This prospective cohort study was performed at Instituto de Medicina Integral Prof. Fernando Figueira, Recife, Pernambuco, Brazil. Fifty-eight patients were diagnosed with advanced NSCLC from January 2019 to January 2020.

RESULTS

The age median was of 64.0 years. Soluble galectin-9 (sGal-9) levels in the smokers compared to nonsmoker patients (p < 0.0001). By using the receiver operating characteristic curve, we found that a baseline of 1694 pg/mL (cutoff). sGAL9 with specificity (72.2%), sensitivity (83.2%) and area under the curve = 0.8497 (p < 0.0004). Until 18.2 months, 46.8% and 72.9% were alive in the sGAL9low and sGAL9high groups, respectively (log-rank test; p = 0.02). The median survival was 15.9 months for sGAL9low (≤1694 pg/mL).

CONCLUSION

This study indicated an association of tobacco with the release of circulating sGal-9 levels and the accuracy of sGal-9 as a potential biomarker predictive of survival time in advanced NSCLC patients. Furthermore, sGal-9 has may be a potential therapeutic target in the advanced NSCLC.

Cigarette smoke sustains immunosuppressive microenvironment inducing M2 macrophage polarization and viability in lung cancer settings

BACKGROUND

It is amply demonstrated that cigarette smoke (CS) has a high impact on lung tumor progression worsening lung cancer patient prognosis and response to therapies. Alteration of immune cell types and functions in smokers' lungs have been strictly related with smoke detrimental effects. However, the role of CS in dictating an inflammatory or immunosuppressive lung microenvironment still needs to be elucidated. Here, we investigated the effect of in vitro exposure to cigarette smoke extract (CSE) focusing on macrophages.

METHODS

Immortalized murine macrophages RAW 264.7 cells were cultured in the presence of CS extract and their polarization has been assessed by Real-time PCR and cytofluorimetric analysis, viability has been assessed by SRB assay and 3D-cultures and activation by exposure to Poly(I:C). Moreover, interaction with Lewis lung carcinoma (LLC1) murine cell models in the presence of CS extract were analyzed by confocal microscopy.

RESULTS

Obtained results indicate that CS induces macrophages polarization towards the M2 phenotype and M2-phenotype macrophages are resistant to the CS toxic activity. Moreover, CS impairs TLR3-mediated M2-M1 phenotype shift thus contributing to the M2 enrichment in lung smokers.

CONCLUSIONS

These findings indicate that, in lung cancer microenvironment of smokers, CS can contribute to the M2-phenotype macrophages prevalence by different mechanisms, ultimately, driving an anti-inflammatory, likely immunosuppressive, microenvironment in lung cancer smokers.

Effect of smoking-related features and 731 immune cell phenotypes on esophageal cancer: a two-sample and mediated Mendelian randomized study

Introduction

Numerous observational studies have indicated that smoking is a substantial risk factor for esophageal cancer. However, there is a shortage of research that delves into the specific causal relationship and potential mediators between the two. Our study aims to validate the correlation between smoking-related traits and esophageal cancer while exploring the possible mediating effects of immune factors.

Methods

Initially, we conducted bidirectional univariate Mendelian Randomization (MR) analyses to forecast the causal effects linking smoking-related traits and esophageal cancer. Subsequently, we employed a two-step MR analysis to scrutinize immune cell phenotypes that could mediate these effects. Finally, the coefficient product method was employed to determine the precise mediating impact. Additionally, we have refined our sensitivity analysis to ensure the reliability of the outcomes.

Results

After analysis, Smoking status: Never had a significant negative association with the incidence of esophageal cancer (inverse-variance weighted (IVW) method, p=1.82e-05, OR=0.10, 95%CI=0.04~0.29). Ever smoked (IVW, p=1.49e-02, OR=4.31, 95%CI=1.33~13.94) and Current tobacco smoking (IVW, p=1.49e-02, OR=4.31, 95%CI=1.33~13.94) showed the promoting effect on the pathogenesis of esophageal cancer. Through further examination, researchers discovered 21 immune cell phenotypes that have a causal relationship with esophageal cancer. After careful screening, two immune cell phenotypes were found to have potential mediating effects. In particular, it was observed that in the case of the preventive effect of Smoking status: Never on esophageal cancer, the absolute count of CD62L plasmacytoid dendritic cells mediated a reduction of 4.21%, while the mediating effect of CD27 in CD20-CD38-B cells was -4.12%. In addition, sensitivity analyses did not reveal significant heterogeneity or level pleiotropy.

Conclusion

The study provides new evidence for the causal relationship between smoking-related features and esophageal cancer and proposes immune factors with potential mediating effects. However, this finding needs to be further demonstrated by more extensive clinical studies.

Pyroptosis-Derived Long Noncoding RNA Profiles Reveal a Novel Signature for Evaluating the Prognosis of Patients With Lung Adenocarcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms.

RESULTS

In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts.

CONCLUSION

We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets.

Lung Cancer in Never Smokers: Delving into Epidemiology, Genomic and Immune Landscape, Prognosis, Treatment, and Screening

Lung cancer in never smokers (LCINS) represents a growing and distinct entity within the broader landscape of lung malignancies. This review provides a comprehensive overview of LCINS, encompassing its epidemiologic trends, risk factors, distinct genomic alterations, clinical outcomes and the ongoing initiative aimed at formulating screening guidelines tailored to this unique population. As LCINS continues to gain prominence, understanding its intricate genomic landscape has become pivotal for tailoring effective therapeutic strategies. Moreover, LCINS does not meet the criteria for lung cancer screening as per the current guidelines. Hence, there is an urgent need to explore its heterogeneity in order to devise optimal screening guidelines conducive to early-stage detection. This review underscores the vital importance of detailed research to elucidate the multifaceted nature of LCINS, with the potential to shape future clinical management and screening recommendations for this unique and growing patient cohort.

Cross-Talks between RKIP and YY1 through a Multilevel Bioinformatics Pan-Cancer Analysis

Recent studies suggest that PEBP1 (also known as RKIP) and YY1, despite having distinct molecular functions, may interact and mutually influence each other's activity. They exhibit reciprocal control over each other's expression through regulatory loops, prompting the hypothesis that their interplay could be pivotal in cancer advancement and resistance to drugs. To delve into this interplay's functional characteristics, we conducted a comprehensive analysis using bioinformatics tools across a range of cancers. Our results confirm the association between elevated YY1 mRNA levels and varying survival outcomes in diverse tumors. Furthermore, we observed differing degrees of inhibitory or activating effects of these two genes in apoptosis, cell cycle, DNA damage, and other cancer pathways, along with correlations between their mRNA expression and immune infiltration. Additionally, YY1/PEBP1 expression and methylation displayed connections with genomic alterations across different cancer types. Notably, we uncovered links between the two genes and different indicators of immunosuppression, such as immune checkpoint blockade response and T-cell dysfunction/exclusion levels, across different patient groups. Overall, our findings underscore the significant role of the interplay between YY1 and PEBP1 in cancer progression, influencing genomic changes, tumor immunity, or the tumor microenvironment. Additionally, these two gene products appear to impact the sensitivity of anticancer drugs, opening new avenues for cancer therapy.

A Functional Genomics Review of Non-Small-Cell Lung Cancer in Never Smokers

There is currently a dearth of information regarding lung cancer in never smokers (LCINS). Additionally, there is a difference in somatic mutations, tumour mutational burden, and chromosomal aberrations between smokers and never smokers (NS), insinuating a different disease entity in LCINS. A better understanding of actionable driver alterations prevalent in LCINS and the genomic landscape will contribute to identifying new molecular targets of relevance for NS that will drastically improve outcomes. Differences in treatment outcomes between NS and smokers, as well as sexes, with NSCLC suggest unique tumour characteristics. Epidermal growth factor receptor (EGFR) tyrosine kinase mutations and echinoderm microtubule-associated protein-like 4 anaplastic lymphoma kinase (EML4-ALK) gene rearrangements are more common in NS and have been associated with chemotherapy resistance. Moreover, NS are less likely to benefit from immune mediators including PD-L1. Unravelling the genomic and epigenomic underpinnings of LCINS will aid in the development of not only novel targeted therapies but also more refined approaches. This review encompasses driver genes and pathways involved in the pathogenesis of LCINS and a deeper exploration of the genomic landscape and tumour microenvironment. We highlight the dire need to define the genetic and environmental aspects entailing the development of lung cancer in NS.

Development and validation of novel prognostic models for zinc finger proteins-related genes in soft tissue sarcoma

As the most common transcriptional regulators, zinc finer proteins (ZNFs) play vital roles in occurrence and progression of malignant tumors. Whereas, information regarding the roles of ZNFs in soft tissue sarcomas (STS) remains scarce. In this study, a comprehensive bioinformatics analysis investigating roles of ZNFs in STS was performed. Initially, we extracted raw datasets of differentially expressed ZNFs from GSE2719. Using a sequence of bioinformatics methods, we then investigated the prognostic significance, function, and molecular subtype of these differentially expressed ZNFs. In addition, CCK8 and plate clone formation assays were used to explore the effect of ZNF141 on STS cells. A total of 110 differentially expressed ZNFs were identified. Nine ZNFs (HLTF, ZNF292, ZNF141, LDB3, PHF14, ZNF322, PDLIM1, NR3C2, and LIMS2) were selected to establish an overall survival (OS) prediction model, and seven ZNFs (ZIC1, ZNF141, ZHX2, ZNF281, ZNHIT2, NR3C2, and LIMS2) were used to develop a progression-free survival (PFS) prediction model. Compared with patients with low-risk in the TCGA training and testing cohorts, as well as the GEO validation cohorts, patients with high-risk had poorer OS and PFS. Using nomograms constructed with the identified ZNFs predicting OS and PFS, we established a clinically useful model. Four distinct molecular subtypes with different prognostic and immune infiltration characteristics were identified. In vitro experiments showed that ZNF141 promoted the proliferation and viability of STS cells. In conclusion, ZNF-related models are useful as prognostic biomarkers, suggesting their potentials as therapeutic targets in STS. These findings will enable us to develop novel strategies treating STS, which will potentially improve outcomes of patients with STS.

Cardiac arrest prediction in smokers using enhanced Artificial Bee Colony algorithm with stacked autoencoder model

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced Artificial Bee Colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.

Gender differences in smoking-induced changes in the tumor immune microenvironment

Both gender and smoking are correlated with prevalence and outcomes in many types of cancers. Tobacco smoke is a known carcinogen through its genotoxicity but can also affect cancer progression through its effect on the immune system. In this study, we aim to evaluate the hypothesis that the effects of smoking on the tumor immune microenvironment will be influenced differently by gender using large-scale analysis of publicly available cancer datasets. We used The Cancer Genomic Atlas (TCGA) datasets (n = 2724) to analyze effects of smoking on different cancer immune subtypes and the relative abundance of immune cell types between male and female cancer patients. We further validated our results by analyzing additional datasets, including Expression Project for Oncology (expO) bulk RNA-seq dataset (n = 1118) and single-cell RNA-seq dataset (n = 14). Results of our study indicate that in female patients, two immune subtypes, C1 and C2, are respectively over and under abundant in smokers vs. never smokers. In males, the only significant difference is underabundance of the C6 subtype in smokers. We identified gender-specific differences in the population of immune cell types between smokers and never smokers in all TCGA and expO cancer types. Increased plasma cell population was identified as the most consistent feature distinguishing smokers and never smokers, especially in current female smokers based on both TCGA and expO data. Our analysis of existing single-cell RNA-seq data further revealed that smoking differentially affects the gene expression profile of cancer patients based on the immune cell type and gender. In our analysis, female and male smokers show different smoking-induced patterns of immune cells in tumor microenvironment. Besides, our results suggest cancer tissues directly exposed to tobacco smoke undergo the most significant changes, but all other cancer types are affected as well. Findings of current study also indicate that changes in the populations of plasma cells and their correlations to survival outcomes are stronger in female current smokers, with implications for cancer immunotherapy of women smokers. In conclusion, results of this study can be used to develop personalized treatment plans for cancer patients who smoke, particularly women smokers, taking into account the unique immune cell profile of their tumors.

Lifetime Exposure to Cigarette Smoke and Risk of Ovarian Cancer by T-cell Tumor Immune Infiltration

BACKGROUND

Exposure to cigarette smoke, particularly in early life, is modestly associated with ovarian cancer risk and may impact systemic immunity and the tumor immune response. However, no studies have evaluated whether cigarette smoke exposure impacts the ovarian tumor immune microenvironment.

METHODS

Participants in the Nurses' Health Study (NHS) and NHSII reported on early life exposure to cigarette smoke and personal smoking history on questionnaires (n = 165,760). Multiplex immunofluorescence assays were used to measure markers of T cells and immune checkpoints in tumor tissue from 385 incident ovarian cancer cases. We used Cox proportional hazards models to evaluate HRs and 95% confidence intervals (CI) for developing ovarian tumors with a low (<median) or high (≥median) immune cell percentage by cigarette exposure categories.

RESULTS

Women exposed versus not to cigarette smoke early in life had a higher risk of developing ovarian cancer with low levels of T cells overall (CD3+: HR: 1.54, 95% CI: 1.08-2.20) and recently activated cytotoxic T cells (CD3+CD8+CD69+: HR: 1.45, 95% CI: 1.05-2.00). These findings were not statistically significant at the Bonferroni-corrected P value of 0.0083. Adult smoking was not significantly associated with tumor immune markers after Bonferroni correction.

CONCLUSIONS

These results suggest early life cigarette smoke exposure may modestly increase risk of developing ovarian tumors with low abundance of total T cells and recently activated cytotoxic T cells.

IMPACT

Future research should focus on understanding the impact of exposures throughout the life course on the ovarian tumor immune microenvironment.

Lung cancer in never smokers: Tumor immunology and challenges for immunotherapy

Lung cancer is the second most common and the most lethal malignancy worldwide. It is estimated that lung cancer in never smokers (LCINS) accounts for 10-25% of cases, and its incidence is increasing according to recent data, although the reasons remain unclear. If considered alone, LCINS is the 7th most common cause of cancer death. These tumors occur more commonly in younger patients and females. LCINS tend to have a better prognosis, possibly due to a higher chance of bearing an actionable driver mutation, making them amenable to targeted therapy. Notwithstanding, these tumors respond poorly to immune checkpoint inhibitors (ICI). There are several putative explanations for the poor response to immunotherapy: low immunogenicity due to low tumor mutation burden and hence low MANA (mutation-associated neo-antigen) load, constitutive PD-L1 expression in response to driver mutated protein signaling, high expression of immunosuppressive factors by tumors cells (like CD39 and TGF-beta), non-permissive immune TME (tumor microenvironment), abnormal metabolism of amino acids and glucose, and impaired TLS (Tertiary Lymphoid Structures) organization. Finally, there is an increasing concern of offering ICI as first line therapy to these patients owing to several reports of severe toxicity when TKIs (tyrosine kinase inhibitors) are administered sequentially after ICI. Understanding the biology behind the immune response against these tumors is crucial to the development of better therapeutic strategies.

Inflammation in Preeclampsia: Genetic Biomarkers, Mechanisms, and Therapeutic Strategies

Objective

Preeclampsia is a common and serious complication of pregnancy, posing a threat to maternal and fetal safety due to the lack of effective biomarkers and treatment strategies. This study aimed to identify potential biomarkers that can be used to predict preeclampsia and identify the molecular mechanisms of preeclampsia pathogenesis and drug prediction at the transcriptome level.

Methods

We analyzed differential expression genes (DEGs) in preeclampsia and non-preeclampsia groups in the GSE75010 dataset, cross-linking with extracted inflammatory response-related genes to obtain differentially expressed inflammation-related genes (DINRGs). Enrichment analysis and protein-protein interaction (PPI) networks were constructed to understand the functions and enrichment pathways. Machine learning models were used to identify key genes associated with preeclampsia and build a nomogram in the training set, which was validated in the validation set. The R package RcisTarget was used to predict transcription factors, and Cytoscape was used to construct miRNA-mRNA pathways, which could identify the molecular mechanisms. Then, we conducted molecular docking of the obtained key genes INHBA (inhibin subunit beta A), OPRK1 (opioid receptor kappa 1), and TPBG (trophoblast glycoprotein), as well as predicted transcription factors with drug molecules. Additionally, the CIBERSORT method explored the differences in immune cell infiltration between preeclampsia and non-preeclampsia samples based on the GSE75010 dataset.

Results

A total of 69 DINRGs associated with preeclampsia patients were screened. INHBA, OPRK1, and TPBG were the key genes based on machine learning models. A nomogram for prediction was further constructed, and the receiver operating curves (ROCs) showed good performance. Based on the transcriptome level of key genes, we proposed that RELA-miR-548K/miR-1206-TPBG may be a potential RNA regulatory pathway regulating the progression of early preeclampsia. Molecular docking suggested the effectiveness of curcumin in the treatment of preeclampsia. Additionally, regulatory T cells (Tregs) and resting mast cells were significantly different between the two groups.

Conclusion

In summary, we identified three key inflammation-associated genes, namely INHBA, OPRK1, and TPBG, which can be used as potential genetic biomarkers for preeclampsia prediction and treatment, and established a nomogram as a predictive model. Additionally, we provided insights into the mechanisms of preeclampsia development at the transcriptome level and performed corresponding drug predictions.

Development of six immune-related lncRNA signature prognostic model for smoking-positive lung adenocarcinoma

BACKGROUND

Smoking is one of the most hazardous risk factors for the development of lung adenocarcinoma (LUAD). Many survival and prognosis-related biomarkers were discovered using database mining. However, the precision of immune-related long noncoding RNAs (lncRNAs) predictions is insufficient. We identified a novel signature to improve the estimate of smoking-related LUAD prognosis.

METHODS

The Cancer Genome Atlas database (TCGA) was used to obtain the LUAD lncRNA expression profiles. The smoking-related LUAD cohort was randomly split into discovery and validation cohorts. To determine the risk score, use the LASSO Cox regression technique on the prognostic immune-related lncRNA. The risk signature has been developed.

RESULTS

A total of 643 immune-related lncRNAs were identified as potential candidates for a risk signature. Finally, six immune-related lncRNAs (AL359915.2, AP000695.1, HSPC324, TGFB2-AS1, AC026355.1, and AC002128.2) were identified and used to carry out risk signature, which showed a close association with overall survival in the discovery cohort. We classified patients as high risk or low risk based on a median risk score of 1.0783. In the discovery cohort, overall survival was marginally longer in the low-risk group than in the high-risk category (p = 2.28e08). The area under the curves (AUC) for 1-, 3-, and 5-year survival was 0.67, 0.7, and 0.82, respectively. Furthermore, we successfully validated and combined cohorts using this risk profile. We discovered a strong positive connection between HSPC324 and VIPR1 as a possible novel biomarker for smoking-related LUAD development in our study.

CONCLUSIONS

Our research has established a six immune-lncRNA signature that may be used to predict the prognosis of smoking-related LUAD with great accuracy.

Impacts of Cigarette Smoking on the Tumor Immune Microenvironment in Esophageal Squamous Cell Carcinoma

Objective: Cigarette smoking is a carcinogenic factor for esophageal cancer and evidence also indicates its effects on tumor microenvironment in patients with esophageal squamous cell carcinoma (ESCC). Materials and Methods: In our study, we demonstrated nine immune infiltrating cells and markers in non-smokers and smokers of 189 non-drinking ESCC patients with multiplex fluorescent immunohistochemistry (mflHC) staining and multispectral imaging. The impacts of cigarette smoking on tumor microenvironment and patient prognosis were also analyzed. Results: Among 189 ESCC patients of non-drinker, 86 patients was current smokers, while 34 males and 59 females were non-smokers and 10 former-smokers. Among 34 male non-smokers and 83 smokers, distinct immune infiltrating cells, with increased DCs in stromal regions (P=0.033), elevated infiltration of Treg cells in intraepithelial regions (P=0.010) and reduced activate cytotoxic T lymphocytes (aCTLs) in both intraepithelial (P=0.021) and stromal regions (P=0.017), were observed in tumor specimens of smoking males, implying an immune suppressed response during cigarette smoke exposure. For smoking characters, the level of stromal tumor-associated macrophages (TAMs) infiltration was correlated with smoking year after age adjusted (r_s =0.352, P=0.002). Though cigarette smoking did not alter the expression of programmed death ligand 1 (PD-L1) in epithelial cells or TAMs in tumor specimens, higher expression of PD-L1 predicted a worse survival in non-smokers but not smokers. Conclusions: Our findings indicated smoking may impair T cell-mediated immune response and supported the possible impacts of cigarette smoking in PD-L1 related research and therapy of ESCC.

MicroRNA-106a-5p promotes the proliferation, autophagy and migration of lung adenocarcinoma cells by targeting LKB1/AMPK

It has previously been reported that lung cancer has the highest morbidity and mortality rate worldwide; however, the pathogenesis underlying lung cancer has not been fully elucidated. The aim of the present was primarily to assess the influence of microRNA (miR)-106a-5p on the biological behaviors of lung cancer cells. In the present study, bioinformatics analysis was used to analyze the expression characteristics of miR-106a-5p and its relationship with the prognosis of patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas. A dual luciferase reporter assay was performed to verify the binding of miR-106a-5p and liver kinase B1 (LKB1). The Cell Counting Kit-8, colony formation and Transwell assays were utilized to detect cell viability, proliferation and migration, respectively. Protein and RNA expression levels were examined by western blotting and reverse transcription-quantitative PCR analysis, respectively. It was observed that miR-106a-5p was highly expressed in LUAD and associated with poor prognosis. miR-106a-5p promoted the proliferation and migration of LUAD cells, and inhibited autophagy. By contrast, LKB1 inhibited cell proliferation and migration, promoted autophagy and blocked the cancer-promoting effects of miR-106a-5p. Overexpression of miR-106a-5p inhibited the phosphorylation of AMP-activated protein kinase (AMPK) and tuberin (TSC2), and promoted the phosphorylation of mTOR. By contrast, overexpression of LKB1 blocked the promotion of mTOR phosphorylation, and the inhibition of AMPK and TSC2 phosphorylation caused by miR-106a-5p. In summary, the results of the present study indicated that miR-106a-5p regulated the phosphorylation of the AMPK pathway by targeting LKB1, and was involved in the proliferation, migration and autophagy of LUAD cells.

Mast Cells Resting-Related Prognostic Signature in Hepatocellular Carcinoma

The immune microenvironment of liver cancer is of great significance for the treatment of liver cancer. After evaluating the content of mast cells resting in the transcriptome data of The Cancer Genome Atlas database by CIBERSORT analysis, this study aimed to group the samples according to the content of mast cells resting in different samples to find the differentially expressed genes in the two groups. Significant prognostic differences were found between high and low mast cells resting infiltration groups. The prognostic model was constructed according to the differentially expressed genes. The model was validated using external independent datasets. The results revealed that the constructed model was reliable. It could well distinguish the prognostic differences of patients in different characteristic groups. The high-risk group was mainly concentrated in metabolic pathways. The risk score of this model was closely related to some immune cells, immune function, and immune checkpoints. Therefore, this model may provide new ideas for immunotherapy of hepatocellular carcinoma.

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.

The Effect of Smoking on the Immune Microenvironment and Immunogenicity and Its Relationship With the Prognosis of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer

Background: The emergence of immune checkpoint inhibitors (ICIs) has opened a new chapter for the treatment of non-small cell lung cancer (NSCLC), and the best beneficiaries of ICI treatment are still being explored. Smoking status has been repeatedly confirmed to affect the efficacy of ICIs in NSCLC patients, but the specific mechanism is still unclear.

Methods: We performed analysis on the Memorial Sloan Kettering Cancer Center (MSKCC) clinical NSCLC cohort receiving ICI treatment, The Cancer Genome Atlas (TCGA) Pan-Lung Cancer cohort, and Gene Expression Omnibus (GEO) database GSE41271 lung cancer cohort that did not receive ICI treatment, including survival prognosis, gene mutation, copy number variation, immunogenicity, and immune microenvironment, and explored the impact of smoking status on the prognosis of NSCLC patients treated with ICIs and possible mechanism. In addition, 8 fresh NSCLC surgical tissue samples were collected for mass cytometry (CyTOF) experiments to further characterize the immune characteristics and verify the mechanism.

Result: Through the analysis of the clinical data of the NSCLC cohort treated with ICIs in MSKCC, it was found that the smokers in NSCLC receiving ICI treatment had a longer progression-free survival (HR: 0.69, 95% CI: 0.49–0.97, p = 0.031) than those who never smoked. Further analysis of the TCGA and GEO validation cohorts found that the differences in prognosis between different groups may be related to the smoking group’s higher immunogenicity, higher gene mutations, and stronger immune microenvironment. The results of the CyTOF experiment further found that the immune microenvironment of smoking group was characterized by higher expression of immune positive regulatory chemokine, and higher abundance of immune activated cells, including follicular helper CD4⁺ T cells, gamma delta CD4⁺ T cells, activated DC, and activated CD8⁺ T cells. In contrast, the immune microenvironment of non-smoking group was significantly enriched for immunosuppressive related cells, including regulatory T cells and M2 macrophages. Finally, we also found highly enriched CD45RA^highCD4⁺ T cells and CD45RA^highCD8⁺ T cells in the non-smoking group.

Conclusion: Our research results suggest that among NSCLC patients receiving ICI treatment, the stronger immunogenicity and activated immune microenvironment of the smoking group make their prognosis better.

ARID1A, ARID1B, and ARID2 Mutations Serve as Potential Biomarkers for Immune Checkpoint Blockade in Patients With Non-Small Cell Lung Cancer

Worldwide, non-small cell lung cancer (NSCLC) has the highest morbidity and mortality of all malignancies. The lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy, with the rapid development of immune checkpoint inhibitors (ICIs) in recent years. The human switch/sucrose nonfermentable (SWI/SNF) chromatin-remodeling complex has been reported to be recurrently mutated in patients with cancer, and those with SWI/SNF mutations have been reported to be sensitive to ICIs. Six reported cohorts, a total of 3416 patients, were used to analyze the mutation status of ARID1A, ARID1B, ARID2 and SMARCA4 in patients with NSCLC and the effect of mutations on prognosis after ICIs. Finally, a nomogram was established to guide the clinical use of ICIs. The results show that patients with NSCLC who have ARID1A, ARID1B, and ARID2 mutations of the SWI/SNF complex were more likely to benefit from ICI therapy.

Impact of Smoking History on Response to Immunotherapy in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis

Objectives

To evaluate the impact of smoking history on the clinical benefit of immunotherapy in patients with non-small cell lung cancer (NSCLC).

Methods

Twenty-three randomized clinical trials and seven real-world studies were included in this meta-analysis. Hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS) and progression-free survival (PFS) and odds ratios for the overall response rate (ORR) were extracted. A fixed-effects or random-effects model was applied to obtain pooled estimates.

Results

Data from 16 high-quality trials involving 10,643 NSCLC patients receiving either immunotherapy or chemotherapy/placebo enabled direct comparison of the survival impact of smoking. Anti-PD-1/PD-L1/CTLA-4 immunotherapy was found to significantly prolong OS and PFS as compared to chemotherapy/placebo in smokers (HR for OS, 0.76 [0.69-0.83], P<0.00001; HR for PFS, 0.65 [0.56-0.75], P<0.00001), and these trends were less or not significant in non-smokers (HR for OS, 0.91 [0.78-1.06], P=0.25; HR for PFS, 0.68 [0.45-1.03], P=0.07). Consistent results were obtained for the first-line or second/third-line use of immunotherapy and for non-squamous NSCLC patients only. Furthermore, the data from 7 trials and 7 real-world studies involving 4,777 patients receiving immunotherapy allowed direct comparison of therapeutic outcomes between smokers and non-smokers. Prolonged OS (HR 0.86 [0.75-0.99], P=0.04) and PFS (HR 0.69 [0.60-0.81], P<0.0001) and a higher response rate (ORR 1.20 [0.94-1.53], P=0.15) were observed in smokers compared to non-smokers receiving immunotherapy.

Conclusions

Immunotherapy was found to have a greater benefit in NSCLC patients with a smoking history than in those who had never smoked.

Screening for Lung Cancer in Individuals Who Never Smoked: An International Association for the Study of Lung Cancer Early Detection and Screening Committee Report

Screening with low-dose computed tomography of high-risk individuals with a smoking history reduces lung cancer mortality. Current screening guidelines and eligibility criteria can miss more than 50% of lung cancers, and in some geographic areas, such as East Asia, a large proportion of the missed lung cancers are in never-smokers. Although randomized trials revealed the benefits of screening for people who smoke, these trials generally excluded never-smokers. Thus, the feasibility and effectiveness of lung cancer screening of individuals who never smoked are uncertain. Several known and suspected risk factors for lung cancers in never-smokers such as exposure to secondhand smoke, occupational carcinogens, radon, air pollution, and pulmonary diseases, such as chronic obstructive pulmonary disease and interstitial lung diseases, and intrinsic factors, such as age, are well noted. In this regard, knowledge of risk factors may make possible quantification and prediction of lung cancer risk in never smokers. It is worth considering if and how never smokers could be included in population-based screening programs. As the implementation of these programs is challenging in many countries owing to multiple factors and the epidemiologic differences by global regions, these issues will need to be evaluated in each country taking into account various factors, including accuracy of risk assessment and cost-effectiveness of screening in never smokers. This report aims to outline current knowledge on risk factors for lung cancer in never smokers to propose research strategies for this topic and initiate a broader discussion on lung cancer screening of never smokers. Similar considerations can be made in current and ex-smokers, which do not fulfill the current screening inclusion criteria, but otherwise are at increased risk. Although screening of never smokers may in the future be effectively conducted, current evidence to support widespread implementation of this practice is lacking.

BAI1 nuclear expression reflects the survival of nonsmoking non-small cell lung cancer patients

Background

Smoking‐ and nonsmoking‐associated lung cancers have different mechanisms of carcinogenesis. We divided non‐small cell lung cancer (NSCLC) patients into nonsmoking and smoking groups with the aim of trying to understand the utility of brain‐specific angiogenesis inhibitor 1 (BAI1) expression in the separate groups.

Methods

Clinicopathological data were obtained from 148 patients who had undergone surgery for NSCLC of the lung. Tissue microarray blocks were made of samples from NSCLC patients. Two pathologists graded the intensity of BAI1 expression as high or low expression in the cancer cells of patients in the smoking and nonsmoking groups.

Results

NSCLC nonsmokers with higher BAI1 nuclear expression had poor disease‐specific survival (DSS) (hazard ratio: 2.679; 95% confidence interval [CI]:1.022–7.022, p = 0.045). The Kaplan–Meier survival curve confirmed that higher BAI1 expression was significantly associated with poor DSS (p = 0.034) in the nonsmoking group.

Conclusions

We divided NSCLC patients into nonsmoking and smoking groups and found that nuclear BAI1 expression was related to patient survival in nonsmoking NSCLC patients. We suggest BAI1 expression as a predictive marker of nonsmoking‐associated NSCLC and recommend that it be evaluated as an AJCC staging criterion in the future.

A Novel Signature for Predicting Prognosis of Smoking-Related Squamous Cell Carcinoma

Tobacco smoking is an established risk factor for squamous cell carcinoma (SCC). We obtained smoking-related SCC, including cervical SCC (CSCC), esophageal SCC (ESCC), head and neck SCC (HNSC), and lung SCC (LUSC), from The Cancer Genome Atlas (TCGA) database to investigate the association between smoking status (reformed and current smoking) and prognosis. We found that reformed smokers had a better prognosis than current smokers in CSCC (p = 0.003), HNSC (p = 0.019), and LUSC (p < 0.01) cohorts. Then, we selected LUSC cohorts as the training cohort and other SCC cohorts as the test cohorts. Function analysis revealed that homologous recombination (HR) was the most significant pathway involved in smoking-induced LUSC. Moreover, the effect of cross-talk between the smoking status and HR deficiency (HRD) on the prognosis was further evaluated, revealing that quitting smoking with high HRD scores could significantly improve patients’ prognosis (p < 0.01). To improve prognosis prediction and more effectively screen suitable populations for platinum drugs and poly-ADP-ribose polymerase (PARP) inhibitors, we constructed a risk score model using smoking- and HRD-related genes in LUSC. The risk score model had high power for predicting 2-, 3-, and 5-year survival (p < 0.01, AUC = 0.67, 0.66, and 0.66). In addition, the risk scores were an independent risk factor for LUSC (HR = 2.34, 95%CI = 1.70–3.23). The practical nomogram was also built using the risk score, smoking status, and other clinical information with a good c-index (0.72, 95%CI = 0.70–0.74). Finally, we used other TCGA SCC cohorts to confirm the reliability and validity of the risk score model (p < 0.01 and AUC > 0.6 at 2, 3, and 5 years in CSCC and HNSC cohorts). In conclusion, the present study suggested that smoking cessation should be a part of smoking-related SCC treatment, and also provided a risk score model to predict prognosis and improve the effectiveness of screening the platinum/PARP population.

The construction and analysis of tumor-infiltrating immune cells and ceRNA networks in metastatic adrenal cortical carcinoma

Purpose: To construct and analyze tumor-infiltrating immune cell and ceRNA (competitive endogenous RNA) networks in metastatic adrenal cortical carcinoma (ACC).

Methods: A ceRNA network was established to identify the ceRNAs involved in metastasis of ACC based on 92 samples from TCGA, including 18 cases of metastasis and 74 cases of non-metastatic primary tumors. And the algorithm “cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT)” was used to quantify the proportion of immune cells in ACC. In addition, predictive nomograms based on the types of important immune cells or ceRNAs were constructed to predict ACC prognosis. Moreover, we evaluated the relationships between metastatic ACC-specific immune cells and ceRNA networks to identify the potential immune gene characteristics.

Results: Ten prognostic biomarkers were identified as key members of the ceRNA network and three tumor-infiltrating immune cells were identified by CIBERSORT algorithm. Some important co-expression patterns between immune cells and ceRNAs network indicate significant correlation between Macrophages M0 and hsa-miR-130b-3p (P < 0.001), Macrophages M0 and H2AFX (P = 0.003).

Conclusions: The present study inferred that the metastasis-related ceRNAs of H2AFX, hsa-miR-130b-3p and Macrophages M0 might play important roles in ACC metastasis.

Stromal FAP is an independent poor prognosis marker in non-small cell lung adenocarcinoma and associated with p53 mutation

OBJECTIVES

Fibroblasts regulate tumor growth and immune surveillance. Here, we study FAP, PDGFβR and α-SMA fibroblast markers in a well-annotated clinical cohort of non-small-cell lung cancer (NSCLC) for analyses of associations with immune cell infiltration, mutation status and survival.

MATERIALS AND METHODS

A well-annotated NSCLC cohort was subjected to IHC analyses of stromal expression of FAP, PDGFβR and α-SMA and of stromal CD8 density. Fibroblast markers-related measurements were analyzed with regard to potential associations with CD8 density, cancer genetic driver mutations, survival and PD-L1 expression in the whole NSCLC cohort and in subsets of patients.

RESULTS

High stromal FAP expression was identified as an independent poor prognostic marker in the whole study population (HR 1.481; 95 % CI, 1.012-2.167, p = 0.023) and in the histological subset of adenocarcinoma (HR 1.720; 95 % CI, 1.126-2.627, p = 0.012). Among patients with adenocarcinoma, a particularly strong association of FAP with poor survival was detected in patients with low stromal CD8 infiltration, and in other subpopulations identified by specific clinical characteristics; elderly patients, females, non-smokers and patients with normal ECOG performance status. α-SMA expression was negatively associated with CD8 infiltration in non-smokers, but none of the fibroblast markers expression was associated with CD8 density in the whole study population. Significant associations were detected between presence of p53 mutations and high α-SMA (p = 0.003) and FAP expression (p < 0.001).

CONCLUSION

The study identifies FAP intensity as a candidate independent NSCLC prognostic biomarker. The study also suggests continued analyses of the relationships between genetic driver mutations and the composition of tumor stroma, as well as continued probing of marker-defined fibroblasts as NSCLC subset-specific modifiers of immune surveillance and outcome.

Current Knowledge about Mechanisms of Drug Resistance against ALK Inhibitors in Non-Small Cell Lung Cancer

Simple Summary

Lung cancer is a devastating disease, with non-small cell lung cancer (NSCLC) being the most common subtype. With the development of novel targeted therapeutics, survival times have continuously improved over the past two decades. In a subset of NSCLC, gene rearrangements of the anaplastic lymphoma kinase (ALK), or gene fusions involving ALK, can be determined. ALK-inhibitors are increasingly used as a standard of care in patients with ALK gene abnormalities, and can also be administered as first-line treatment in advanced-stage NSCLC. However, over the disease course, cancers tend to develop resistance mechanisms, warranting the switch from first- to second- or third-generation ALK inhibitors. With this literature review, we aim to give a concise overview about these resistance mechanisms, and what kind of sequential treatment may be feasible if therapy failure upon an ALK inhibitor occurs.

Abstract

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer subtypes. Two to seven percent of NSCLC patients harbor gene rearrangements of the anaplastic lymphoma kinase (ALK) gene or, alternatively, harbor chromosomal fusions of ALK with echinoderm microtubule-associated protein-like 4 (EML4). The availability of tyrosine kinase inhibitors targeting ALK (ALK-TKIs) has significantly improved the progression-free and overall survival of NSCLC patients carrying the respective genetic aberrations. Yet, increasing evidence shows that primary or secondary resistance to ALK-inhibitors during the course of treatment represents a relevant clinical problem. This necessitates a switch to second- or third-generation ALK-TKIs and a close observation of NSCLC patients on ALK-TKIs during the course of treatment by repetitive molecular testing. With this review of the literature, we aim at providing an overview of current knowledge about resistance mechanisms to ALK-TKIs in NSCLC.

Immune Resistance in Lung Adenocarcinoma

Lung cancer is the leading cancer killer worldwide, imposing grievous challenges for patients and clinicians. The incidence of lung adenocarcinoma (LUAD), the main histologic subtype of lung cancer, is still increasing in current-, ex-, and even non-smokers, whereas its five-year survival rate is approximately 15% as the vast majority of patients usually present with advanced disease at the time of diagnosis. The generation of novel drugs targeting key disease driver mutations has created optimism for the treatment of LUAD, but, as these mutations are not universal, this therapeutic line benefits only a subset of patients. More recently, the advent of targeted immunotherapies and their documented clinical efficacy in many different cancers, including LUAD, have started to change cancer management. Immunotherapies have been developed in order to overcome the cancer's ability to develop mechanisms of immune resistance, i.e., to adapt to and evade the host inflammatory and immune responses. Identifying a cancer's immune resistance mechanisms will likely advance the development of personalized immunotherapies. This review examines the key pathways of immune resistance at play in LUAD and explores therapeutic strategies which can unleash potent antitumor immune responses and significantly improve therapeutic efficacy, quality of life, and survival in LUAD.

The Obesity Paradox in Lung Cancer: Associations With Body Size Versus Body Shape

Background

The association between obesity and lung cancer (LC) remains poorly understood. However, other indices of obesity on the basis of body shape instead of body size have not been examined yet. The aim of this study was to evaluate the association between different indices of body size and body shape and the risk of LC. In particular, this study examined the association between A Body Shape Index, a more precise indicator of abdominal fat than traditional anthropometric measures, and the risk of LC.

Methods

In the prospective cohort the Rotterdam Study, we analysed data of 9,689 participants. LC diagnoses were based on medical records and anthropometric measurements were assessed at baseline. Cox-regression analyses with corresponding Hazard Ratios were used to examine the association between the anthropometric measurements and the risk of LC with adjustment for potential confounders. Potential non-linear associations were explored with cubic splines using the Likelihood ratio (LR) test.

Results

During follow-up, 319 participants developed LC. Body mass Index (BMI) was inversely associated with the risk of lung cancer (HR 0.94, 95% CI: 0.91–0.97) and persisted after excluding lung cancer cases during the first 10 years of follow-up. There was evidence for a non-linear association between BMI and the risk of lung cancer (0,04, df = 1), which indicated that the inverse association between BMI and lung cancer was mainly present in non-obese participants. Waist circumference (WC) (HR 1.03 95% CI: 1.01–1.05), Waist-to-Hip Ratio (WHR) (HR 1.23 95% CI: 1.09–1.38) and ABSI (A Body Shape Index) (HR 1.17 95% CI: 1.05–1.30) were positively and linearly associated with the risk of lung cancer.

Conclusions

Body shape rather than body size may be an important risk indicator of LC. Future research should focus on the role of visceral fat and the risk of LC as well as the underlying mechanisms.

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma

Background

Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history.

Methods

The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical characteristics was explored. According to the level of immune cell infiltration, LUSC patients with smoking history were divided into high or low group to screen the differentially expressed lncRNAs and mRNAs. The prediction of target genes was performed by miRanda. Finally, the prognostic value of a certain signature was confirmed in an independent dataset.

Results

Higher abundance of tumor-infiltrating T follicular helper (Tfh) cells together with a lower abundance of resting memory CD4 T cells had been found in LUSC current reformed smokers for ≤15 years and current smoking patients. Moreover, Tfh cell infiltration was not only associated with better overall survival (OS) but also varied from different degrees of TNM stage. Low expression of lncRNA PWRN1 and its potential regulating genes DMRTB1, PIRT, APOBEC1, and ZPBP2 were associated with better OS. Combining PWRN1 and four regulating genes as a signature, patients with higher-level expression of the signature had shorter survival time in not only the TCGA but also in the GEO dataset.

Conclusions

It was found that Tfh cells presented higher infiltration in LUSC current reformed smokers for ≤15 years and current smokers, while resting memory CD4 T cells had lower infiltration. The signature consisting of PWRN1 as well as its predicted targeted mRNAs was dysregulated in different levels of Tfh cell infiltration and might indicate patients' OS.

Integrative analysis of genomic alteration, immune cells infiltration and prognosis of lung squamous cell carcinoma (LUSC) to identify smoking-related biomarkers

Lung squamous cell carcinoma (LUSC) is the most common histologic type of smoking-related non-small cell lung cancer (NSCLC). However, there are no identified potential biomarkers for smoking-related LUSC diagnosis and prognosis. Especially, the characteristics of genetic alteration and tumor microenvironment induced by cigarette smoking remain unknown. Here, we performed integrative analysis of 463 LUSC with smoking history information from The Cancer Genome Atlas (TCGA). Non-smokers had the best prognosis, and current reformed smokers had better overall survival (OS) than current smokers in all and stage I-II cohort. Then, pathway enrichment analysis might suggest that smoking may play a role in regulating tumor metabolism and invasion and metastasis via those pathways. We constructed an eight-gene signature and identified WNT7A, Solute carrier-7A5 (SLC7A5) and Brain‑type glycogen phosphorylase (PYGB), which may be served as biomarkers related to the smoking. Notably, the single copy deletion of WNT7A and SLC17A5 and the low-level amplification of PYGB may be related to the epigenetic mechanism of smoking on tumorigenesis. We also estimated the relative proportion of 24 immune cell subtypes within tumor microenvironment in different smoking status. Interestingly, we found NK cells activated, NK cells resting and endothelial cells might play an important role in immunologic dysfunction and harmful tumor microenvironment induced by cigarette smoking. Our research has identified potential biomarkers for smoking-related LUSC diagnosis and prognosis, which would help to further understand the pathogenesis of LUSC.

Genetic and microenvironmental differences in non-smoking lung adenocarcinoma patients compared with smoking patients

Background

Non-smoking-related lung adenocarcinoma (LUAD) has its own characteristics. Genetic and microenvironmental differences in smoking and non-smoking LUAD patients were analyzed to elucidate the oncogenesis of non-smoking-related LUAD, which will improve our understanding of the underlying molecular mechanism and be of clinical use in the future.

Methods

The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) databases were used for clinical and genomic information. Various bioinformatics tools were used to analyze differences in somatic mutations, RNA and microRNA (miRNA) expression, immune infiltration, and stemness indices. GO, KEGG, and GSVA analyses were performed with R. A merged protein-protein interaction (PPI) network was constructed and analyzed. A miRNA-differentially expressed gene network was constructed with miRNet. qRT-PCR was used for validation of 4 most significantly differently expressed genes and 2 miRNAs in tumor samples obtained from 20 pairs of non-smoking and smoking patients.

Results

Five hundred and one patients with LUAD were obtained, including 210 in the non-smoking group and 292 in the smoking group. A total of 174 significantly altered somatic mutations were detected, including mutations in tumor protein p53 and epidermal growth factor receptor, which were downregulated in non-smoking-related LUAD. At the RNA level, 231 significantly differentially expressed genes were obtained; 124 were upregulated and 107 downregulated in the non-smoking group. GSVA analysis revealed 42 significant pathways. Other functional and enrichment analyses of somatic mutations and RNA expression levels revealed that these genes were significantly enriched in receptor activity regulation and receptor binding. Differences in microenvironments including immune infiltration (e.g., CD8⁺ T cells and resting mast cells) and stemness indices were also found between groups. A 79-pair interaction was found between differentially expressed genes and miRNAs, of which miR-335-5p and miR-34a-5p were located in the center. Twenty-one genes, including vitronectin, neurotensin, and neuronatin, were differentially expressed in both non-smoking LUAD patients and DMSO-treated A549 cells. And the different expression of neurotensin, neuronatin, trefoil factor family2, regenerating family member 4, miR-377-5p, miR-34a were verified with the same tendency in our own samples.

Conclusions

Non-smoking LUAD patients, compared to smokers, have different characteristics in terms of somatic mutation, gene, and miRNA expression and the microenvironment, indicating a diverse mechanism of oncogenesis.

α5-nAChR and survivin: Two potential biological targets in lung adenocarcinoma

Recent studies have shown that the overexpression of α5 nicotinic acetylcholine receptor (α5-nAChR) is associated with nicotine-related lung carcinogenesis. Survivin is one of the biomarkers of a worse prognosis for smoking-related lung cancer. The aim of this study is to investigate the association of α5-nAChR, survivin, and clinical outcomes in lung adenocarcinoma (LUAD). We analyzed the expression level and correlation of CHRNA5 (encoding α5-nAChR) and BIRC5 (encoding survivin) in LUAD with The Cancer Genome Atlas data set. The relationship between overall survival (OS) and the expression of CHRNA5 or/and BIRC5 was evaluated by the Kaplan-Meier method and Cox proportional hazards model. Moreover, our results showed that the expression of α5-nAChR mediated survivin expression in lung cancer cells and in lung tumor xenografts. Relationships between the expression of α5-nAChR and/or survivin with clinical-pathological characteristics were analyzed using LUAD tissue samples. The results showed that expression of α5-nAChR was correlated with survivin expression in vitro and in vivo. The group coexpressing α5-nAChR and survivin had a worse prognosis than other subgroups in LUAD (p < .05). In conclusion, ascertaining the expression of both α5-nAChR and survivin provides a better measure of prognosis for LUAD patients. The combined inhibition of α5-nAChR and survivin may be a promising multitargeted gene therapeutic strategy in LUAD diagnosis.

The benefits of smoking cessation on survival in cancer patients by integrative analysis of multi-omics data

Few studies have examined the association between smoking status (including former smokers) at diagnosis and overall survival among cancer patients. We aimed to assess the benefits of quitting smoking on cancer prognosis in cohorts of cancer patient smokers obtained from the Cancer Genome Atlas (TCGA) database. Hazard ratios (HR) were calculated to evaluate smoking behavior at cancer diagnosis (reformed smokers vs. current smokers) in association with overall survival using multivariate‐adjusted Cox regressions analysis. According to our analyses, quitting smoking was the independent protective factor for overall survival in lung squamous cell carcinoma (LUSC) (HR = 0.67, 95% CI = 0.48–0.94). Comprehensive analysis of multicomponent data across reformed and current smokers identified a total of 85 differential expressed genes (DEGs) affected by different modes of genetic and epigenetic regulation, potentially representing cancer drivers in smokers. Moreover, we provided a smoking‐associated gene expression signature, which could evaluate the true effect on prognosis with high power (HR = 1.70, 95% CI = 1.19–2.43, AUC = 0.65, 0.67, and 0.70 for 2‐, 3‐, and 5‐year survival, respectively). This signature was also applicable in other smoking‐related cancers, including bladder urothelial carcinoma (HR = 1.70, 95% CI = 1.01–2.88), cervical carcinoma (HR = 5.69, 95% CI = 1.37–23.69), head and neck squamous cell carcinoma (HR = 1.97, 95% CI = 1.41–2.76), lung adenocarcinoma (HR = 1.73, 95% CI = 1.16–2.57), and pancreatic adenocarcinoma (HR = 4.28, 95% CI = 1.47–12.47). In conclusion, this study demonstrates that quitting smoking at diagnosis decreases risk of death in cancer patients. We also provide a smoking‐associated gene expression signature to evaluate the effect of smoking on survival. Lastly, we suggest that smoking cessation could comprise a part of cancer treatment to improve survival rates of cancer patients.

Improved Prognostic Prediction in Never-Smoker Lung Cancer Patients by Integration of a Systemic Inflammation Marker with Tumor Immune Contexture Analysis

Almost 25% of lung cancers (LCs) occur in never-smokers. LC inflammatory profile, based on plasma C-reactive protein levels (CRP), predicts mortality, independently by smoking-status. We hypothesized that: CRP could be associated with tumor immune contexture (TIC) in never-smokers and both these two parameters may improve their prognosis. Sixty-eight never-smokers LC patients with high or low CRP were selected. The programmed cell death protein 1 (PD-1) and its ligand (PD-L1), the human leukocyte antigens (HLA-DR and HLA-I), CD8, CD4, CD3, CD33, CD163, and CD68 were evaluated by immunohistochemistry on surgical samples given TIC evaluation. The classification model based on TIC scores was generated by Classification and Regression Tree analysis. Tumor mutational burden was evaluated by targeted next-generation sequencing. Exclusively high CRP (H-CRP) subset showed PD-L1 expression in 35% of LC as well as lower HLA-I and HLA-DR in their stromal cells. CD3, CD4, CD8, HLA-I, HLA-DR tumor cells staining were associated with a "low inflammatory profile" subset. CRP and LC immune profiles drive clinical outcome: 5-year survival 88% against 8% was associated with low and high-risk profiles (p < 0.0001). Clinical outcome prediction in never-smoker LC patients may be improved by both CRP and tumor immune contexture evaluation.

Mast cell-based molecular subtypes and signature associated with clinical outcome in early-stage lung adenocarcinoma

Mast cells are a major component of the immune microenvironment in tumour tissues and modulate tumour progression by releasing pro‐tumorigenic and antitumorigenic molecules. Regarding the impact of mast cells on the outcomes of patients with lung adenocarcinoma (LUAD) patient, several published studies have shown contradictory results. Here, we aimed at elucidating the role of mast cells in early‐stage LUAD. We found that high mast cell abundance was correlated with prolonged survival in early‐stage LUAD patients. The mast cell‐related gene signature and gene mutation data sets were used to stratify early‐stage LUAD patients into two molecular subtypes (subtype 1 and subtype 2). The neural network‐based framework constructed with the mast cell‐related signature showed high accuracy in predicting response to immunotherapy. Importantly, the prognostic mast cell‐related signature predicted the survival probability and the potential relationship between TP53 mutation, c‐MYC activation and mast cell activities. The meta‐analysis confirmed the prognostic value of the mast cell‐related gene signature. In summary, this study might improve our understanding of the role of mast cells in early‐stage LUAD and aid in the development of immunotherapy and personalized treatments for early‐stage LUAD patients.

Inherited lung cancer: a review

Lung cancer is the most common cancer worldwide and has high rates of mortality. The major risk factor associated with this disease is tobacco smoke, but approximately 10%-25% of all lung cancer cases occur in patients who have never smoked. Data suggest that lung cancer in never-smokers has a different molecular profile, tumour microenvironment and epidemiology than that in smokers. Several risk factors have been associated with its occurrence, and the possibility of inherited predisposition is becoming clearer. A better understanding of this disease is essential for the future development of personalised screening, diagnosis and treatment approaches, with consequent reduction of mortality. In this review, we discuss historical studies of lung cancer in never-smokers and the currently available evidence of inherited predisposition to this disease.

Effects of Inhaled Tobacco Smoke on the Pulmonary Tumor Microenvironment

Tobacco smoke is a multicomponent mixture of chemical, organic, and inorganic compounds, as well as additive substances and radioactive materials. Many studies have proved the carcinogenicity of various of these compounds through the induction of DNA adducts, mutational potential, epigenetic changes, gene fusions, and chromosomal events. The tumor microenvironment plays an important role in malignant tumor formation and progression through the regulation of expression of key molecules which mediate the recruitment of immune cells to the tumor site and subsequently regulate tumor growth and metastasis. In this chapter, we discuss the effects of inhaled tobacco smoke in the tumor microenvironment of the respiratory tract. The mechanisms underlying these effects as well as their link with tumor progression are analyzed.

Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma

Smoking is one of the most important factors associated with the development of lung cancer. However, the signaling pathways and driver genes in smoking-associated lung adenocarcinoma remain unknown. The present study analyzed 433 samples of smoking-associated lung adenocarcinoma and 75 samples of non-smoking lung adenocarcinoma from the Cancer Genome Atlas database. Gene Ontology (GO) analysis was performed using the Database for Annotation, Visualization and Integrated Discovery and the ggplot2 R/Bioconductor package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the R packages RSQLite and org.Hs.eg.db. Multivariate Cox regression analysis was performed to screen factors associated with patient survival. Kaplan-Meier and receiver operating characteristic curves were used to analyze the potential clinical significance of the identified biomarkers as molecular prognostic markers for the five-year overall survival time. A total of 373 differentially expressed genes (DEGs; |log2-fold change|≥2.0 and P<0.01) were identified, of which 71 were downregulated and 302 were upregulated. These DEGs were associated with 28 significant GO functions and 11 significant KEGG pathways (false discovery rate <0.05). Two hundred thirty-eight proteins were associated with the 373 differentially expressed genes, and a protein-protein interaction network was constructed. Multivariate regression analysis revealed that 7 mRNAs, cytochrome P450 family 17 subfamily A member 1, PKHD1 like 1, retinoid isomerohydrolase RPE65, neurotensin receptor 1, fetuin B, insulin-like growth factor binding protein 1 and glucose-6-phosphatase catalytic subunit, significantly distinguished between non-smoking and smoking-associated adenocarcinomas. Kaplan-Meier analysis demonstrated that patients in the 7 mRNAs-high-risk group had a significantly worse prognosis than those of the low-risk group. The data obtained in the current study suggested that these genes may serve as potential novel prognostic biomarkers of smoking-associated lung adenocarcinoma.

ATM rs189037 significantly increases the risk of cancer in non-smokers rather than smokers: an updated meta-analysis

Rs189037 (G>A) is an important functional variant with ataxia telangiectasia mutated (ATM) gene, which might affect ATM’s expression involvement in several human cancers. Increasing evidence reveals that smoking-related cancers have distinct molecular characteristics from non-smoking cancers. Until now, the role of ATM rs189037 in cancer risk stratified by smoking status still remains unclear. To evaluate the association between ATM rs189037 and cancer risk based on smoking status, we performed this meta-analysis by a comprehensive literature search via databases of PubMed, Embase, Web of Science and CNKI, updated till January 2019. Multivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were extracted from eligible studies if available, to assess the relationship strengths. A total of seven eligible studies were included, comprising 4294 cancer patients (smokers: 1744 [40.6%]) and 4259 controls (smokers: 1418 [33.3%]). Results indicated a significant association of ATM rs189037 with cancer risk. In non-smokers, compared with GG genotype, AA genotype increased a 1.40-fold risk of overall cancer (OR = 1.40, 95% CI = 1.15–1.70, P_{heterogeneity}=0.433, I² = 0.0%). Subgroup analysis in lung cancer (LC) also exhibited a significant result (OR = 1.41, 95% CI = 1.15–1.73, P_{heterogeneity}=0.306, I² = 17.0%) only in non-smokers. However, the association was not observed in smokers, no matter for overall cancer or for LC. Our findings highlight that ATM rs189037 significantly increases cancer susceptibility in non-smokers, rather than in smokers. The association is prominent in LC.

Non-Smoking-Associated Lung Cancer: A distinct Entity in Terms of Tumor Biology, Patient Characteristics and Impact of Hereditary Cancer Predisposition

Non-small cell lung cancer (NSCLC) in non-, and especially in never-smoking patients is considered a biologically unique type of lung cancer, since risk factors and tumorigenic conditions, other than tobacco smoke, come into play. In this review article, we comprehensively searched and summarized the current literature with the aim to outline what exactly triggers lung cancer in non-smokers. Changes in the tumor microenvironment, distinct driver genes and genetic pathway alterations that are specific for non-smoking patients, as well as lifestyle-related risk factors apart from tobacco smoke are critically discussed. The data we have reviewed highlights once again the importance of personalized cancer therapy, i.e., careful molecular and genetic assessment of the tumor to provide tailored treatment options with optimum chances of good response-especially for the subgroups of never-smokers.