Tumor necrosis factor in lung cancer: Complex roles in biology and resistance to treatment

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach

Background

DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.

Method

Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.

Results

A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.

Conclusions

This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Unravelling the Triad of Lung Cancer, Drug Resistance, and Metabolic Pathways

Lung cancer, characterized by its heterogeneity, presents a significant challenge in therapeutic management, primarily due to the development of resistance to conventional drugs. This resistance is often compounded by the tumor's ability to reprogram its metabolic pathways, a survival strategy that enables cancer cells to thrive in adverse conditions. This review article explores the complex link between drug resistance and metabolic reprogramming in lung cancer, offering a detailed analysis of the molecular mechanisms and treatment strategies. It emphasizes the interplay between drug resistance and changes in metabolic pathways, crucial for developing effective lung cancer therapies. This review examines the impact of current treatments on metabolic pathways and the significance of considering metabolic factors to combat drug resistance. It highlights the different challenges and metabolic alterations in non-small-cell lung cancer and small-cell lung cancer, underlining the need for subtype-specific treatments. Key signaling pathways, including PI3K/AKT/mTOR, MAPK, and AMPK, have been discussed for their roles in promoting drug resistance and metabolic changes, alongside the complex regulatory networks involved. This review article evaluates emerging treatments targeting metabolism, such as metabolic inhibitors, dietary management, and combination therapies, assessing their potential and challenges. It concludes with insights into the role of precision medicine and metabolic biomarkers in crafting personalized lung cancer treatments, advocating for metabolic targeting as a promising approach to enhance treatment efficacy and overcome drug resistance. This review underscores ongoing advancements and hurdles in integrating metabolic considerations into lung cancer therapy strategies.

Clinical significance of PNO1 as a novel biomarker and therapeutic target of hepatocellular carcinoma

The RNA-binding protein PNO1 plays an essential role in ribosome biogenesis. Recent studies have shown that it is involved in tumorigenesis; however, its role in hepatocellular carcinoma (HCC) is not well understood. The purpose of this study was to examine whether PNO1 can be used as a biomarker of HCC and also examine the therapeutic potential of PNO1 knockout for the treatment of HCC. PNO1 expression was upregulated in HCC and associated with poor prognosis. PNO1 expression was positively associated with tumour stage, lymph node metastasis and poor survival. PNO1 expression was significantly higher in HCC compared to that in fibrolamellar carcinoma or normal tissues. Furthermore, HCC tissues with mutant Tp53 expressed higher PNO1 than those with wild-type Tp53. PNO1 knockout suppressed cell viability, colony formation and EMT of HCC cells. Since activation of Notch signalling pathway promotes HCC, we measured the effects of PNO1 knockout on the components of Notch pathway and its targets. PNO1 knockout suppressed Notch signalling by modulating the expression of Notch ligands and their receptors, and downstream targets. PNO1 knockout also inhibited genes involved in surface adhesion, cell cycle, inflammation and chemotaxis. PNO1 knockout also inhibited colony and spheroid formation, cell migration and invasion, and markers of stem cells, pluripotency and EMT in CSCs. Overall, our data suggest that PNO1 can be used as a diagnostic and prognostic biomarker of HCC, and knockout of PNO1 by CRISPR/Cas9 can be beneficial for the management of HCC by targeting CSCs.

Exploration of Ginkgo biloba leaves on non-small cell lung cancer based on network pharmacology and molecular docking

BACKGROUND

Pharmacological studies have found Ginkgo biloba leaves have the effect of inhibiting neoplasms, it is clinically used in treating various neoplasms. However, the mechanism of Ginkgo biloba leaves in treating non-small cell lung cancer (NSCLC) remains unclear.

METHODS

The active components and corresponding targets of Ginkgo biloba leaves were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) database, and the targets of NSCLC were obtained from the GeneCards, OMIM, TTD, and DrugBank databases. The common targets of NSCLC and Ginkgo biloba leaves were obtained from VENNY 2.1.0. The STRING database was utilized to construct protein-protein intersections, by using the Cytoscape 3.7.1 software, the protein-protein intersection was optimized and the drug-disease network diagram was constructed. The DAVID database was utilized to perform GO and KEGG analysis. Finally, The Autodock Vina software was used to perform molecular docking of core components and targets.

RESULTS

The key components of Ginkgo biloba leaves in treating NSCLC include quercetin, luteolin, and kaempferol, which may act on Tp53, AKT1, and TNF. Bioinformatic annotation analysis results suggest that Ginkgo biloba leaves may implicated in PI3K-AKT and MAPK signaling pathways. The molecular docking results show the firm affinity between key ingredients and targets.

CONCLUSION

The potential mechanism of Ginkgo biloba leaves in treating NSCLC has been discussed in this study, which provides a theoretical basis for the clinical treatment of NSCLC and further experimental validation.

PRKCSH contributes to TNFSF resistance by extending IGF1R half-life and activation in lung cancer

Tumor necrosis factor superfamily (TNFSF) resistance contributes to the development and progression of tumors and resistance to various cancer therapies. Tumor-intrinsic alterations involved in the adaptation to the TNFSF response remain largely unknown. Here, we demonstrate that protein kinase C substrate 80K-H (PRKCSH) abundance in lung cancers boosts oncogenic IGF1R activation, leading to TNFSF resistance. PRKCSH abundance is correlated with IGF1R upregulation in lung cancer tissues. Specifically, PRKCSH interacts with IGF1R and extends its half-life. The PRKCSH-IGF1R axis in tumor cells impairs caspase-8 activation, increases Mcl-1 expression, and inhibits caspase-9, leading to an imbalance between cell death and survival. PRKCSH deficiency augmented the antitumor effects of natural killer (NK) cells, representative TNFSF effector cells, in a tumor xenograft IL-2Rg-deficient NOD/SCID (NIG) mouse model. Our data suggest that PRKCSH plays a critical role in TNFSF resistance and may be a potential target to improve the efficacy of NK cell-based cancer therapy.

Correlation of Increased Soluble Tumor Necrosis Factor Receptor 1 with Mortality and Dependence on Treatment in Non-Small-Cell Lung Cancer Patients: A Longitudinal Cohort Study

BACKGROUND

Tumor necrosis factor (TNF) is a multipotent cytokine involved in inflammation and anti-tumor activity. TNF-α exerts its function upon binding to TNF-receptor 1 (TNF-R1) and TNF-receptor 2 (TNF-R2). This study investigates the relationship of soluble (s) TNF-R1 levels in non-small-cell lung cancer (NSCLC) patients with treatment and overall survival.

METHODS

In total, 134 NSCLC patients treated at the Medical Faculty of Martin Luther University Halle-Wittenberg between 2017 and 2019 were included in this study. Serum levels of sTNF-R1 were measured via ELISA at baseline and during and after treatment. A linear mixed-effects model was used to assess sTNF-R1 changes over time. Linear regression was applied to investigate the association between clinical characteristics and changes in sTNF-R1. Cox regression models were used to estimate associations with overall mortality.

RESULTS

The estimated average sTNFR-1 at baseline was 2091.71 pg/mL, with a change of 6.19 pg/mL per day. Cox models revealed that the individual change in sTNF-R1 was more strongly associated with mortality than its baseline value, especially after adjusting for covariates.

CONCLUSIONS

This study provides evidence that the individual change in sTNF-R1 levels during and after treatment were associated with the risk of mortality, suggesting the use of the sTNF-R1 trajectory as a prognostic marker.

Cytokine Signatures for Lung Cancer Diagnosis in African American Populations

Lung cancer is the leading cause of cancer-related deaths among both men and women. African Americans (AAs) experience disproportionately higher incidence and mortality compared to other ethnic groups. Cytokines play multifaceted and crucial roles in the initiation, progression, and spread of cancer. Our aim was to identify cytokine biomarkers for the early detection of lung cancer in AAs. We examined eight key cytokines (Interleukin-1, IL-6, IL-8, IL-10, IL-12p70, monocyte chemotactic protein-1 (MCP-1), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α)) in the plasma of 104 lung cancer patients and 48 cancer-free individuals using the FirePlex Immunoassay. These findings were subsequently validated in a separate cohort of 58 cases and 58 controls. IL-8, IFN-γ, and TNF-α exhibited elevated levels in both AA and White American (WA) lung cancer cases. Notably, IL-10 and MCP-1 displayed significant increases specifically in AA lung cancer patients, with MCP-1 levels associated with lung adenocarcinoma cases. Conversely, WA lung cancer patients showed heightened IL-6 levels, particularly linked to lung adenocarcinoma. The combined use of specific cytokines showed promise in lung cancer diagnosis, with IL-8, IL-10, and MCP-1 achieving 76% sensitivity and 79% specificity in AAs and IL-6 and IL-8 combined offering 76% sensitivity and 74% specificity in WAs. These diagnostic biomarkers were validated in the independent cohort. The ethnicity-related cytokine biomarkers hold promise for diagnosing lung cancer in AAs and WAs, potentially addressing the observed racial disparity.

Exploration of kiwi root on non-small cell lung cancer based on network pharmacology and molecular docking

BACKGROUND

Kiwi root is a Chinese herb clinically used in the treatment of lung neoplasm; however, the multi-target mechanism of kiwi root in the treatment of non-small cell lung cancer (NSCLC) remains to be elucidated. Thus, this study aimed to investigate the molecular mechanisms of kiwi root in the treatment of NSCLC through network pharmacology and molecular docking techniques.

METHODS

The active components and targets of kiwi root were obtained from the TCMSP database, and NSCLC-related targets were obtained from the GeneCards, OMIM, and DrugBank databases. The intersection targets of NSCLC and kiwi root were obtained from VENNY 2.1.0. Then, the common targets were imported into the STRING database, and by using the Cytoscape 3.7.1 software, drug-disease network diagrams were created. Afterwards, the DAVID database was utilized to perform bioinformatic annotation. Finally, molecular docking of key components and key targets was performed by Autodock Tools.

RESULTS

A total of 4083 NSCLC-related disease genes were collected from the GeneCards, OMIM,and DrugBank databases, and 177 non-duplicated drug targets were acquired from the TCMSP database. A total of 138 intersection target genes were obtained, in which TP53, AKT1, and TNF were the key targets.

CONCLUSION

Through network pharmacology techniques, the mechanism of kiwi root in the treatment of NSCLC has been uncovered and provides a theoretical basis for the clinical treatment of NSCLC with kiwi root, which requires further experimental validation.

The mystery of titan hunter: Rationalized striking of the MAPK pathway via Newly synthesized 6-Indolylpyridone-3-Carbonitrile derivatives

MAPK pathway sparkles with RTK activation, passes through subsequent downstream RAS-RAF-MEK-ERK signaling cascades, with consequent direct and indirect CDK4/6 signaling activation, and ends with cell survival, division, and proliferation. However, the emergence of anomalies such as mutations or overexpression in one or more points of the pathway could lead to cancer development and drug resistance. Therefore, designing small inhibitors to strike multitudinous MAPK pathway steps could be a promising synergistic strategy to confine cancer. In this study, twelve 6-indolylpyridone-3-carbonitrile candidates were synthesized and assessed in vitro for antineoplastic activity using four cancer cell lines. The initial antiproliferative screening revealed that compounds 3g, 3h, and 3i were the most potent candidates (GI% Avg = 70.10, 73.94, 74.33%, respectively) compared to staurosporine (GI% Avg = 70.99%). The subsequent safety and selectivity assessment showed that 3h exhibited sub-micromolar inhibition against lung cancer cells (HOP-92 GI50 = 0.75 μM) and 13.7 times selectivity toward cancerous cells over normal cells. As a result, 3h was nominated for deep mechanistic studies which evidenced that compound 3h impressively blocks multiple keystones of the MAPK pathway with nanomolar potency (EGFRWT IC50 = 281 nM, c-MET IC50 = 205 nM, B-RAFWT IC50 = 112 nM, and CDK4/6 IC50 = 95 and 184 nM, respectively). Surprisingly, 3h showed a remarkable potency against mutated EGFR and B-RAF, being 4 and 1.3 more selective to the mutated enzymes over the wild-type forms (EGFRT790M IC50 = 69 nM and B-RAFV600E IC50 = 83 nM). Ultimately, combined molecular docking and molecular dynamics (MD) calculations were executed to inspect the mode of binding and the complex stability of 3h towards the keystones of the MAPK pathway.

Serum cytokines and neutrophil-to-lymphocyte ratio as predictive biomarkers of benefit from PD-1 inhibitors in gastric cancer

Background

Immunotherapy is significantly revolutionizing cancer treatment and demonstrating promising efficacy in gastric cancer (GC) patients. However, only a subset of patients could derive benefits from targeted monoclonal antibody therapy against programmed death receptor 1 (PD-1). This study aims to identify suitable serum cytokines and blood cell ratios as predictive biomarkers to aid in the selection of GC patients likely to benefit from PD-1 inhibitors.

Materials and methods

This retrospective study included 41 GC patients who received PD-1 inhibitors combined with chemotherapy, 36 GC patients treated solely with chemotherapy, and 33 healthy controls. The study assessed the levels of seven cytokines: interleukin-2 (IL-2), IL-4, IL-6, IL-10, IL-17A, tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), and various inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), total lymphocyte count (TLC), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). Measurements were obtained using the inpatient system. Univariate and multivariate Cox regression analyses were performed to evaluate the predictive significance of these hematologic parameters for clinical outcomes.

Results

Levels of IL-6, IL-10, TNF-α, NLR, and PLR were significantly elevated in GC patients compared to healthy controls, while TLC and LMR were higher in the control group. Among the 41 patients receiving PD-1 inhibitors and chemotherapy, baseline IL-2 was associated with OS and PFS. Additionally, IL-6 and IL-17A correlated with OS, while NLR was linked to PFS (all P<0.05). These factors were identified as independent prognostic indicators in both univariate and multivariate analyses. Furthermore, almost all cytokine levels increased following the initiation of PD-1 inhibitor treatment.

Conclusions

The introduction of PD-1 inhibitors alongside chemotherapy in GC impacts serum cytokine levels. IL-2, IL-6, IL-17A, and NLR exhibit potential as reliable circulating predictive biomarkers for identifying patients who may benefit from PD-1 inhibitors combined with chemotherapy.

Integrated Network Pharmacology, Molecular Docking, Molecular Simulation, and In Vitro Validation Revealed the Bioactive Components in Soy-Fermented Food Products and the Underlying Mechanistic Pathways in Lung Cancer

Globally, lung cancer remains one of the leading causes of cancer-related mortality, warranting the exploration of novel and effective therapeutic approaches. Soy-fermented food products have long been associated with potential health benefits, including anticancer properties. There is still a lack of understanding of the active components of these drugs as well as their underlying mechanistic pathways responsible for their anti-lung cancer effects. In this study, we have undertaken an integrated approach combining network pharmacology and molecular docking to elucidate the mechanism of action of soy-fermented food products against lung cancer through simulation and in vitro validation. Using network pharmacology, we constructed a comprehensive network of interactions between the identified isoflavones in soy-fermented food products and lung cancer-associated targets. Molecular docking was performed to predict the binding affinities of these compounds with key lung cancer-related proteins. Additionally, molecular simulation was utilized to investigate the stability of the compound-target complexes over time, providing insights into their dynamic interactions. Our results identified daidzein as a potential active component in soy-fermented food products with high binding affinities towards critical lung cancer targets. Molecular dynamic simulations confirmed the stability of the daidzein-MMP9 and daidzein-HSP90AA1 complexes, suggesting their potential as effective inhibitors. Additionally, in vitro validation experiments demonstrated that treatment with daidzein significantly inhibited cancer cell proliferation and suppressed cancer cell migration and the invasion of A549 lung cancer cells. Consequently, the estrogen signaling pathway was recognized as the pathway modulated by daidzein against lung cancer. Overall, the findings of the present study highlight the therapeutic potential of soy-fermented food products in lung cancer treatment and provide valuable insights for the development of targeted therapies using the identified bioactive compounds. Further investigation and clinical studies are warranted to validate these findings and translate them into clinical applications for improved lung cancer management.

TNFR1 and TNFR2, Which Link NF-κB Activation, Drive Lung Cancer Progression, Cell Dedifferentiation, and Metastasis

TNFR1 and TNFR2, encoded by TNFRSF1A and TNFRSF1B, respectively, are the most well-characterized members among the TNFR superfamily. TNFR1 is expressed in most cell types, while TNFR2 has been reported to be preferentially expressed in leukocytes. Lung cancer remains the leading cause of cancer mortality worldwide but TNFRs' activities in lung cancer development have not been fully evaluated. Recently, overexpressed TNFR1 was reported in a large proportion of human lung squamous cell carcinomas. Increased TNFR1 coupled with increased UBCH10 caused lung SCC cell dedifferentiation with epithelial-mesenchymal transition features and the metastasis in a combined spontaneous lung SCC and TNFR1 transgenic mouse model. UBCH10, an E2 ubiquitin-conjugating enzyme that is an oncogene, increased Sox2, c-Myc, Twist1, and Bcl2 levels. Increased TNFR1 upregulated UBCH10 expression by activating c-Rel and p65 NF-κB. Lung SCC patients overexpressing TNFRSF1A and one of these target genes died early compared to lung SCC patients expressing lower levels of these genes. Recently, we also revealed that TNFR2 was required for lung adenocarcinoma progression, delivering a signaling pathway of TNF/TNFR2/NF-κB-c-Rel, in which macrophage-produced ROS and TNF converted CD4 T cells to Foxp3 Treg cells, generating an immunosuppressive tumor microenvironment and promoting lung ADC progression. In human lung ADC cohorts, TNFRSF1B expression was highly correlated with TNF, FOXP3, and CD4 expression. Of note, TNF stimulated the activities of TNFR1 and TNFR2, two membrane-binding receptors, which accelerate tumorigenesis through diverse mechanisms. This review focuses on these new findings regarding the roles of TNFR1 and TNFR2 in lung SCC and ADC development in humans and mice, and highlights the potential therapeutic targets of human lung cancers.

Germline variants of the genes involved in NF-kB activation are associated with the risk of COPD and lung cancer development

Chronic obstructive pulmonary disease (COPD) and lung cancer (LC) are closely related diseases associated with smoking history and dysregulated immune response. However, not all smokers develop the disease, indicating that genetic susceptibility could be important. Therefore, the aim of this study was to search for the potential overlapping genetic biomarkers, with a focus on single nucleotide polymorphisms (SNPs) located in the regulatory regions of immune-related genes. Additionally, the aim was to see if an identified SNP has potentially an effect on proinflamma-tory cytokine concentration in the serum of COPD patients. We extracted summary data of variants in 1511 immune-related genes from COPD and LC genome-wide association studies (GWAS) from the UK Biobank. The LC data had 203 cases, patients diagnosed with LC, and 360 938 controls, while COPD data had 1 897 cases and 359 297 controls. Assuming 1 association/gene, SNPs with a p-value < 3.3 × 10^-5 were considered statistically significantly associated with the disease. We identified seven SNPs located in different genes (BAG6, BTNL2, TNF, HCP5, MICB, NCR3, ABCF1, TCF7L1) to be associated with the COPD risk and two with the LC risk (HLA-C, HLA-B), with statistical significance. We also identified two SNPs located in the IL2RA gene associated with LC (rs2386841; p = 1.86 × 10^-4) and COPD (rs11256442; p = 9.79 × 10^-3) but with lower significance. Functional studies conducted on COPD patients showed that RNA expression of IL2RA, IFNγ and related proinflammatory cytokines in blood serum did not correlate with a specific genotype. Although results presented in this study do not fully support our hypothesis, it is worth to mention that the identified genes/SNPs that were associated with either COPD or LC risk, all were involved in the activation of the NF-κB transcription factor which is closely related to the regulation of the inflammatory response, a condition associated with both pathologies.

Integrated circulating tumour DNA and cytokine analysis for therapy monitoring of ALK-rearranged lung adenocarcinoma

BACKGROUND

Detection of circulating tumour DNA (ctDNA) in biological fluids is a minimally invasive alternative to tissue biopsy for therapy monitoring. Cytokines are released in the tumour microenvironment to influence inflammation and tumorigenic mechanisms. Here, we investigated the potential biomarker utility of circulating cytokines vis-à-vis ctDNA in ALK-rearranged+ lung adenocarcinoma (ALK + NSCLC) and explored the optimal combination of molecular parameters that could indicate disease progression.

METHODS

Longitudinal serum samples (n = 296) were collected from ALK + NSCLC patients (n = 38) under tyrosine kinase inhibitor (TKI) therapy and assayed to quantify eight cytokines: IFN-γ, IL-1β, IL-6, IL-8, IL-10, IL-12p70, MCP1 and TNF-α. Generalised linear mixed-effect modelling was performed to test the performance of different combinations of cytokines and previously determined ctDNA parameters in identifying progressive disease.

RESULTS

Serum IL-6, IL-8 and IL-10 were elevated at progressive disease, with IL-8 having the most significant impact as a biomarker. Integrating changes in IL-8 with ctDNA parameters maximised the performance of the classifiers in identifying disease progression, but this did not significantly outperform the model based on ctDNA alone.

CONCLUSIONS

Serum cytokine levels are potential disease progression markers in ALK + NSCLC. Further validation in a larger and prospective cohort is necessary to determine whether the addition of cytokine evaluation could improve current tumour monitoring modalities in the clinical setting.

IL-36 expression is increased in NSCLC with IL-36 stimulation of lung cancer cells promoting a pro-tumorigenic phenotype

The IL-36 cytokines are a recently described subset of the IL-1 family of cytokines, and have been shown to play a role in the pathogenesis of respiratory diseases such as asthma and COPD. Given the common aetiological links between COPD and lung cancer development, as well as the involvement of other IL-1 family members in lung tumorigenesis, the aim of this work was to investigate the role of IL-36 cytokines in the pathogenesis of lung cancer. In this study we demonstrate that expression of IL-36 cytokines and receptor mRNA and protein are significantly increased in lung cancer tissue compared to adjacent non-tumour tissue. In vitro assays showed that stimulation of two lung cancer cell lines, SKMES-1 human squamous cell and LLC murine lung cancer, with IL-36R agonists resulted in increased cellular migration and proliferation. All IL-36 cytokines induced the expression of pro-inflammatory chemokines in both lung cancer cell lines with synergistic effects identified upon co-stimulation of cells with IL-17, IL-22 and TNFα. Furthermore, we report that IL-36 cytokines induce protein expression of the immune checkpoint inhibitor protein PD-L1 on lung cancer cells. Taken together, this data indicates that targeting IL-36R signalling may be a useful targeted therapy for lung cancer patients with IL-36R⁺ cancer cells.

Serum cytokine levels and other associated factors as possible immunotherapeutic targets and prognostic indicators for lung cancer

Lung cancer is one of the most prevalent cancer types and the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) accounts for 80-85% of all cancer incidences. Lung cancer therapy and prognosis largely depend on the disease's degree at the diagnosis time. Cytokines are soluble polypeptides that contribute to cell-to-cell communication, acting paracrine or autocrine on neighboring or distant cells. Cytokines are essential for developing neoplastic growth, but they are also known to operate as biological inducers following cancer therapy. Early indications are that inflammatory cytokines such as IL-6 and IL-8 play a predictive role in lung cancer. Nevertheless, the biological significance of cytokine levels in lung cancer has not yet been investigated. This review aimed to assess the existing literature on serum cytokine levels and additional factors as potential immunotherapeutic targets and lung cancer prognostic indicators. Changes in serum cytokine levels have been identified as immunological biomarkers for lung cancer and predict the effectiveness of targeted immunotherapy.

Sorafenib Alleviates Inflammatory Signaling of Tumor Microenvironment in Precancerous Lung Injuries

According to population-based studies, lung cancer is the prominent reason for cancer-related mortality worldwide in males and is also rising in females at an alarming rate. Sorafenib (SOR), which is approved for the treatment of hepatocellular carcinoma and renal cell carcinoma, is a multitargeted protein kinase inhibitor. Additionally, SOR is the subject of interest for preclinical and clinical trials in lung cancer. This study was designed to assess in vivo the possible effects of sorafenib (SOR) in diethylnitrosamine (DEN)-induced lung carcinogenesis and examine its probable mechanisms of action. A total of 30 adult male rats were divided into three groups (1) control, (2) DEN, and (3) DEN + SOR. The chemical induction of lung carcinogenesis was performed by injection of DEN intraperitoneally at 150 mg/kg once a week for two weeks. The DEN-administered rats were co-treated with SOR of 10 mg/kg by oral gavage for 42 alternate days. Serum and lung tissue samples were analyzed to determine SRY-box transcription factor 2 (SOX-2) levels. The tumor necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1β) levels were measured in lung tissue supernatants. Lung sections were analyzed for cyclooxygenase-2 (COX-2) and c-Jun N-terminal kinase (JNK) histopathologically. In addition, cyclooxygenase-2 (COX-2) and c-Jun N-terminal kinase (JNK) were analyzed by immunohistochemistry and immunofluorescence methods, respectively. SOR reduced the level of SOX-2 that maintenance of cancer stemness and tumorigenicity, and TNF-α and IL-1β levels. Histopathological analysis demonstrated widespread inflammatory cell infiltration, disorganized alveolar structure, hyperemia in the vessels, and thickened alveolar walls in DEN-induced rats. The damage was markedly reduced upon SOR treatment. Further, immunohistochemical and immunofluorescence analysis also revealed increased expression of COX-2 and JNK expression in DEN-intoxicated rats. However, SOR treatment alleviated the expression of these inflammatory markers in DEN-induced lung carcinogenesis. These findings suggested that SOR inhibits DEN-induced lung precancerous lesions through decreased inflammation with concomitant in reduced SOX-2 levels, which enables the maintenance of cancer stem cell properties.

Inhibition of ribosome assembly factor PNO1 by CRISPR/Cas9 technique suppresses lung adenocarcinoma and Notch pathway: Clinical application

Growth is crucially controlled by the functional ribosomes available in cells. To meet the enhanced energy demand, cancer cells re-wire and increase their ribosome biogenesis. The RNA-binding protein PNO1, a ribosome assembly factor, plays an essential role in ribosome biogenesis. The purpose of this study was to examine whether PNO1 can be used as a biomarker for lung adenocarcinoma and also examine the molecular mechanisms by which PNO1 knockdown by CRISPR/Cas9 inhibited growth and epithelial-mesenchymal transition (EMT). The expression of PNO1 was significantly higher in lung adenocarcinoma compared to normal lung tissues. PNO1 expression in lung adenocarcinoma patients increased with stage, nodal metastasis, and smoking. Lung adenocarcinoma tissues from males expressed higher PNO1 than those from females. Furthermore, lung adenocarcinoma tissues with mutant Tp53 expressed higher PNO1 than those with wild-type Tp53, suggesting the influence of Tp53 status on PNO1 expression. PNO1 knockdown inhibited cell viability, colony formation, and EMT, and induced apoptosis. Since dysregulated signalling through the Notch receptors promotes lung adenocarcinoma, we measured the effects of PNO1 inhibition on the Notch pathway. PNO1 knockdown inhibited Notch signalling by suppressing the expression of Notch receptors, their ligands, and downstream targets. PNO1 knockdown also suppressed CCND1, p21, PTGS-2, IL-1α, IL-8, and CXCL-8 genes. Overall, our data suggest that PNO1 can be used as a diagnostic biomarker, and also can be an attractive therapeutic target for the treatment of lung adenocarcinoma.

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases

Background: Brain metastasis, with an incidence of more than 30%, is a common complication of non-small cell lung cancer (NSCLC). Therefore, there is an urgent need for an assessment method that can effectively predict brain metastases in NSCLC and help understand its mechanism. Materials and methods: GSE30219, GSE31210, GSE37745, and GSE50081 datasets were downloaded from the GEO database and integrated into a dataset (GSE). The integrated dataset was divided into the training and test datasets. TCGA-NSCLC dataset was regarded as an independent verification dataset. Here, the limma R package was used to identify the differentially expression genes (DEGs). Importantly, the RiskScore model was constructed using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Moreover, we explored in detail the tumor mutational signature, immune signature, and sensitivity to treatment of brain metastases in NSCLC. Finally, a nomogram was built using the rms package. Results: First, 472 DEGs associated with brain metastases in NSCLC were obtained, which were closely associated with cancer-associated pathways. Interestingly, a RiskScore model was constructed using 11 genes from 472 DEGs, and the robustness was confirmed in GSE test, entire GSE, and TCGA datasets. Samples in the low RiskScore group had a higher gene mutation score and lower immunoinfiltration status. Moreover, we found that the patients in the low RiskScore group were more sensitive to the four chemotherapy drugs. In addition, the predictive nomogram model was able to effectively predict the outcome of patients through appropriate RiskScore stratification. Conclusion: The prognostic RiskScore model we established has high prediction accuracy and survival prediction ability for brain metastases in NSCLC.

Identification of N6-methylandenosine related lncRNA signatures for predicting the prognosis and therapy response in colorectal cancer patients

Despite recent advances in surgical and multimodal therapies, the overall survival (OS) of advanced colorectal cancer (CRC) patients remains low. Thus, discerning sensitive prognostic biomarkers to give the optimistic treatment for CRC patients is extremely critical. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play an important role in CRC progression. Nonetheless, few studies have focused on the impact of m6A-related lncRNAs on the prognosis, tumor microenvironment (TME) and treatment of CRC. In this study, 1707 m6A-related lncRNAs were identified through Pearson correlation analysis and Weighted co-expression network analysis (WGCNA) using The Cancer Genome Atlas (TCGA) cohort. Then, 28 m6A-related prognostic lncRNAs were screened by univariate Cox regression analysis, followed by identifying two clusters by consensus clustering analysis. A prognostic model consisted of 8 lncRNA signatures was constructed by the least absolute shrinkage and selection operator (LASSO). Kaplan–Meier curve analysis and a nomogram were performed to investigate the prognostic ability of this model. The risk score of prognostic model act as an independent risk factor for OS rate. Functional enrichment analysis indicated that lncRNA signatures related tumor immunity. The low-risk group characterized by increased microsatellite instability-high (MSI-H), mutation burden, and immunity activation, indicated favorable odds of OS. Moreover, the lncRNA signatures were significantly associated with the cancer stem cell (CSC) index and drug sensitivity. In addition, 3 common immune genes shared by the lncRNA signatures were screened out. We found that these immune genes were widely distributed in 2 cell types of TME. Finally, a ceRNA network was constructed to identify ZEB1-AS1 regulatory axis in CRC. We found that ZEB1-AS1 was significantly overexpressed in tumor tissues, and was related to the metastasis of EMT and the chemoresistance of 5-Fu in CRC. Therefore, our study demonstrated the important role of m6A-related lncRNAs in TME remodeling. Moreover, these results illustrated the levels of ZEB1-AS1 might be valuable for predicting the progression and prognosis of CRC, and further provided a new target for the diagnosis and treatment of CRC patients.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

scAllele: A versatile tool for the detection and analysis of variants in scRNA-seq

Single-cell RNA sequencing (scRNA-seq) data contain rich information at the gene, transcript, and nucleotide levels. Most analyses of scRNA-seq have focused on gene expression profiles, and it remains challenging to extract nucleotide variants and isoform-specific information. Here, we present scAllele, an integrative approach that detects single-nucleotide variants, insertions, deletions, and their allelic linkage with splicing patterns in scRNA-seq. We demonstrate that scAllele achieves better performance in identifying nucleotide variants than other commonly used tools. In addition, the read-specific variant calls by scAllele enables allele-specific splicing analysis, a unique feature not afforded by other methods. Applied to a lung cancer scRNA-seq dataset, scAllele identified variants with strong allelic linkage to alternative splicing, some of which are cancer specific and enriched in cancer-relevant pathways. scAllele represents a versatile tool to uncover multilayer information and previously unidentified biological insights from scRNA-seq data.

Identification and validation of a novel prognostic model of inflammation-related gene signature of lung adenocarcinoma

Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.

Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci

Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.

Primary and Acquired Resistance against Immune Check Inhibitors in Non-Small Cell Lung Cancer

Simple Summary

NSCLC accounts for approximately 84% of lung malignancies and the clinical application of ICIs provides a novel and promising strategy. However, approximately 80% of NSCLC patients do not benefit from ICIs due to drug resistance complicated by disciplines and diverse mechanisms. Through this review, we provide a whole map of current understanding of primary and acquired resistance mechanisms in NSCLC. In the first part, resistance mechanisms of 6 FDA-approved ICIs-related primary resistance are collected and arranged into 7 steps of the well-known cancer-immunity cycle. Acquired resistance induced by ICIs are summarized in the second part. In the third part, we discuss the future direction, including the deeper understanding of tumor microenvironment and the combinational treatment. Through this review, clinicians can get clear and direct clues to find the underlying mechanisms in patients and translational researchers can acquire several directions to overcome resistance and apply new combinational treatment.

Abstract

Immune checkpoint inhibitors have emerged as the treatment landscape of advanced non-small cell lung cancer (NSCLC) in recent years. However, approximately 80% of NSCLC patients do not benefit from ICIs due to primary resistance (no initial response) or acquired resistance (tumor relapse after an initial response). In this review, we highlight the mechanisms of primary and secondary resistance. Furthermore, we provide a future direction of the potential predictive biomarkers and the tumor microenvironmental landscape and suggest treatment strategies to overcome these mechanisms.

In vitro preliminary study on different anti-PD-1 antibody concentrations on T cells activation

Lung adenocarcinoma predominates among diagnosed nonsmall cell lung cancer subtypes in nonsmokers. The introduction of immune checkpoint inhibitors into clinical practice offered patients prolonged progression-free survival and overall survival times. However, the results demonstrate that the benefits do not apply to all patients. Nivolumab is a monoclonal antibody against the PD-1 protein expressed mainly on T lymphocytes and is widely used in cancer therapy in different settings. Tumor cells often express the PD-L1 molecule and can effectively block the action of PD-1-positive lymphocytes. A body of knowledge regarding the high expression of PD-L1 on tumor cells highlights that it does not always correlate with the effectiveness of anti-PD-1 therapy. The side effects of the therapy also constitute a significant issue. These side effects can occur at any time during anti-PD-1 treatment and lead to discontinuation and even the death of the patient. In these situations, it is possible to delay the dosage. Nevertheless, unfortunately, it is not possible to reduce the dose of anti-PD-1 antibody, which would undoubtedly minimize side effects, leaving the patient's immune system active. In our preliminary study, we analyzed the effect of different concentrations of nivolumab on the functioning of T lymphocytes. Activation and proliferation markers were investigated on T cells after being cultured with antigen-stimulated autologous dendritic cells. This process may indicate an appropriate concentration of nivolumab, which shows clinical activity with minimal side effects.

Comprehensive Analysis of Immune Implication and Prognostic Value of IFI44L in Non-Small Cell Lung Cancer

Interferon-induced protein 44-like (IFI44L), a type I interferon-stimulated gene (ISG), has been reported to be involved in innate immune processes and to act as a tumor suppressor in several cancers. However, its immune implication on lung cancer remains unclear. Here, we systemically analyzed the immune association of IFI44L with multiple tumor-infiltrating immune cells (TIICs) and immunomodulators through bioinformatics methods in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Then, the IFI44L-related immunomodulators were selected to construct the prognostic signatures in the lung adenocarcinoma (LUAD) cohort and the lung squamous cell carcinoma (LUSC) cohort, respectively. Concordance index and time-dependent receiver operating characteristics (ROC) curves were applied to evaluate the prognostic signatures. GSE72094 and GSE50081 were used to validate the TCGA-LUAD signature and TCGA-LUSC signature, respectively. A nomogram was established by risk score and clinical features in the LUAD cohort. Finally, the prognostic value and biological function of IFI44L were verified in a real-world cohort and in vitro experiments. The results indicated that IFI44L showed significant correlation with TIICs in LUAD and LUSC samples. Functional enrichment analysis showed that IFI44L may participate in various cancer/immune-related pathways, including JAK/STAT signaling pathway and NF-κB signaling pathway. A total of 44 immunomodulators presented obvious association with IFI44L in the TCGA-LUAD cohort and a robust 10-immunomodulator signature was constructed. Patients in the higher-risk group presented worse prognosis than those in the lower-risk group. Notably, the risk signature was successfully validated in GSE72094. Multivariate Cox regression suggested that the risk signature could act as independent prognostic factors in both TCGA-LUAD and GSE72094 cohorts. Besides, a 17-immunomodulator signature was established in the TCGA-LUSC cohort and similar results were presented through analysis. The nomogram exhibited good accuracy in predicting overall survival (OS) outcome among TCGA-LUAD patients than the risk signature and other clinical features, with the area under curve values being 0.782 at 1 year, 0.825 at 3 years, and 0.792 at 5 years. Finally, tissue microarray analysis indicated that higher expression of IFI44L presented opposite relationship with pathological stage (p = 0.016) and a better outcome among lung cancer patients (p = 0.024). Functional experiments found that IFI44L overexpression significantly inhibited the proliferation, migration, and invasion in LUAD and LUSC cells; RT-qPCR experiments verified the correlation between the expression level of IFI44L with multiple immunomodulators in SPC-A-1 and NCI-H520 cells. In conclusion, our research highlighted that IFI44L is associated with tumor immune infiltration and provided information on IFI44L's immune implication, which indicates that IFI44L has potential clinical immunotherapeutic value and the proposed nomogram is a promising biomarker for non-small cell lung cancer patients.

Comprehensive targeting of resistance to inhibition of RTK signaling pathways by using glucocorticoids

Inhibition of RTK pathways in cancer triggers an adaptive response that promotes therapeutic resistance. Because the adaptive response is multifaceted, the optimal approach to blunting it remains undetermined. TNF upregulation is a biologically significant response to EGFR inhibition in NSCLC. Here, we compared a specific TNF inhibitor (etanercept) to thalidomide and prednisone, two drugs that block TNF and also other inflammatory pathways. Prednisone is significantly more effective in suppressing EGFR inhibition-induced inflammatory signals. Remarkably, prednisone induces a shutdown of bypass RTK signaling and inhibits key resistance signals such as STAT3, YAP and TNF-NF-κB. Combined with EGFR inhibition, prednisone is significantly superior to etanercept or thalidomide in durably suppressing tumor growth in multiple mouse models, indicating that a broad suppression of adaptive signals is more effective than blocking a single component. We identify prednisone as a drug that can effectively inhibit adaptive resistance with acceptable toxicity in NSCLC and other cancers.

Molecular Mechanism of Antioxidant and Anti-Inflammatory Effects of Omega-3 Fatty Acids in Perilla Seed Oil and Rosmarinic Acid Rich Fraction Extracted from Perilla Seed Meal on TNF-α Induced A549 Lung Adenocarcinoma Cells

Industrially, after the removal of oil from perilla seeds (PS) by screw-type compression, the large quantities of residual perilla seed meal (PSM) becomes non-valuable waste. Therefore, to increase the health value and price of PS and PSM, we focused on the biological effects of perilla seed oil (PSO) and rosmarinic acid-rich fraction (RA-RF) extracted from PSM for their role in preventing oxidative stress and inflammation caused by TNF-α exposure in an A549 lung adenocarcinoma culture model. The A549 cells were pretreated with PSO or RA-RF and followed by TNF-α treatment. We found that PSO and RA-RF were not toxic to TNF-α-induced A549 cells. Both extracts significantly decreased the generation of reactive oxygen species (ROS) in this cell line. The mRNA expression levels of IL-1β, IL-6, IL-8, TNF-α, and COX-2 were significantly decreased by the treatment of PSO and RA-RF. The Western blot indicated that the expression of MnSOD, FOXO1, and NF-κB and phosphorylation of JNK were also significantly diminished by PSO and RA-RF treatment. The results demonstrated that PSO and RA-RF act as antioxidants to scavenge TNF-α induced ROS levels, resulting in decreased the expression of MnSOD, FOXO1, NF-κB and JNK signaling pathway in a human lung cell culture exposed to TNF-α.

New Insights on Old Biomarkers Involved in Tumor Microenvironment Changes and Their Diagnostic Relevance in Non-Small Cell Lung Carcinoma

BACKGROUND

Lung cancer is a multifactorial disease with a heterogeneous tumor group that hampers diagnostic and therapeutic approaches, as well as understanding of the processes that underlie its pathogenesis. Current research efforts are focused on examining alterations in the tumor microenvironment, which may affect the pathogenesis and further malignant progression in lung cancer. The aim of this study was to investigate changes in the levels of biomarkers involved in the lung tumor microenvironment and their diagnostic utility in differentiating lung cancer subtypes and stages.

METHODS

This study comprised 112 lung cancer patients, 50 with adenocarcinoma, 35 with squamous cell carcinoma, 13 with other non-small cell lung carcinoma subtypes, and 14 with other lung neoplasms than non-small cell lung carcinoma. Tumor markers (CEA, CYFRA 21-1, and NSE) were measured in the patients' sera and plasmas, along with IL-6, TNF-α, SAA1, CRP, MMP-2, MMP-9, glucose, lactate, and LDH, utilizing enzyme-linked immunosorbent assays, enzyme immunoassays, and automated clinical chemistry and turbidimetry systems. The results were statistically analyzed across patient groups based on the subtype and stage of lung cancer.

RESULTS

Glucose concentrations showed statistically significant (p < 0.05) differences both between lung cancer subtypes and stages, with the highest levels in patients with other lung neoplasms (me = 130.5 mg/dL) and in patients with stage IIB lung cancer (me = 132.0 mg/dL). In patients with advanced lung cancer, IL-6 and LDH had considerably higher concentration and activity. There was also a significant positive correlation between IL-6 and MMP-9 in adenocarcinoma and SqCC, with correlation coefficients of 0.53 and 0.49, respectively. The ROC analyses showed that the best single biomarkers for distinguishing adenocarcinoma from squamous cell carcinoma are glucose, CRP, and CYFRA 21-1; however, their combination did not significantly improve sensitivity, specificity, and the AUC value. The combinations of IL-6, glucose, LDH and CEA, IL-6, SAA1, MMP-9, and lactate can distinguish patients with stage IIB lung cancer from those with stage IIA with 100% sensitivity, 100% specificity, and with an AUC value of 0.8333 and 1.0000, respectively, whereas the combination of CEA, IL-6, and LDH can identify patients with stage IIIA lung cancer from those with stage IIB with 72.73% sensitivity, 94.44% specificity, and an AUC value of 0.8686.

CONCLUSION

There is a link between biomarkers of tumor microenvironment changes and tumor markers, and combinations of these markers may be clinically useful in the differential diagnosis of adenocarcinoma and squamous cell carcinoma, as well as lung cancer stages IIB and IIA, and IIIA and IIB.

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (m⁶A) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with m⁶A in LUAD patients is critical. In this study, m⁶A-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an m⁶A-related lncRNA model. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 m⁶A-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the m⁶A-based lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.

DNA methylation profiles of bronchoscopic biopsies for the diagnosis of lung cancer

Background

Lung cancer is the leading cause of cancer-related death in most western countries in both, males and females, accounting for roughly 20–25% of all cancer deaths. For choosing the most appropriate therapy regimen a definite diagnosis is a prerequisite. However, histological characterization of bronchoscopic biopsies particularly with low tumor cell content is often challenging. Therefore, this study aims at (a) determining the value of DNA methylation analysis applied to specimens obtained by bronchoscopic biopsy for the diagnosis of lung cancer and (b) at comparing aberrantly CpG loci identified in bronchoscopic biopsy with those identified by analyzing surgical specimens.

Results

We report the HumanMethylation450-based DNA methylation analysis of paired samples of bronchoscopic biopsy specimens either from the tumor side or from the contralateral tumor-free bronchus in 37 patients with definite lung cancer diagnosis and 18 patients with suspicious diagnosis. A differential DNA methylation analysis between both biopsy sites of patients with definite diagnosis identified 1303 loci. Even those samples were separated by the set of 1303 loci in which histopathological analysis could not unambiguously define the dignity. Further differential DNA methylation analyses distinguished between SCLC and NSCLC. We validated our results in an independent cohort of 40 primary lung cancers obtained by open surgical resection and their corresponding controls from the same patient as well as in publically available DNA methylation data from a TCGA cohort which could also be classified with high accuracy.

Conclusions

Considering that the prognosis correlates with tumor stage at time of diagnosis, early detection of lung cancer is vital and DNA methylation analysis might add valuable information to reliably characterize lung cancer even in histologically ambiguous sample material.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01024-6.