Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Construction of a Diagnostic Model for Small Cell Lung Cancer Combining Metabolomics and Integrated Machine Learning.

BACKGROUND: To date, no study has systematically explored the potential role of serum metabolites and lipids in the diagnosis of small cell lung cancer (SCLC). Therefore, we aimed to conduct a case-cohort study that included 191 cases of SCLC, 91 patients with lung adenocarcinoma, 82 patients with squamous cell carcinoma, and 97 healthy controls. METHODS: Metabolomics and lipidomics were applied to analyze different metabolites and lipids in the serum of these patients. The SCLC diagnosis model (d-model) was constructed using an integrated machine learning technology and a training cohort (n = 323) and was validated in a testing cohort (n=138). RESULTS: Eight metabolites, including 1-mristoyl-sn-glycero-3-phosphocholine, 16b-hydroxyestradiol, 3-phosphoserine, cholesteryl sulfate, D-lyxose, dioctyl phthalate, DL-lactate and Leu-Phe, were successfully selected to distinguish SCLC from controls. The d-model was constructed based on these 8 metabolites and showed improved diagnostic performance for SCLC, with the area under curve (AUC) of 0.933 in the training cohort and 0.922 in the testing cohort. Importantly, the d-model still had an excellent diagnostic performance after adjusting the stage and related clinical variables and, combined with the progastrin-releasing peptide (ProGRP), showed the best diagnostic performance with 0.975 of AUC for limited-stage patients. CONCLUSION: This study is the first to analyze the difference between metabolomics and lipidomics and to construct a d-model to detect SCLC using integrated machine learning. This study may be of great significance for the screening and early diagnosis of SCLC patients.

Tumor mutational burden adjusted by neutrophil-to-lymphocyte ratio serves as a potential biomarker for atezolizumab-treated patients with extensive stage small cell lung cancer.

BACKGROUND: There is a desperate for the identification of more accurate and efficient biomarkers for ICI responses in patients with SCLC. METHODS: The data of our study was obtained from IMpower133 study. A total of 202 patients with SCLC received the treatment of placebo plus carboplatin plus etoposide (EC) while a total of 201 patients with SCLC received the treatment of atezolizumab plus EC. Overall survival (OS) was compared using Kaplan Meier analyses. Univariate and multivariate Cox regression analysis were used to determine independent prognostic variables affecting OS in patients with SCLC. RESULTS: We have demonstrated that a higher TMB adjusted by a lower neutrophil-to-lymphocyte ratio (NLR) is significantly correlated with improved OS, in patients with SCLC subject to either atezolizumab or placebo (P = 0.001 for atezolizumab and P = 0.034 for placebo). Moreover, Cox model showed that TMB < 10 mut/Mb adjusted by NLR ≥ median was an independent factor of OS for atezolizumab-treated SCLC patients (hazard ratio [HR], 2.82; 95% confidence interval; 1.52-5.24; P = 0.001). Both univariate and multivariate cox regression analysis showed that for patients with SCLC harboring low NLR and high TMB, survival is significantly longer in those treated with atezolizumab than those treated with placebo. Survival benefit is significantly higher in atezolizumab-treated patients with SCLC than those treated with placebo (P = 0.018 for TMB cutoff = 10 mut/Mb, P = 0.034 for TMB cutoff = 16 mut/Mb). CONCLUSION: Our findings provide a promising insight into the utility of NLR-adjusted TMB in the prognosis and immune responses in patients with SCLC.

Influential Metrics Estimation and Dynamic Frequency Selection Based on Two-Dimensional Mapping for JPEG-Reversible Data Hiding.

JPEG Reversible Data Hiding (RDH) is a method designed to extract hidden data from a marked image and perfectly restore the image to its original JPEG form. However, while existing RDH methods adaptively manage the visual distortion caused by embedded data, they often neglect the concurrent increase in file size. In rectifying this oversight, we have designed a new JPEG RDH scheme that addresses all influential metrics during the embedding phase and a dynamic frequency selection strategy with recoverable frequency order after data embedding. The process initiates with a pre-processing phase of blocks and the subsequent selection of frequencies. Utilizing a two-dimensional (2D) mapping strategy, we then compute the visual distortion and file size increment (FSI) for each image block by examining non-zero alternating current (AC) coefficient pairs (NZACPs) and their corresponding run lengths. Finally, we select appropriate block groups based on the influential metrics of each block group and proceed with data embedding by 2D histogram shifting (HS). Extensive experimentation demonstrates how our method's efficiently and consistently outperformed existing techniques with a superior peak signal-to-noise Ratio (PSNR) and optimized FSI.

Acid-Base Cascades in Zeotype Single Crystal for Sugar Conversion.

The chemically catalyzed production of fructose syrup from high concentrations of glucose is crucial for the food industry and biorefining. In this work, a single crystal catalyst was synthesized via protective desilication of zeolite while incorporating indium. Glucose was isomerized in methanol at concentrations as high as 33 wt % before being hydrolyzed with water. The final fructose production was 54.9 %, with 89.1 % selectivity and 93.3 % sugar recovery, the highest isomerization rate at the highest concentration ever reported. Indium was present in the single-crystal catalyst as oxide nanoparticles and boundary framework atoms, and it achieved intelligent cooperation in the production of fructose syrup in methanol by catalyzing isomerization and selective glycosidation, minimizing degradation due to fructose accumulation and eliminating side reactions. This study contributed to the advancement of the industrial practice of chemically catalyzed glucose isomerization.

Molecular subtyping of small-cell lung cancer based on mutational signatures with different genomic features and therapeutic strategies.

Small-cell lung cancer (SCLC) is an exceptionally lethal malignancy characterized by extremely high alteration rates and tumor heterogeneity, which limits therapeutic options. In contrast to non-small-cell lung cancer that develops rapidly with precision oncology, SCLC still remains outside the realm of precision medicine. No recurrent and actionable mutations have been detected. Additionally, a paucity of substantive tumor specimens has made it even more difficult to classify SCLC subtypes based on genetic background. We therefore carried out whole-exome sequencing (WES) on the largest available Chinese SCLC cohort. For the first time, we partitioned SCLC patients into three clusters with different genomic alteration profiles and clinical features based on their mutational signatures. We showed that these clusters presented differences in intratumor heterogeneity and genome instability. Moreover, a wide existence of mutually exclusive gene alterations, typically within similar biological functions, was detected and suggested a high SCLC intertumoral heterogeneity. Particularly, Cluster 1 presented the greatest potential to benefit from immunotherapy, and Cluster 3 constituted recalcitrant SCLC, warranting biomarker-directed drug development and targeted therapies in clinical trials. Our study would provide an in-depth insight into the genome characteristics of the Chinese SCLC cohort, defining distinct molecular subtypes as well as subtype-specific therapies and biomarkers. We propose tailoring differentiated therapies for distinct molecular subgroups, centering on a personalized precision chemotherapy strategy combined with immunization or targeted therapy for patients with SCLC.

Orchestrating smart therapeutics to achieve optimal treatment in small cell lung cancer: recent progress and future directions.

Small cell lung cancer (SCLC) is a recalcitrant malignancy with elusive mechanism of pathogenesis and dismal prognosis. Over the past decades, platinum-based chemotherapy has been the backbone treatment for SCLC. However, subsequent chemoresistance after initial effectiveness urges researchers to explore novel therapeutic targets of SCLC. Recent years have witnessed significant improvements in targeted therapy in SCLC. New molecular candidates such as Ataxia telangiectasia and RAD3-related protein (ATR), WEE1, checkpoint kinase 1 (CHK1) and poly-ADP-ribose polymerase (PARP) have shown promising therapeutic utility in SCLC. While immune checkpoint inhibitor (ICI) has emerged as an indispensable treatment modality for SCLC, approaches to boost efficacy and reduce toxicity as well as selection of reliable biomarkers for ICI in SCLC have remained elusive and warrants our further investigation. Given the increasing importance of precision medicine in SCLC, optimal subtyping of SCLC using multi-omics have gradually applied into clinical practice, which may identify more drug targets and better tailor treatment strategies to each individual patient. The present review summarizes recent progress and future directions in SCLC. In addition to the emerging new therapeutics, we also focus on the establishment of predictive model for early detection of SCLC. More importantly, we also propose a multi-dimensional model in the prognosis of SCLC to ultimately attain the goal of accurate treatment of SCLC.

Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome.

BACKGROUND: Peritoneal metastasis (PM) frequently occurs in patients with gastric cancer (GC) and is a major cause of mortality. Risk stratification for PM can optimize decision making in GC treatment. METHODS: A total of 25 GC patients (13 with synchronous, 6 with metachronous PM and 6 PM-free) were included in this study. Quantitative proteomics by high-depth tandem mass tags labeling and whole-exome sequencing were conducted in primary GC and PM samples. Proteomic signature and prognostic model were established by machine learning algorithms in PM and PM-free GC, then validated in two external cohorts. Tumor-infiltrating immune cells in GC were analyzed by CIBERSORT. RESULTS: Heterogeneity between paired primary and PM samples was observed at both genomic and proteomic levels. Compared to primary GC, proteome of PM samples was enriched in RNA binding and extracellular exosomes. 641 differently expressed proteins (DEPs) between primary GC of PM group and PM-free group were screened, which were enriched in extracellular exosome and cell adhesion pathways. Subsequently, a ten-protein signature was derived based on DEPs by machine learning. This signature was significantly associated with patient prognosis in internal cohort and two external proteomic datasets of diffuse and mixed type GC. Tumor-infiltrating immune cell analysis showed that the signature was associated with immune microenvironment of GC. CONCLUSIONS: We characterized proteomic features that were informative for PM progression of GC. A protein signature associated with immune microenvironment and patient outcome was derived, and it could guide risk stratification and individualized treatment.

Rational Positioning of Metal Ions to Stabilize Open Tin Sites in Beta Zeolite for Catalytic Conversion of Sugars.

Via hydrothermal synthesis of Sn-Al gels, mild dealumination and ion exchange, a bimetallic Sn-Ni-Beta catalyst was prepared which can convert glucose to methyl lactate (MLA) and methyl vinyl glycolate (MVG) in methanol at yields of 71.2 % and 10.2 %, respectively. Results from solid-state magic-angle spinning nuclear magnetic resonance, X-ray photoelectron spectroscopy, transmission electron microscopy, spectroscopic analysis, probe-temperature-programmed desorption, and density functional theory calculations conclusively reveal that the openness of the Sn sites, such as by the formation of [(SiO)3 -Sn-OH] entities, is governed by an adjacent metal cation such as Ni2+ , Co2+ , and Mn2+ . This relies on the low structure-defective pore channel, provided by the current synthesis scheme, and the specific silica hydroxyl anchor point is associated with the incorporation of Sn for additional and precise metal ion localization. The presence of metal cations significantly improved the catalytic performance of Sn-Ni-Beta for glucose isomerization and conversion to MLA of sugar compared with Sn-Beta.

Untargeted metabolomics and lipidomics identified four subtypes of small cell lung cancer.

INTRODUCTION: Small cell lung cancer (SCLC) is a heterogeneous malignancy with dismal prognosis. However, few studies have conducted on the metabolic heterogeneity in SCLC. OBJECTIVE: We therefore identify SCLC classifications using untargeted metabolomics and lipidomics. We also compared their survival and the immunotherapy responses. METHODS: Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) analysis was performed in 191 SCLC serum samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted to identify metabolic pathways. The Kaplan-Meier and log-rank test were used to analyze the survival curves. The univariate and multivariate Cox proportional hazards regression models were used to evaluate prognostic factors for OS in patients with SCLC. RESULTS: Distinct subtypes of SCLC were identified by consensus clustering algorithm using partioning around medoids (pam) based on untargeted metabolomics and lipidomics. Four distinct subtypes of SCLC were identified, with distinct metabolic pathways. Subgroup 2 had the longest survival whereas Subgroup 1 had the shortest. Subtype 2 benefited most from immunotherapy in OS, as in contrast to Subtype 3 with shortest survival. CONCLUSION: Our study revealed the metabolic heterogeneity in SCLC and identified four subtypes with distinct metabolic features. It indicates promising therapeutic and prognostic value that may guide treatment for SCLC. The subtype-specific clinical trials may be designed and would be instructive for drug development.

Non-small-cell lung cancer patients harboring TP53/KRAS co-mutation could benefit from a PD-L1 inhibitor.

Aim: To explore the association between TP53 mutation and atezolizumab in non-small-cell lung cancer (NSCLC) patients. Materials & methods: Patients with NSCLC from the POPLAR and OAK studies were included. Kaplan-Meier analysis was performed to detect progression-free survival (PFS) and overall survival (OS). PFS and OS were compared using multivariate Cox regression analysis. Results: OS was significantly longer with atezolizumab compared with docetaxel among TP53/KRAS co-mutant NSCLC patients (hazard ratio [HR]: 0.014; 95% CI: 0.000-0.721). There is no significant OS difference between atezolizumab versus docetaxel for TP53-mutant NSCLC patients (HR: 0.831; 95% CI: 0.473-1.458). There is no significant OS difference between atezolizumab versus docetaxel for KRAS-mutant NSCLC patients (HR: 1.354; 95% CI: 0.528-3.472). Conclusion: PD-L1 inhibitors may bring OS benefits for patients with NSCLC harbored TP53/KRAS co-mutation.

Mutations in TP53 have been reported to be the most frequent oncogenic mutations in non-small-cell lung cancer (NSCLC), but there are no favorable targeted therapies. The association between TP53 mutation and atezolizumab in NSCLC patients was explored in this study. Patients with NSCLC from the POPLAR and OAK studies were included. Longer overall survival (OS) was found in TP53-wildtype NSCLC patients treated with atezolizumab compared with those treated with docetaxel. OS benefit of atezolizumab over docetaxel was detected for NSCLC patients only with co-occurrence of TP53 and KRAS mutations. For patients with NSCLC harboring TP53 mutation detected by fluid biopsy, this study provides insights and perspectives into anti-PD-L1 treatment.

EPHA5 mutation was associated with adverse outcome of atezolizumab treatment in late-stage non-small cell lung cancers.

BACKGROUND: The aim of the study was to investigate predictive value of gene mutation for atezolizumab treatment response from OAK and POPLAR cohorts. METHODS: Several public databases were used for analyzing gene mutation type of EPHA5 and association with alterations of other genes. Survival analysis was performed for patients receiving atezolizumab from OAK and POPLAR cohorts. RESULTS: EPHA5 mutation have high frequency to harbor TP53 and KEAP1 mutations. The bTMB value has significant difference between EPHA5 mutant and wild-type cases. Patients with EPHA5 mutation got worse survival compared to those without gene mutations receiving atezolizumab (P = 0.0186). CONCLUSIONS: EPHA5 mutant NSCLC may represent a subpopulation which showed worse response after treatment of atezolizumab compared to wild-type ones.

Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin ß family-related genes.

BACKGROUND: Integrin ß (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and the LUAD prognosis remains unclear. METHODS: In this study, the samples were assigned to different subgroups utilizing non-negative matrix factorization clustering according to the expression of ITGB family members in LUAD. Kaplan-Meier (K-M) survival analysis revealed the significant differences in the prognosis between different ITGB subgroups. Subsequently, we screened differentially expressed genes among different subgroups and conducted univariate Cox analysis, random forest feature selection, and multivariate Cox analysis. 9-feature genes (FAM83A, AKAP12, PKP2, CYP17A1, GJB3, TMPRSS11F, KRT81, MARCH4, and STC1) in the ITGB superfamily were selected to establish a prognostic assessment model for LAUD. RESULTS: In accordance with the median risk score, LUAD samples were divided into high- and low-risk groups. The receiver operating characteristic (ROC) curve of LUAD patients' survival was predicted via K-M survival curve and principal component analysis dimensionality reduction. This model was found to have a favorable performance in LUAD prognostic assessment. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes between groups and Gene Set Enrichment Analysis (GSEA) of intergroup samples confirmed that the high- and low-risk groups had evident differences mainly in the function of extracellular matrix (ECM) interaction. Risk score and univariate and multivariate Cox regression analyses of clinical factors showed that the prognostic model could be applied as an independent prognostic factor for LUAD. Then, we draw the nomogram of 1-, 3-, and 5-year survival of LUAD patients predicted with the risk score and clinical factors. Calibration curve and clinical decision curve proved the favorable predictive ability of nomogram. CONCLUSION: We constructed a LUAD prognostic risk model based on the ITGB superfamily, which can provide guidance for clinicians on their prognostic judgment.

Pan-Genomic and Transcriptomic Analyses of Marine Pseudoalteromonas agarivorans Hao 2018 Revealed Its Genomic and Metabolic Features.

The genomic and carbohydrate metabolic features of Pseudoalteromonas agarivorans Hao 2018 (P. agarivorans Hao 2018) were investigated through pan-genomic and transcriptomic analyses, and key enzyme genes that may encode the process involved in its extracellular polysaccharide synthesis were screened. The pan-genome of the P. agarivorans strains consists of a core-genome containing 2331 genes, an accessory-genome containing 956 genes, and a unique-genome containing 1519 genes. Clusters of Orthologous Groups analyses showed that P. agarivorans harbors strain-specifically diverse metabolisms, probably representing high evolutionary genome changes. The Kyoto Encyclopedia of Genes and Genomes and reconstructed carbohydrate metabolic pathways displayed that P. agarivorans strains can utilize a variety of carbohydrates, such as d-glucose, d-fructose, and d-lactose. Analyses of differentially expressed genes showed that compared with the stationary phase (24 h), strain P. agarivorans Hao 2018 had upregulated expression of genes related to the synthesis of extracellular polysaccharides in the logarithmic growth phase (2 h), and that the expression of these genes affected extracellular polysaccharide transport, nucleotide sugar synthesis, and glycosyltransferase synthesis. This is the first investigation of the genomic and metabolic features of P. agarivorans through pan-genomic and transcriptomic analyses, and these intriguing discoveries provide the possibility to produce novel marine drug lead compounds with high biological activity.

Effect of Different Initial Fermentation pH on Exopolysaccharides Produced by Pseudoalteromonas agarivorans Hao 2018 and Identification of Key Genes Involved in Exopolysaccharide Synthesis via Transcriptome Analysis.

Exopolysaccharides (EPSs) are carbohydrate polymers produced and secreted by microorganisms. In a changing marine environment, EPS secretion can reduce damage from external environmental disturbances to microorganisms. Meanwhile, EPSs have promising application prospects in the fields of food, cosmetics, and pharmaceuticals. Changes in external environmental pH have been shown to affect the synthesis of EPSs in microorganisms. In this study, we analyzed the effects of different initial fermentation pHs on the production, monosaccharide composition, and antioxidant activity of the EPSs of Pseudoalteromonas agarivorans Hao 2018. In addition, the transcriptome sequence of P. agarivorans Hao 2018 under different initial fermentation pH levels was determined. GO and KEGG analyses showed that the differentially expressed genes were concentrated in the two-component regulatory system and bacterial chemotaxis pathways. We further identified the expression of key genes involved in EPS synthesis during pH changes. In particular, the expression of genes encoding the glucose/galactose MFS transporter, phosphomannomutase, and mannose-1-phosphate guanylyltransferase was upregulated when the environmental pH increased, thus promoting EPS synthesis. This study not only contributes to elucidating the environmental adaptation mechanisms of P. agarivorans, but also provides important theoretical guidance for the directed development of new products using biologically active polysaccharides.

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study.

BACKGROUND: The prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis. METHODS: The 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test. RESULTS: Univariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group. CONCLUSIONS: We established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.

A novel genomic classification system of gastric cancer via integrating multidimensional genomic characteristics.

BACKGROUND: Gastric cancer (GC) is one of the leading causes of cancer deaths with high heterogeneity. There is currently a paucity of clinically applicable molecular classification system to guide precise medicine. METHODS: A total of 70 Chinese patients with GC were included in this study and whole-exome sequencing was performed. Unsupervised clustering was undertaken to identify genomic subgroups, based on mutational signature, copy number variation, neoantigen, clonality, and essential genomic alterations. Subgroups were characterized by clinicopathological factors, molecular features, and prognosis. RESULTS: We identified 32 significantly mutated genes (SMGs), including TP53, ARID1A, PIK3CA, CDH1, and RHOA. Of these, PREX2, PIEZO1, and FSIP2 have not been previously reported in GC. Using a novel genome-based classification method that integrated multidimensional genomic features, we categorized GC into four subtypes with distinct clinical phenotypes and prognosis. Subtype 1, which was predominantly Lauren intestinal type, harbored recurrent TP53 mutation and ERBB2 amplification, high tumor mutation burden (TMB)/tumor neoantigen burden (TNB), and intratumoral heterogeneity, with a liver metastasis tendency. Subtype 2 tended to occur at an elder age, accompanying with frequent TP53 and SYNE1 mutations, high TMB/TNB, and was associated with poor prognosis. Subtype 3 and subtype 4 included patients with mainly diffuse/mixed type tumors, high frequency of peritoneal metastasis, and genomical stability, whereas subtype 4 was associated with a favorable prognosis. CONCLUSIONS: By integrating multidimensional genomic characteristics, we proposed a novel genomic classification system of GC associated with clinical phenotypes and provided a new insight to facilitate genome-guided risk stratification and disease management.

CD44, a marker of cancer stem cells, is positively correlated with PD-L1 expression and immune cells infiltration in lung adenocarcinoma.

BACKGROUND: PD-L1 inhibitors is widely applied in lung adenocarcinoma patients. Tumor cells with high PD-L1 expression could trigger immune evasion. Cancer stem cells (CSCs) can evade from immunesurveillance due to their immunomodulating effects. However, the correlation between CSC and PD-L1 and some immune-related markers is seldom reported in patients with lung adenocarcinoma. Therefore, we aimed to ascertain their association in lung adenocarcinoma patients. METHODS: We assessed CD44 expression and its association with PD-L1 in lung adenocarcinoma, using Tumor Immune Estimation Resource (TIMER), which was further validated in our patient cohort. The immune cells infiltration was depicted by CIBERSORT using GEO database. The correlation between CD44 and immune cells was also analyzed. We further evaluated the prognostic role of CD44 in patients with lung adenocarcinoma both using Kaplan-Meier plotter and validated in our patient cohort. RESULTS: Positive association between CD44 and PD-L1 were found in lung adenocarcinoma patients. T cells CD4 memory resting cells and mast cells resting cells varied significantly between patients with CD44 high and those with CD44 low. Furthermore, positive association could be found between CD44 expression and immune cells. Arm-level depletion of CD44 was linked with B cell, CD4+ T cell, neutrophil and dendritic cell infiltration. Patients with higher CD44 levels had worsened overall survival (OS). CONCLUSIONS: In summary, these results demonstrate that CD44 was associated with PD-L1 and infiltration of immune cells, and was a negative prognostic factor for predicting worsened OS in lung adenocarcinoma.

YAP/TAZ affects the development of pulmonary fibrosis by regulating multiple signaling pathways.

YAP and TAZ are important co-activators of various biological processes in human body. YAP/TAZ plays a vital role in the development of pulmonary fibrosis. Dysregulation of the YAP/TAZ signaling pathway is one of the most important causes of pulmonary fibrosis. Therefore, considering its crucial role, summary of the signal mechanism of YAP/TAZ is of certain guiding significance for the research of YAP/TAZ as a therapeutic target. The present review provided a detailed introduction to various YAP/TAZ-related signaling pathways and clarified the specific role of YAP/TAZ in these pathways. In the meantime, we summarized and evaluated possible applications of YAP/TAZ in the treatment of pulmonary fibrosis. Overall, our study is of guiding significance for future research on the functional mechanism of YAP/TAZ underlying lung diseases as well as for identification of novel therapeutic targets specific to pulmonary fibrosis.

Comprehensive analysis of multiple parameters associated with tumor immune microenvironment in ARID1A mutant cancers.

Aim: To verify the relationship between ARID1A and tumor immune microenvironment thus immune checkpoint inhibitors (ICIs) response. Material & methods: Several public databases were used to characterize the association between ARID1A gene alteration and tumor immunity. Results: The gene mutation frequency was 8.2% in all cancer types. The ARID1A-mutated cancers have higher scores of mutation count, tumor mutational burden, neoantigen load (p < 0.001) and T cell repertoire, B cell repertoire diversity (p < 0.05). The gene mutation has tight association with multiple-activated immune cells. Survival analysis suggested that patients with ARID1A mutant cancers benefit more from ICIs treatment (p = 0.013). Conclusion: The ARID1A gene mutation was correlated with higher tumor immunogenicity and activated antitumor immune microenvironment, resulting in superior cohort that respond well to ICIs.