The Bioaccessibility and Bioavailability of Pentachlorophenol in Five Animal-Derived Foods Measured by Simulated Gastrointestinal Digestion.

Pentachlorophenol (PCP) is a ubiquitous emerging persistent organic pollutant detected in the environment and foodstuffs. Despite the dietary intake of PCP being performed using surveillance data, the assessment does not consider the bioaccessibility and bioavailability of PCP. Pork, beef, pork liver, chicken and freshwater fish Ctenopharyngodon Idella-fortified by three levels of PCP were processed by RIVM and the Caco-2 cell model after steaming, boiling and pan-frying, and PCP in foods and digestive juices were detected using isotope dilution-UPLC-MS/MS. The culinary treatment and food matrix were significantly influenced (p < 0.05) in terms of the bioaccessibility and bioavailability of PCP. Pan-frying was a significant factor (p < 0.05) influencing the digestion and absorption of PCP in foods, with the following bioaccessibility: pork (81.37-90.36%), beef (72.09-83.63%), pork liver (69.11-78.07%), chicken (63.43-75.52%) and freshwater fish (60.27-72.14%). The bioavailability was as follows: pork (49.39-63.41%), beef (40.32-53.43%), pork liver (33.63-47.11%), chicken (30.63-40.83%) and freshwater fish (17.14-27.09%). Pork and beef with higher fat content were a key factor in facilitating the notable PCP bioaccessibility and bioavailability (p < 0.05). Further, the exposure of PCP to the population was significantly reduced by 42.70-98.46% after the consideration of bioaccessibility and bioavailability, with no potential health risk. It can improve the accuracy of risk assessment for PCP.

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level.

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-ß pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma.

PURPOSE: The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics. METHODS: We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes. RESULTS: Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM. CONCLUSIONS: MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review.

Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

CTLs heterogeneity and plasticity: implications for cancer immunotherapy.

Cytotoxic T lymphocytes (CTLs) play critical antitumor roles, encompassing diverse subsets including CD4+, NK, and γδ T cells beyond conventional CD8+ CTLs. However, definitive CTLs biomarkers remain elusive, as cytotoxicity-molecule expression does not necessarily confer cytotoxic capacity. CTLs differentiation involves transcriptional regulation by factors such as T-bet and Blimp-1, although epigenetic regulation of CTLs is less clear. CTLs promote tumor killing through cytotoxic granules and death receptor pathways, but may also stimulate tumorigenesis in some contexts. Given that CTLs cytotoxicity varies across tumors, enhancing this function is critical. This review summarizes current knowledge on CTLs subsets, biomarkers, differentiation mechanisms, cancer-related functions, and strategies for improving cytotoxicity. Key outstanding questions include refining the CTLs definition, characterizing subtype diversity, elucidating differentiation and senescence pathways, delineating CTL-microbe relationships, and enabling multi-omics profiling. A more comprehensive understanding of CTLs biology will facilitate optimization of their immunotherapy applications. Overall, this review synthesizes the heterogeneity, regulation, functional roles, and enhancement strategies of CTLs in antitumor immunity, highlighting gaps in our knowledge of subtype diversity, definitive biomarkers, epigenetic control, microbial interactions, and multi-omics characterization. Addressing these questions will refine our understanding of CTLs immunology to better leverage cytotoxic functions against cancer.

Platinum-based neoadjuvant chemotherapy upregulates STING/IFN pathway expression and promotes TILs infiltration in NSCLC.

Objectives: To evaluate the effects of platinum-based neoadjuvant chemotherapy (NACT) on the STING/IFN pathway and tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC), as well as clinicopathological factors affecting patient survival. Materials and methods: A total of 68 patients aged 34-77 years with NSCLC who received neoadjuvant chemotherapy and surgical treatment from March 2012 to February 2019 were reviewed, and the clinical pathological data and paired tissue specimens before and after NACT were collected. Immunohistochemistry and immunofluorescence were used to detect the protein levels of STING, PD-L1 and IFN-ß, and the infiltration density of CD3+ TILs and CD8+TILs. The correlation between the expression of STING, PD-L1, IFN-ß and the infiltration density of CD3+ TILs and CD8+ TILs as well as the clinicopathological characteristics before and after NACT was analyzed. The relationship between the related indexes, clinicopathological features and prognosis was also discussed. Results: NACT increased the expression of STING, IFN-ß and PD-L1 in tumor cells, and the infiltration of CD3+ and CD8+ TILs. In addition, ypTNM stage, ypN stage, changes in CD3+ TILs and in PD-L1 were associated with DFS (disease-free survival). CD3+ TILs changes and ypN stage were associated with OS (overall survival). Notably, ypN stage and CD3+ TILs changes were independent prognostic factors for DFS and OS. Conclusion: NACT stimulates STING/IFN-ß pathway, promotes infiltration of CD3+ and CD8+ TILs, triggers innate and adaptive immunity, and also upregulates PD-L1, which complemented the rationale for neoadjuvant chemotherapy in combination with immunotherapy. In addition, DFS was longer in patients with ypTNM I, ypN0-1, and elevated CD3+TILs after NACT. Patients with ypN0 and elevated CD3+ TILs after NACT had better OS benefits.

Multiomics-based classifier to decipher immune landscape of uveal melanoma and predict patient outcomes.

Uveal melanoma (UVM) prognosis and the possibilities for targeted therapy depend on a thorough understanding of immune infiltration features and the analysis of genomic and immune signatures. Leveraging multi-omics data from The Cancer Genome Atlas and GEO datasets, we employed an unsupervised clustering algorithm to categorize UVM into immune-related subgroups. Subsequent multi-omics analysis revealed two distinct UVM subtypes, each characterized by unique genomic mutations and immune microenvironment disparities. The aggressive UMCS2 subtype exhibited higher TNM stage and poorer survival, marked by elevated metabolism and increased immune infiltration. However, UMCS2 displayed heightened tumor mutational burden and immune dysfunction, leading to reduced responsiveness to immunotherapy. Importantly, these subtypes demonstrated differential sensitivity to targeted drugs due to significant variances in metabolic and immune environments, with UMCS2 displaying lower sensitivity. We developed a robust, subtype-specific marker-based risk scoring system. This system's diagnostic accuracy was validated through ROC curves, decision curve analysis, and calibration curves, all yielding satisfactory results. Additionally, cell experiments identified the pivotal function of HTR2B, the most crucial factor in this risk model. Knocking down HTR2B significantly reduced the activity, proliferation, and invasion ability of the UVM cell line. These findings underscored the impact of gene and immune microenvironment alterations in driving distinct molecular subtypes, emphasizing the need for precise treatment strategies. The molecular subtyping-based risk assessment system not only aids in predicting patient prognosis but also guides the identification of populations suitable for combined treatment. Molecules represented by HTR2B in the model may serve as effective therapeutic targets for UVM.Communicated by Ramaswamy H. Sarma.

Long noncoding RNA TMPO-AS1 accelerates glycolysis by regulating the miR-1270/PKM2 axis in colorectal cancer.

BACKGROUND: Long noncoding RNA thymopoietin-antisense RNA 1 (TMPO-AS1) is recognized as a participant in cancer progression. Nevertheless, its biological function in colorectal cancer remains obscure and needs further elucidation. METHODS AND RESULTS: First, we discovered enriched TMPO-AS1 in the tumor tissues that were related to poor prognosis. TMPO-AS1 knockdown enhanced SW480 cell apoptosis but inhibited invasion, proliferation, migration, and glucose metabolism. Further, MiR-1270 is directly bound with TMPO-AS1. MiR-1270 mimics were confirmed to inhibit cell proliferation, invasion, and glucose metabolism in our study. Mechanistically, miR-1270 directly is bound with the 3' untranslated regions (3'UTR) of PKM2 to downregulate PKM2. MiR-1270 inhibitors reversed the TMPO-AS1 knockdown's effect on suppressing the tumor cell proliferation, invasion, and glycolysis, while the knockdown of PKM2 further inverted the function of miR-1270 inhibitors on the TMPO-AS1 knockdown. CONCLUSIONS: This study illustrated that TMPO-AS1 advanced the development and the glycolysis of colorectal cancer by modulating the miR-1270/PKM2 axis, which provided a new insight into the colorectal cancer therapeutic strategy.

Advancing the Boundaries of Immunotherapy in Lung Adenocarcinoma with Idiopathic Pulmonary Fibrosis by a Biomimetic Proteinoid Enabling Selective Endocytosis.

The coexistence of lung adenocarcinoma (LUAD) with idiopathic pulmonary fibrosis (IPF), which has been extensively documented as a prominent risk factor for checkpoint inhibitor-related pneumonitis (CIP) in patients undergoing immunotherapy, has long been considered a restricted domain for the use of immune checkpoint inhibitors (ICIs). To overcome it, an approach was employed herein to specifically target PD-L1 within the cellular interior, surpassing the conventional focus solely on the cytomembrane, thereby facilitating the development of ICIs capable of distinguishing between LUAD cells and noncancerous cells based on their distinctive endocytic propensities. By exploiting the aurophilicity-driven self-assembly of a PD-L1 binding peptide (PDBP) and subsequently encapsulating it within erythrocyte membranes (EM), the resulting biomimetic ICIs protein EMS-PDBP exhibited extraordinary selectivity in internalizing LUAD cells, effectively targeting PD-L1 within cancer cells while hindering its membrane translocation. The EMS-PDBP treatment not only reactivated the antitumor immune response in the LUAD orthotopic allograft mouse model but also demonstrated a favorable safety profile by effectively eliminating any immune-related adverse events (irAEs). Most significantly, EMS-PDBP successfully and safely restored the antitumor immune response in a mouse model of LUAD with coexistent IPF, thus shattering the confines of ICIs immunotherapy. The reported EMS-PDBP collectively offers a potential strategy for immune reactivation to overcome the limitations of immunotherapy in LUAD coexisting with IPF.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study.

Background: Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA. Methods: We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed. Results: Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes. Conclusion: CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

SPCS, a Novel Classifier System Based on Senescence Axis Regulators Reveals Tumor Microenvironment Heterogeneity and Guides Frontline Therapy for Clear Cell Renal Carcinoma.

RATIONALE: The emerging evidence suggested that senescence regulator genes were involved in multi cancers, which may be utilized as new targets for cancers. However, the dysregulation and clinical impact of senescence regulator genes in clear cell renal cell cancer (ccRCC) were still in foggy. METHODS: Using multiomics data from TCGA-KIRC and other datasets, we comprehensively investigated the function of senescence regulator genes in ccRCC. ccRCC patients could be remodeled into 2 significant different groups basing on senescence regulators expression: senescence-pattern cancer subtype1 (SPCS1) and subtype2 (SPCS2). We further explored clinical characteristics, functional analysis, tumor immune microenvironment, immunotherapy response, genomic mutation and drug sensitivity between the 2 subtypes. Besides, senescence-pattern related risk model was established to determine the patient's prognosis of ccRCC. Finally, the overview of MECP2 function was investigated in multi cancers. RESULTS: ccRCC patients could be divided into SPCS1 (normal aging group) and SPCS2 (Aging disorder group). The 2 subtypes showed significant different clinical characteristics and biological process in ccRCC. SPCS2, an aggressive subtype, comprised higher clinical stage and worse prognosis of ccRCC patients. SPCS2 subtype indicated activated oncogenic signaling pathway and metabolic signatures to prompt cancer expansion. SPCS2 subgroup owned immunocompromised status, which induced immune dysfunction and low ICI therapy response. The genome-copy numbers of SPCS2, including arm-gain and arm-loss was significantly more frequent than SPCS1. In addition, the 2 subtypes argue contrasting drug sensitivity profiles in clinical specimens and matched cell lines. Finally, we constructed a prognostic risk model consisted of each subtype's leading biomarkers, which exerted a satisfied performance for ccRCC patients. CONCLUSION: Senescence regulator-related signature could modify functional pathways and tumor immune microenvironment by genome mutation and pathway interaction. Senescence regulator-related molecular subtype strengthen the understanding of ccRCC' characterization and guide clinical treatment. Targeting senescence regulators may be regard as a proper way in ccRCC.

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study.

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.Communicated by Ramaswamy H. Sarma.