Risk Of Non-small Cell Lung Cancer Research Articles

Associations of PD-1 and PD-L1 gene polymorphisms with cancer risk: a meta-analysis based on 50 studies.

Programmed death-1 and its ligand-1 (PD-1/PD-L1), immune checkpoints proteins, play a crucial role in anti-tumor responses. A large number of studies have evaluated the relationships of PD-1/PD-L1 polymorphisms with risk of cancer, but evidence for the associations remains inconsistent. Therefore, we performed a meta-analysis to examine the associations between PD-1/PD-L1 single nucleotide polymorphisms (SNPs) and cancer predisposition. Results showed that PD-1.3 and PD-L1 rs17718883 were significantly correlated with overall cancer risk. PD-1.5 was prominently linked with cervical cancer (CC), non-small cell lung cancer (NSCLC), TC (thyroid cancer), brain tumor, AML (acute myelocytic leukemia) and UCC (urothelial cell carcinoma) risk, PD-1.9 with breast cancer (BC), AML, esophageal cancer (EC) and ovarian cancer (OC) risk, and PD-1.3 with colorectal cancer (CRC) and BCC (basal cell carcinoma) risk. PD-1.1 polymorphism slightly elevated BC and OC susceptibility, whereas the rs4143815 variant notably decreased the risk of gastric cancer (GC), hepatocellular carcinoma (HCC) and OC, but increased the risk of BC. PD-1.6 was closely linked with AML risk, PD-L1 rs2890658 with NSCLC, HCC and BC risk, rs17718883 with HCC and GC risk, rs10815225 with GC risk, and rs2297136 with NSCLC and HCC risk. Interestingly, the rs7421861, rs10815225, and rs10815225 markedly reduced cancer susceptibility among Asians. The rs7421861 polymrophism decreased cancer risk among Caucasians, rather than the rs10815225 elevated cancer risk. Our results supported that PD-1 and PD-L1 SNPs were dramatically correlated with cancer risk.

Causal role of metabolites in non-small cell lung cancer: Mendelian randomization (MR) study.

On a global scale, lung cancer(LC) is the most commonly occurring form of cancer. Nonetheless, the process of screening and detecting it in its early stages presents significant challenges. Earlier research endeavors have recognized metabolites as potentially reliable biomarkers for LC. However, the majority of these studies have been limited in scope, featuring inconsistencies in terms of the relationships and levels of association observed.Moreover, there has been a lack of consistency in the types of biological samples utilized in previous studies. Therefore, the main objective of our research was to explore the correlation between metabolites and Non-small cell lung cancer (NSCLC).Thorough two-sample Mendelian randomization (TSMR) analysis, we investigated potential cause-and-effect relationships between 1400 metabolites and the risk of NSCLC.The analysis of TSMR revealed a significant causal impact of 61 metabolites on NSCLC.To ensure the reliability and validity of our findings, we perform FDR correction for P-values by Benjaminiand Hochberg(BH) method, Our results indicate that Oleate/vaccenate (18:1) levels and Caffeine to paraxanthine ratio may be causally associated with an increased risk of NSCLC [Oleate/vaccenate(18:1)levels: OR = 1.171,95%CI: 1.085-1.265, FDR = 0.036; Caffeine to paraxanthine ratio: OR = 1.386, 95%CI:1.191-1.612,FDR = 0.032].

Abstract 2206: Obesity and cancer outcomes among patients with lung cancer and cutaneous melanoma treated with immune checkpoint inhibitors and EGFR inhibitors

Abstract Background: Obesity is a causal factor in cancer risk and progression. Immune checkpoint inhibitors (ICI) and EGFR inhibitors (EGFRi) are indicated for the treatment of several cancers, including non-small-cell lung cancer (NSCLC) and cutaneous melanoma (CM). Improved outcomes was observed with ICI in preclinical studies. We aimed to determine the association of obesity with progression-free survival (PFS) or overall survival (OS) among patients with NSCLC and CM treated with ICI or EGFRi. Methods: This was a retrospective study utilizing 2011-2019 SEER-Medicare data of Medicare Parts A and B enrolled patients ≥66 years diagnosed with NSCLC or CM who received ICI or EGFRi (NSCLC only) within 1-year post-diagnosis. Patients were followed from treatment commencement until death, loss to follow-up, or end of study. Descriptive statistics and survival analysis were used to achieve study objectives. Results: Among 2393 NSCLC patients receiving ICI, obese patients (N=281, 11.7%) were younger (73.4±5.2 yrs vs 74.4±5.9 yrs, p<0.01) with higher CCI scores (2 (IQR=1-4) vs 1 (IQR=0-2), p<0.0001) compared to non-obese patients (N=2,311, 89.2%). Time to progression(TTP) was 41% faster among obese patients (392 days (95% CI=330-468) vs 493 days (95% CI=464-525), p<0.01; HR=1.41 (1.13-1.76), p<0.01) with no significant difference in time to death (TTD) (443 days (404-488) vs 491 days (462-524), p=0.35). Among 880 CM patients, those with obesity (N=91, 10.3%)) were significantly younger (74.7±6.1 yrs vs 76.3±7.3 yrs, p=0.02) with higher CCI scores (2 (IQR=0-4) vs 0 (IQR=0-2), p<0.0001). TTP was 343 days (320-400). TTD was 1,409 days (1,107-2,136) with no association between obesity status and PFS or OS. Among 2,524 NSCLC patients receiving EGFRi, obese patients (126, 5.0%) were younger (73.7±6.1 yrs) vs 75.6±6.5 yrs, p<0.01), had higher CCI scores (2 (IQR=1-3) vs 0 (IQR=0-1), p<0.0001), and longer time to treatment (3 mths (IQR=1-7) vs 2 mths (IQR=1-6), p=0.02). A higher proportion were White (76.2% vs 61.3%, p<0.001). Risk of progression was 48% higher among obese patients (664 dys (496-778) vs 1,231 dys (1,127-1,379), p<0.0001; HR=1.48, 95% CI=1.11-1.97, p<0.01) with no significant difference in TTD (559 dys (440-763) vs 673 dys (639-707), p=0.35). Conclusion: Obesity with associated with PFS but not OS among patients with NSCLC with no association observed among patients with CM. Given that the link between obesity and NSCLC risk remains controversial, the observed association with PFS may be linked to the absence of the paradoxical effect of improve outcomes that may be seen with cancers caused by obesity such as CM. Citation Format: Sabina O. Nduaguba, Lori Hazlehurst. Obesity and cancer outcomes among patients with lung cancer and cutaneous melanoma treated with immune checkpoint inhibitors and EGFR inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2206.

Gut Microbiota Signatures with Potential Clinical Usefulness in Colorectal and Non-Small Cell Lung Cancers.

The application of bacterial metagenomic analysis as a biomarker for cancer detection is emerging. Our aim was to discover gut microbiota signatures with potential utility in the diagnosis of colorectal cancer (CRC) and non-small cell lung cancer (NSCLC). A prospective study was performed on a total of 77 fecal samples from CRC and NSCLC patients and controls. DNA from stool was analyzed for bacterial genomic sequencing using the Ion Torrent™ technology. Bioinformatic analysis was performed using the QIIME2 pipeline. We applied logistic regression to adjust for differences attributable to sex, age, and body mass index, and the diagnostic accuracy of our gut signatures was compared with other previously published results. The feces of patients affected by different tumor types, such as CRC and NSCLC, showed a differential intestinal microbiota profile. After adjusting for confounders, Parvimonas (OR = 53.3), Gemella (OR = 6.01), Eisenbergiella (OR = 5.35), Peptostreptococcus (OR = 9.42), Lactobacillus (OR = 6.72), Salmonella (OR = 5.44), and Fusobacterium (OR = 78.9) remained significantly associated with the risk of CRC. Two genera from the Ruminococcaceae family, DTU089 (OR = 20.1) and an uncharacterized genus (OR = 160.1), were associated with the risk of NSCLC. Our two panels had better diagnostic capacity for CRC (AUC = 0.840) and NSLC (AUC = 0.747) compared to the application of two other published panels to our population. Thus, we propose a gut bacteria panel for each cancer type and show its potential application in cancer diagnosis.

Combining Classic and Novel Neutrophil-Related Biomarkers to Identify Non-Small-Cell Lung Cancer

Background: Recent studies have revealed that neutrophils play a crucial role in cancer progression. This study aimed to explore the diagnostic value of neutrophil-related biomarkers for non-small-cell lung cancer (NSCLC). Methods: We initially assessed the associations between classic neutrophil-related biomarkers (neutrophil-to-lymphocyte ratio (NLR), absolute neutrophil counts (NEU), absolute lymphocyte counts (LYM)) and NSCLC in 3942 cases and 6791 controls. Then, we measured 11 novel neutrophil-related biomarkers via Luminex Assays in 132 cases and 66 controls, individually matching on sex and age (±5 years), and evaluated their associations with NSCLC risk. We also developed the predictive models by sequentially adding variables of interest and assessed model improvement. Results: Interleukin-6 (IL-6) (odds ratio (OR) = 10.687, 95% confidence interval (CI): 3.875, 29.473) and Interleukin 1 Receptor Antagonist (IL-1RA) (OR = 8.113, 95% CI: 3.182, 20.689) shows strong associations with NSCLC risk after adjusting for body mass index, smoking status, NLR, and carcinoembryonic antigen. Adding the two identified biomarkers to the predictive model significantly elevated the model performance from an area under the receiver operating characteristic curve of 0.716 to 0.851 with a net reclassification improvement of 97.73%. Conclusions: IL-6 and IL-1RA were recognized as independent risk factors for NSCLC, improving the predictive performance of the model in identifying disease.

Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC

Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.

Red blood cell distribution width is associated with sarcopenia risk in early-stage non-small cell lung cancer

BackgroundSarcopenia has received increasing attention in non-small cell lung cancer (NSCLC). Red blood cell distribution width (RDW) is a significant component of the complete blood count and indicates the heterogeneity of erythrocyte volume. Little information is known about RDW in relation to sarcopenia in early-stage (IA-IIIA) NSCLC. The purpose of the present study was to investigate the association between RDW and sarcopenia risk in early-stage NSCLC patients.MethodsThis study included 378 patients with pathologically confirmed stage IA-IIIA NSCLC. Sarcopenia was defined by measuring the skeletal muscle index (SMI) at the eleventh thoracic vertebra level. The maximum Youden index on the receiver operating characteristic (ROC) curve was used to estimate the cutoff value for RDW to predict sarcopenia. Logistic regression analyses were carried out to assess the independent risk factors for sarcopenia in NSCLC.ResultsThe ROC curve indicated that the best cutoff point for RDW to predict sarcopenia was 12.9 (sensitivity of 43.80% and specificity of 76.76%, respectively). Moreover, there were significant differences in hemoglobin (p < 0.001), comorbidities (p = 0.001), histological type (p = 0.002), and cancer stage (p = 0.032) between the high RDW and low RDW groups. Logistic regression analyses revealed that high RDW is an independent risk factor for sarcopenia in early-stage NSCLC.ConclusionRDW is associated with sarcopenia risk in early-stage NSCLC.

AuNPs/CNC Nanocomposite with A "Dual Dispersion" Effect for LDI-TOF MS Analysis of Intact Proteins in NSCLC Serum Exosomes.

Detecting exosomal markers using laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) is a novel approach for examining liquid biopsies of non-small cell lung cancer (NSCLC) samples. However, LDI-TOF MS is limited by low sensitivity and poor reproducibility when analyzing intact proteins directly. In this report, gold nanoparticles/cellulose nanocrystals (AuNPs/CNC) is introduced as the matrix for direct analysis of intact proteins in NSCLC serum exosomes. AuNPs/CNC with "dual dispersion" effects dispersed and stabilized AuNPs and improved ion inhibition effects caused by protein aggregation. These features increased the signal-to-noise ratio of [M+H]+ peaks by two orders of magnitude and lowered the detection limit of intact proteins to 0.01mgmL-1. The coefficient of variation with or without AuNPs/CNC is measured as 10.2% and 32.5%, respectively. The excellent reproducibility yielded a linear relationship (y=15.41x - 7.983, R2=0.989) over the protein concentration range of 0.01 to 20mgmL-1. Finally, AuNPs/CNC-assisted LDI-TOF MS provides clinically relevant fingerprint information of exosomal proteins in NSCLC serum, and characteristic proteins S100 calcium-binding protein A10, Urokinase plasminogen activator surface receptor, Plasma protease C1 inhibitor, Tyrosine-protein kinase Fgr and Mannose-binding lectin associated serine protease 2 represented excellent predictive biomarkers of NSCLC risk.

The Role of Human Papilloma Virus (HPV) in Primary Lung Cancer Development: State of the Art and Future Perspectives.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Notably, the incidence of lung cancer among never-smokers, predominantly women, has been rising in recent years. Among the various implicated risk factors, human papilloma virus (HPV) may play a role in the development of NSCLC in a certain subset of patients. The prevalence of high-risk HPV-DNA within human neoplastic lung cells varies across the world; however, the carcinogenetic role of HPV in NSCLC has not been completely understood. Bloodstream could be one of the routes of transmission from infected sites to the lungs, along with oral (through unprotected oral sex) and airborne transmission. Previous studies reported an elevated risk of NSCLC in patients with prior HPV-related tumors, such as cervical, laryngeal, or oropharyngeal cancer, with better prognosis for HPV-positive lung cancers compared to negative forms. On the other hand, 16% of NSCLC patients present circulating HPV-DNA in peripheral blood along with miRNAs expression. Typically, these patients have a poorly differentiated NSCLC, often diagnosed at an advanced stage. However, HPV-positive lung cancers seem to have a better response to target therapies (EGFR) and immune checkpoint inhibitors and show an increased sensitivity to platinum-based treatments. This review summarizes the current evidence regarding the role of HPV in NSCLC development, especially among patients with a history of HPV-related cancers. It also examines the diagnostic and prognostic significance of HPV, investigating new future perspectives to enhance cancer screening, diagnostic protocols, and the development of more targeted therapies tailored to specific cohorts of NSCLC patients with confirmed HPV infection.

Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients’ characteristic data, be more relevant to different patients, and help achieve precision medicine.

Impact of IL-6 and IL-1β Gene Variants on Non-small-cell Lung Cancer Risk in Egyptian Patients

AbstractLung cancer is a serious health and life issue, with the fastest-growing incidence and fatality rates worldwide. It is now clear that inflammation is a key factor involved in all aspects of carcinogenesis, notably lung cancer development. Genetic changes, including polymorphisms in inflammatory genes, are supposed to be a significant cause of increased lung cancer risk. The main idea of this research was to disclose the linkage between both IL-6 rs1800795 and IL-1β rs16944 variants and susceptibility to non-small-cell lung cancer (NSCLC) in Egyptians. This case–control design was composed of 127 cases and 138 controls, which were genotyped using the ARMS-PCR technique. To examine the NSCLC susceptibility under various genetic models, the odds ratio (OR) and 95% confidence intervals (CIs) were determined by logistic regression. Rs1800795 of the IL-6 gene was linked to higher odds of NSCLC under the allele model (adjusted, OR 2.28; 95% CI 1.2–4.33; p = 0.011). In the genetic models, IL-6 rs1800795 elevated the odds of NSCLC, while IL-1β rs16944 decreased the odds of NSCLC. Stratification analysis showed that IL-6 rs1800795 greatly increased the NSCLC risk in females and adenocarcinoma subtypes, whereas IL-1β rs16944 largely decreased the NSCLC risk for males, patients aged &lt; 55, and nonsmokers. Regarding clinical data, the IL-6 variant was remarkably correlated with tumor size. This work primarily established that IL-6 and IL-1β variants have a great impact on NSCLC development in the Egyptian population; thus, it may be a supportive guide for earlier NSCLC prevention.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p≤1.67×10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p≤1.28×10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

CCR5 promoter polymorphisms associated with nonsmall cell lung cancer.

C-C chemokine receptor 5 (CCR5) plays a crucial role in the regulation of immune cell activation and migration as well as the progression of many cancers. We performed an in silico analysis using public data resources and found that the lung cancer patients with higher CCR5 expression had a notably better overall survival than those with lower CCR5 expression patients and CCR5 expression level is positive correlated with the infiltration of immune cells, such as B, CD8+ T and CD4+ T cells, in both lung adenocarcinoma and lung squamous cell cancer. In the present study, we investigated the association between the promoter polymorphism of CCR5 and nonsmall cell lung cancer (NSCLC). A case-control study of 449 NSCLC patients and 516 controls of Chinese Han population was conducted, along with polymorphism detection using a sequencing method. A dual-luciferase reporter assay system was used to analyse the transcriptional activity of CCR5 promoter variations. Our results showed that the frequency of rs1799987-AA was significantly higher in the NSCLC group than in the controls in recessive model (p=.007, OR=1.66 95% confidence interval [CI]: 1.14-2.40, adjusted by sex and age); the G allele showed a significant associated with NSCLC in dominant model (p=.003, OR=1.64, 95%CI: 1.18-2.28, adjusted by sex and age). Compared with haplotype H1 rs2227010-rs2734648-rs1799987-rs1799988-rs1800023-rs1800024: A-T-G-T-G-C, haplotype H5: A-G-G-T-G-C increased the risk of NSCLC by over 10-fold (p<.0001, OR=16.09, 95%CI: 5.37-48.20, adjusted by sex and age) and notably depressed the transcriptional activity of the CCR5 promoter in 293T, A549, H1299 and HeLa cells. In conclusion, CCR5 promoter polymorphisms are significantly associated with NSCLC by affecting the transcriptional activity of the CCR5 promoter.

A genetic variant in gene NDUFAF4 confers the risk of non-small cell lung cancer by perturbing hsa-miR-215 binding.

Hsa-microRNA-215 (hsa-miR-215) plays multiple roles in carcinogenesis through regulating its target genes. Genetic variants in hsa-miR-215 target sites thus may affect hsa-miR-215-mRNA interactions, result in altered expression of target genes and even influence cancer susceptibility. This study aimed to investigate the associations of genetic variants which located in the binding sites of hsa-miR-215 with non-small cell lung cancer (NSCLC) susceptibility in the Chinese population and reveal the potential regulatory mechanism of functional variants in NSCLC development. The candidate genetic variants were predicted and screened through bioinformatics analysis based on the degree of complementarity of hsa-miR-215 sequences. The potential effects of genetic variants on the binding ability of hsa-miR-215 and target genes were also predicted. A case-control study with 932 NSCLC patients and 1036 healthy controls was conducted to evaluate the association of candidate genetic variants with NSCLC susceptibility, and an independent case-control study with 552 NSCLC cases and 571 controls were used to further validate the promising associations. Dual luciferase reporter gene assay was applied to explore the regulation of the genetic variants on transcription activity of target gene. Cell phenotyping experiments in vitro and RNA sequencing (RNA-seq) were then carried out to preliminarily explore the potential regulatory mechanisms of the target genes in NSCLC. A total of five candidate genetic variants located in the binding sites of hsa-miR-215 were screened. The two-stage case-control study showed that a variant rs1854268 A > T, which located in the 3' untranslated (3'UTR) region of NDUFAF4 gene, was associated with decreased risk of NSCLC (additive model, odds ratio [OR] = 0.83, 95% confidence interval [CI]: 0.75-0.92, p < 0.001). Functional annotation displayed that rs1854268 A > T might downregulate the expression of NDUFAF4 by enhancing the binding affinity of hsa-miR-215-5p to NDUFAF4 mRNA. Additionally, transient knockdown of the NDUFAF4 could inhibit lung cancer cell migration and promote lung cancer cell apoptosis. Further RNA-seq analysis revealed that the knockdown of NDUFAF4 may affect NSCLC development by downregulating the nuclear factor kappa B (NF-κB) and phosphoinositide 3 kinase-AKT (PI3K-AKT) signaling pathways. Moreover, the overexpression of CCND1 could partially attenuate the effects of NDUFAF4 knock down on lung cancer cell migration and apoptosis, indicating that CCND1 may be involved in the tumor-promoting effects of NDUFAF4 as a downstream molecule of NDUFAF4 gene. In conclusion, the genetic variant rs1854268 (A > T) on NDUFAF4 confers NSCLC susceptibility by altering the binding affinity of hsa-miR-215-5p, thus regulating the expression of NDUFAF4 and subsequently influencing downstream tumor molecules and pathways such as CCND1, NF kappa B, and PI3K-AKT signaling pathways.

Taxifolin Inhibits the Growth of Non-Small-Cell Lung Cancer via Downregulating Genes Displaying Novel and Robust Associations with Immune Evasion Factors.

Using an LL2 cell-based syngeneic mouse LC model, taxifolin suppressed allografts along with the appearance of 578 differentially expressed genes (DEGs). These DEGs were associated with enhancement of processes related to the extracellular matrix and lymphocyte chemotaxis as well as the reduction in pathways relevant to cell proliferation. From these DEGs, we formulated 12-gene (TxflSig) and 7-gene (TxflSig1) panels; both predicted response to ICB (immune checkpoint blockade) therapy more effectively in non-small-cell lung cancer (NSCLC) than numerous well-established ICB biomarkers, including PD-L1. In both panels, the mouse counterparts of ITGAL, ITGAX, and TMEM119 genes were downregulated by taxifolin. They were strongly associated with immune suppression in LC, evidenced by their robust correlations with the major immunosuppressive cell types (MDSC, Treg, and macrophage) and multiple immune checkpoints in NSCLC and across multiple human cancer types. ITGAL, ITGAX, and IIT (ITGAL-ITGAX-TMEM119) effectively predicted NSCLC's response to ICB therapy; IIT stratified the mortality risk of NSCLC. The stromal expressions of ITGAL and ITGAX, together with tumor expression of TMEM119 in NSCLC, were demonstrated. Collectively, we report multiple novel ICB biomarkers-TxflSig, TxflSig1, IIT, ITGAL, and ITGAX-and taxifolin-derived attenuation of immunosuppressive activities in NSCLC, suggesting the inclusion of taxifolin in ICB therapies for NSCLC.

Associations between telomere attrition, genetic variants in telomere maintenance genes, and non-small cell lung cancer risk in the Jammu and Kashmir population of North India

BackgroundTelomeres are repetitive DNA sequences located at the ends of chromosomes, playing a vital role in maintaining chromosomal integrity and stability. Dysregulation of telomeres has been implicated in the development of various cancers, including non-small cell lung cancer (NSCLC), which is the most common type of lung cancer. Genetic variations within telomere maintenance genes may influence the risk of developing NSCLC. The present study aimed to evaluate the genetic associations of select variants within telomere maintenance genes in a population from Jammu and Kashmir, North India, and to investigate the relationship between telomere length and NSCLC risk.MethodsWe employed the cost-effective and high-throughput MassARRAY MALDI-TOF platform to assess the genetic associations of select variants within telomere maintenance genes in a population from Jammu and Kashmir, North India. Additionally, we used TaqMan genotyping to validate our results. Furthermore, we investigated telomere length variation and its relation to NSCLC risk in the same population using dual-labeled fluorescence-based qPCR.ResultsOur findings revealed significant associations of TERT rs10069690 and POT1 rs10228682 with NSCLC risk (adjusted p-values = 0.019 and 0.002, respectively), while TERF2 rs251796 and rs2975843 showed no significant associations. The TaqMan genotyping validation further substantiated the associations of TERT rs10069690 and rs2242652 with NSCLC risk (adjusted p-values = 0.02 and 0.003, respectively). Our results also demonstrated significantly shorter telomere lengths in NSCLC patients compared to controls (p = 0.0004).ConclusionThis study highlights the crucial interplay between genetic variation in telomere maintenance genes, telomere attrition, and NSCLC risk in the Jammu and Kashmir population of North India. Our findings suggest that TERT and POT1 gene variants, along with telomere length, may serve as potential biomarkers and therapeutic targets for NSCLC in this population. Further research is warranted to elucidate the underlying mechanisms and to explore the potential clinical applications of these findings.

Development and validation of a risk model with variables related to non-small cell lung cancer in patients with pulmonary nodules: a retrospective study

BackgroundLung cancer is a major global threat to public health for which a novel predictive nomogram is urgently needed. Non-small cell lung cancer (NSCLC) which accounts for the main port of lung cancer cases is attracting more and more people’s attention.Patients and methodsHere, we designed a novel predictive nomogram using a design dataset consisting of 515 pulmonary nodules, with external validation being performed using a separate dataset consisting of 140 nodules and a separate dataset consisting of 237 nodules. The selection of significant variables for inclusion in this model was achieved using a least absolute shrinkage and selection operator (LASSO) logistic regression model, after which a corresponding nomogram was developed. C-index values, calibration plots, and decision curve analyses were used to gauge the discrimination, calibration, and clinical utility, respectively, of this predictive model. Validation was then performed with the internal bootstrapping validation and external cohorts.ResultsA predictive nomogram was successfully constructed incorporating hypertension status, plasma fibrinogen levels, blood urea nitrogen (BUN), density, ground-glass opacity (GGO), and pulmonary nodule size as significant variables associated with nodule status. This model exhibited good discriminative ability, with a C-index value of 0.765 (95% CI: 0.722-0.808), and was well-calibrated. In validation analyses, this model yielded C-index values of 0.892 (95% CI: 0.844-0.940) for external cohort and 0.853 (95% CI: 0.807-0.899) for external cohort 2. In the internal bootstrapping validation, C-index value could still reach 0.753. Decision curve analyses supported the clinical value of this predictive nomogram when used at a NSCLC possibility threshold of 18%.ConclusionThe nomogram constructed in this study, which incorporates hypertension status, plasma fibrinogen levels, BUN, density, GGO status, and pulmonary nodule size, was able to reliably predict NSCLC risk in this Chinese cohort of patients presenting with pulmonary nodules.

Machine Learning Study of SNPs in Noncoding Regions to Predict Non-small Cell Lung Cancer Susceptibility

Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (<3.02e-6). Notably, rs80040741 and rs6010259 were annotated from the intron regions of MUC3A and MLC1, respectively. Together with another five SNPs previously reported in Chinese NSCLC patients and another four covariates (e.g., smoking status, age, low dose CT screening, sex), a predictive model by machine learning methods can separate the NSCLC from healthy controls with an accuracy of 86%. This is the first time to apply machine learning method in predicting the NSCLC susceptibility using both genetic and clinical characteristics. Our findings will provide a promising method in NSCLC early diagnosis and improve our understanding of applying machine learning methods in precision medicine.

Integrative analysis of GWAS and transcriptomics data reveal key genes for non-small lung cancer.

Lung cancer is one of the world's most common and deadly cancers. The two main types of lung cancer are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). More than 85% of lung cancers are NSCLC. Genetic factors play a significant role in the risk of NSCLC. Growing studies focus on studying risk factors at the molecular level. The aim of the study is to build a pipeline to integrate Genome-wide association analysis (GWAS) and transcriptomics data with machine learning to effectively identify genetic risk factors of NSCLC. GWAS datasets and GWAS summary data were downloaded from GWAS catalog, which include lung carcinoma genetic variants among the European population. Then, with the GWAS summary, data functional analysis of significant SNPs was performed using a webserver called FUMAGWAS. The transcriptomics data of NSCLC and non-NSCLC people were used to build a machine learning model to identify the key genes that help predict the NSCLC. The top up-regulation and down-regulation genes were identified by the BART cancer webserver, and the mechanistic roles of the genes were validated by literature review. By performing integrative analysis of GWAS and transcriptomics analysis using machine learning, we identified multiple SNPs and genes that related to NSCLC. The computational pipeline may facilitate the biomarker discovery for NSCLC and other diseases.

Integrative splicing-quantitative-trait-locus analysis reveals risk loci for non-small-cell lung cancer

Splicing quantitative trait loci (sQTLs) have been demonstrated to contribute to disease etiology by affecting alternative splicing. However, the role of sQTLs in the development of non-small-cell lung cancer (NSCLC) remains unknown. Thus, we performed a genome-wide sQTL study to identify genetic variants that affect alternative splicing in lung tissues from 116 individuals of Chinese ancestry, which resulted in the identification of 1,385 sQTL-harboring genes (sGenes) containing 378,210 significant variant-intron pairs. A comprehensive characterization of these sQTLs showed that they were enriched in actively transcribed regions, genetic regulatory elements, and splicing-factor-binding sites. Moreover, sQTLs were largely distinct from expression quantitative trait loci (eQTLs) and showed significant enrichment in potential risk loci of NSCLC. We also integrated sQTLs into NSCLC GWAS datasets (13,327 affected individuals and 13,328 control individuals) by using splice-transcriptome-wide association study (spTWAS) and identified alternative splicing events in 19 genes that were significantly associated with NSCLC risk. By using functional annotation and experiments, we confirmed an sQTL variant, rs35861926, that reduced the risk of lung adenocarcinoma (rs35861926-T, OR= 0.88, 95% confidence interval [CI]: 0.82-0.93, p= 1.87×10-5) by promoting FARP1 exon 20 skipping to downregulate the expression level of the long transcript FARP1-011. Transcript FARP1-011 promoted the migration and proliferation of lung adenocarcinoma cells. Overall, our study provided informative lung sQTL resources and insights into the molecular mechanisms linking sQTL variants to NSCLC risk.