Tough Bonding of PVA Hydrogel-on-Textured Titanium Alloy with Varying Texture Densities in Swollen State.

Cartilage defects in large joints are a common occurrence in numerous degenerative diseases, especially in osteoarthritis. The hydrogel-on-metal composite has emerged as a potential candidate material, as hydrogels, to some extent, replicate the composition of human articular cartilage consisting of collagen fibers and proteoglycans. However, achieving tough bonding between the hydrogel and titanium alloy remains a significant challenge due to the swelling of the hydrogel in a liquid medium. This swelling results in reduced interfacial toughness between the hydrogel and titanium alloy, limiting its potential clinical applications. Herein, our approach aimed to achieve durable bonding between a hydrogel and a titanium alloy composite in a swollen state by modifying the surface texture of the titanium alloy. Various textures, including circular and triangular patterns, with dimple densities ranging from 10 to 40%, were created on the surface of the titanium alloy. Subsequently, poly(vinyl alcohol) (PVA) hydrogel was deposited onto the textured titanium alloy using a casting-drying method. Our findings revealed that PVA hydrogel on the textured titanium alloy with a 30% texture density exhibited the highest interfacial toughness in the swollen state, measuring at 1300 J m-2 after reaching equilibrium swelling in deionized water, which is a more than 2-fold increase compared to the hydrogel on a smooth substrate. Furthermore, we conducted an analysis of the morphologies of the detached hydrogel from the textured titanium alloy after various swelling durations. The results indicated that interfacial toughness could be enhanced through mechanical interlocking, facilitated by the expanded volume of the hydrogel protrusions as the swelling time increased. Collectively, our study demonstrates the feasibility of achieving tough bonding between a hydrogel and a metal substrate in a liquid environment. This research opens up promising avenues for designing soft/hard heterogeneous materials with strong adhesive properties.

Exosomal transfer of miR-181b-5p confers senescence-mediated doxorubicin resistance via modulating BCLAF1 in breast cancer.

BACKGROUND: Doxorubicin resistance represents a major clinical challenge for treating patients with advanced breast cancer (BC). Exosomes, exchanging genetic cargo between heterogeneous populations of tumour cells, have been proposed to mediate drug resistance and cancer progression in other cancer types. However, their specific role in mediating doxorubicin resistance in BC remains unclear. Here, we demonstrate the important role of exosomal miR-181b-5p (exo-miR-181b-5p) in mediating doxorubicin resistance. METHODS: Small-RNA sequencing and bioinformatic analyses were used to screen miRNAs mediating doxorubicin resistance in BC, which were further verified by RT-qPCR. SA-ß-gal staining assays allowed us to measure cellular senescence. Exosomes from patients' serum before and after neoadjuvant chemotherapy were isolated for exo-miR-181b-5p quantification. RESULTS: Doxorubicin-resistant BC cell lines exhibited upregulated exosomal miR-181b-5p. Addition of exo-miR-181b-5p actively fused with recipient cells and transferred a drug-resistant phenotype. Overexpression of miR-181b-5p downregulated p53/p21 levels and inhibited doxorubicin-induced G1 arrest and senescence by suppressing BCLAF1 expression in vitro. Further, in vivo experiments showed treatment of exo-miR-181b-5p inhibitors exhibited superior tumour control and reversed the doxorubicin-resistance phenotype, accompanied with increased tumoral BCLAF1. CONCLUSION: Our data suggests exo-miR-181b-5p as a prognostic biomarker and a therapeutic potential for exo-miR-181b-5p inhibitors in the treatment of doxorubicin-resistant BC patients.

The role of TXNIP in cancer: a fine balance between redox, metabolic, and immunological tumor control.

Thioredoxin-interacting protein (TXNIP) is commonly considered a master regulator of cellular oxidation, regulating the expression and function of Thioredoxin (Trx). Recent work has identified that TXNIP has a far wider range of additional roles: from regulating glucose and lipid metabolism, to cell cycle arrest and inflammation. Its expression is increased by stressors commonly found in neoplastic cells and the wider tumor microenvironment (TME), and, as such, TXNIP has been extensively studied in cancers. In this review, we evaluate the current literature regarding the regulation and the function of TXNIP, highlighting its emerging role in modulating signaling between different cell types within the TME. We then assess current and future translational opportunities and the associated challenges in this area. An improved understanding of the functions and mechanisms of TXNIP in cancers may enhance its suitability as a therapeutic target.

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes.

BACKGROUND: Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis. METHODS: The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded. RESULTS: We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1-C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment. CONCLUSIONS: This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management.

Establishment of machine learning-based tool for early detection of pulmonary embolism.

BACKGROUND AND OBJECTIVES: Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients. METHODS: We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model. RESULTS: We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism. CONCLUSIONS: In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study.

Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).

Epigenetically regulated gene expression profiles decipher four molecular subtypes with prognostic and therapeutic implications in gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract which seriously endangers the health of human beings worldwide. Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of GC. This study aimed to investigate the impact of epigenetically regulated genes on the prognosis, immune microenvironment, and potential treatment of GC. RESULTS: Under the premise of verifying significant co-regulation of the aberrant frequencies of microRNA (miRNA) correlated (MIRcor) genes and DNA methylation-correlated (METcor) genes. Four GC molecular subtypes were identified and validated by comprehensive clustering of MIRcor and METcor GEPs in 1521 samples from five independent multicenter GC cohorts: cluster 1 was characterized by up-regulated cell proliferation and transformation pathways, with good prognosis outcomes, driven by mutations, and was sensitive to 5-fluorouracil and paclitaxel; cluster 2 performed moderate prognosis and benefited more from apatinib and cisplatin; cluster 3 was featured by an up-regulated ligand-receptor formation-related pathways, poor prognosis, an immunosuppression phenotype with low tumor purity, resistant to chemotherapy (e.g., 5-fluorouracil, paclitaxel, and cisplatin), and targeted therapy drug (apatinib) and sensitive to dasatinib; cluster 4 was characterized as an immune-activating phenotype, with advanced tumor stages, benefit more from immunotherapy and displayed worst prognosis. CONCLUSIONS: According to the epigenetically regulated GEPs, we developed four robust GC molecular subtypes, which facilitated the understanding of the epigenetic mechanisms underlying GC heterogeneity, offering an optimized decision-making and surveillance platform for GC patients.

CTRP4/interleukin-6 receptor signaling ameliorates autoimmune encephalomyelitis by suppressing Th17 cell differentiation.

C1q/TNF-related protein 4 (CTRP4) is generally thought to be released extracellularly and plays a critical role in energy metabolism and protecting against sepsis. However, its physiological functions in autoimmune diseases have not been thoroughly explored. In this study, we demonstrate that Th17 cell-associated experimental autoimmune encephalomyelitis was greatly exacerbated in Ctrp4-/- mice compared with WT mice due to increased Th17 cell infiltration. The absence of Ctrp4 promoted the differentiation of naive CD4+ T cells into Th17 cells in vitro. Mechanistically, CTRP4 interfered with the interaction between IL-6 and the IL-6 receptor (IL-6R) by directly competing to bind with IL-6R, leading to suppression of IL-6-induced activation of the STAT3 pathway. Furthermore, the administration of recombinant CTRP4 protein ameliorated disease symptoms. In conclusion, our results indicate that CTRP4, as an endogenous regulator of the IL-6 receptor-signaling pathway, may be a potential therapeutic intervention for Th17-driven autoimmune diseases.

Ampere-hour-scale soft-package potassium-ion hybrid capacitors enabling 6-minute fast-charging.

Extreme fast charging of Ampere-hour (Ah)-scale electrochemical energy storage devices targeting charging times of less than 10 minutes are desired to increase widespread adoption. However, this metric is difficult to achieve in conventional Li-ion batteries due to their inherent reaction mechanism and safety hazards at high current densities. In this work, we report 1 Ah soft-package potassium-ion hybrid supercapacitors (PIHCs), which combine the merits of high-energy density of battery-type negative electrodes and high-power density of capacitor-type positive electrodes. The PIHC consists of a defect-rich, high specific surface area N-doped carbon nanotube-based positive electrode, MnO quantum dots inlaid spacing-expanded carbon nanotube-based negative electrode, carbonate-based non-aqueous electrolyte, and a binder- and current collector-free cell design. Through the optimization of the cell configuration, electrodes, and electrolyte, the full cells (1 Ah) exhibit a cell voltage up to 4.8 V, high full-cell level specific energy of 140 Wh kg-1 (based on the whole mass of device) with a full charge of 6 minutes. An 88% capacity retention after 200 cycles at 10 C (10 A) and a voltage retention of 99% at 25 ± 1 °C are also demonstrated.

Exosomal transfer leads to chemoresistance through oxidative phosphorylation-mediated stemness phenotype in colorectal cancer.

Background: Recently years have seen the increasing evidence identifying that OXPHOS is involved in different processes of tumor progression and metastasis and has been proposed to be a potential therapeutical target for cancer treatment. However, the exploration in oxidative phosphorylation-mediated chemoresistance is still scarce. In our study, we identify exosomal transfer leads to chemoresistance by reprogramming metabolic phenotype in recipient cells. Methods: RNA sequencing analysis was used to screen altered targets mediating exosome transfer-induced chemoresistance. Seahorse assay allowed us to measure mitochondrial respiration. Stemness was measured by spheroids formation assay. Serum exosomes were isolated for circ_0001610 quantification. Results: The induced oxidative phosphorylation leads to more stem-like properties, which is dependent on the transfer of exosomal circ_0001610. Exosome transfer results in the removal of miR-30e-5p-mediated suppression of PGC-1a, a master of mitochondrial biogenesis and function. Consequently, increased PGC-1a reshapes cellular metabolism towards oxidative phosphorylation, leading to chemoresistance. Inhibition of OXPHOS or exosomal si-circ_0001610 increases the sensitivity of chemotherapy by decreasing cell stemness in vitro and in vivo. Conclusion: Our data suggests that exosomal circ_0001610-induced OXPHOS plays an important role in chemoresistance and supports a therapeutical potential of circ_0001610 inhibitors in the treatment of oxaliplatin-resistant colorectal cancer by manipulating cell stemness.

FAM96A suppresses epithelial-mesenchymal transition and tumor metastasis by inhibiting TGFß1 signals.

Metastasis is the main cause of death in 90% of patients with tumors, and epithelial-mesenchymal transition (EMT) plays a key role in this process. Family with sequence similarity 96 member A (FAM96A) is an evolutionarily conserved protein related to cytosolic iron assembly. However, no research has been conducted on its role in tumor metastasis. Bioinformatics analysis with Kaplan-Meier analysis showed that patients with lower FAM96A expression in different tumors had shorter survival times and poorer prognoses. Here, we identified FAM96A as a crucial regulator of the TGFß signaling pathway because it inhibits TGFß-mediated tumor metastasis and EMT. FAM96A did not affect the proliferation or apoptosis of tumor cells but significantly reduced their migration and invasion abilities in vitro. The colonization and metastatic abilities of FAM96A-knockout tumor cells were significantly enhanced in vivo. Furthermore, the overexpression of exogenous FAM96A inhibited TGFß-induced EMT through the SMAD-mediated pathway and downregulated the expression of endogenous transforming growth factor-ß1 (TGFß1). These findings demonstrated a correlation between EMT and FAM96A gene expression for the first time, which is highly important for revealing the mechanism underlying tumor metastasis.

Exploration of Aging-Care Parameters to Predict Mortality of Patients Aged 80-Years and Above with Community-Acquired Pneumonia.

Purpose: The study explores a clinical model based on aging-care parameters to predict the mortality of hospitalized patients aged 80-year and above with community-acquired pneumonia (CAP). Patients and methods: In this study, four hundred and thirty-five CAP patients aged 80-years and above were enrolled in the Central Hospital of Minhang District, Shanghai during 01,01,2018-31,12,2021. The clinical data were collected, including aging-care relevant factors (ALB, FRAIL, Barthel Index and age-adjusted Charlson Comorbidity Index) and other commonly used factors. The prognostic factors were screened by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to predict the mortality risk. Results: Univariate analysis demonstrated that several factors, including gender, platelet distribution width, NLR, ALB, CRP, pct, pre-albumin, CURB-65, low-density, lipoprotein, Barthel Index, FRAIL, leucocyte count, neutrophil count, lymphocyte count and aCCI, were associated with the prognosis of CAP. Multivariate model analyses further identified that CURB-65 (p < 0.0001, OR = 5.44, 95% CI = 3.021-10.700), FRAIL (p < 0.0001, OR = 5.441, 95% CI = 2.611-12.25) and aCCI (p = 0.003, OR = 1.551, 95% CI = 1.165-2.099) were independent risk factors, whereas ALB (p = 0.005, OR = 0.871, 95% CI = 0.788-0.957) and Barthel Index (p = 0.0007, OR = 0.958, 95% CI = 0.933-0.981) were independent protective factors. ROC curves were plotted to further predict the in-hospital mortality and revealed that combination of three parameters (Barthel Index+ FRAI +CURB-65) showed the best performance. Conclusion: This study showed that CURB-65, frailty and aCCI were independent risk factors influencing prognosis. In addition, ALB and Barthel Index were protective factors for in CAP patients over 80-years old. AUC was calculated and revealed that combination of three parameters (Barthel Index+ FRAI +CURB-65) showed the best performance.

Exploration of prognostic factors for prediction of mortality in elderly CAP population using a nomogram model.

Background: The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. The assessment tools including CURB-65 and qSOFA have been applied in early detection of high-risk patients with CAP. However, several disadvantages exist to limit the efficiency of these tools for accurate assessment in elderly CAP. Therefore, we aimed to explore a more comprehensive tool to predict mortality in elderly CAP population by establishing a nomogram model. Methods: We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. The least absolute shrinkage and selection operator (LASSO) logistic regression combined with multivariate analyses were used to select independent predictive factors and established nomogram models via R software. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance. Results: LASSO and multiple logistic regression analyses showed the age, pulse, NLR, albumin, BUN, and D-dimer were independent risk predictors. A nomogram model (NB-DAPA model) was established for predicting mortality of CAP in elderly patients. In both training and validation set, the area under the curve (AUC) of the NB-DAPA model showed superiority than CURB-65 and qSOFA. Meanwhile, DCA revealed that the predictive model had significant net benefits for most threshold probabilities. Conclusion: Our established NB-DAPA nomogram model is a simple and accurate tool for predicting in-hospital mortality of CAP, adapted for patients aged 65 years and above. The predictive performance of the NB-DAPA model was better than PSI, CURB-65 and qSOFA.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma.

Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

Prognostic Significance of Cuproptosis-Related Gene Signatures in Breast Cancer Based on Transcriptomic Data Analysis.

Breast cancer (BRCA) remains a serious threat to women's health, with the rapidly increasing morbidity and mortality being possibly due to a lack of a sophisticated classification system. To date, no reliable biomarker is available to predict prognosis. Cuproptosis has been recently identified as a new form of programmed cell death, characterized by the accumulation of copper in cells. However, little is known about the role of cuproptosis in breast cancer. In this study, a cuproptosis-related genes (CRGs) risk model was constructed, based on transcriptomic data with corresponding clinical information relating to breast cancer obtained from both the TCGA and GEO databases, to assess the prognosis of breast cancer by comprehensive bioinformatics analyses. The CRGs risk model was constructed and validated based on the expression of four genes (NLRP3, LIPT1, PDHA1 and DLST). BRCA patients were then divided into two subtypes according to the CRGs risk model. Furthermore, our analyses revealed that the application of this risk model was significantly associated with clinical outcome, immune infiltrates and tumor mutation burden (TMB) in breast cancer patients. Additionally, a new clinical nomogram model based on risk score was established and showed great performance in overall survival (OS) prediction, confirming the potential clinical significance of the CRGs risk model. Collectively, our findings revealed that the CRGs risk model can be a useful tool to stratify subtypes and that the cuproptosis-related signature plays an important role in predicting prognosis in BRCA patients.

Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment.

The incidence and mortality of colorectal cancer (CRC) are increasing year by year. The accurate classification of CRC can realize the purpose of personalized and precise treatment for patients. The tumor microenvironment (TME) plays an important role in the malignant progression and immunotherapy of CRC. An in-depth understanding of the clusters based on the TME is of great significance for the discovery of new therapeutic targets for CRC. We extracted data on CRC, including gene expression profile, DNA methylation array, somatic mutations, clinicopathological information, and copy number variation (CNV), from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) (four datasets-GSE14333, GSE17538, GSE38832, and GSE39582), cBioPortal, and FireBrowse. The MCPcounter was utilized to quantify the abundance of 10 TME cells for CRC samples. Cluster repetitive analysis was based on the Hcluster function of the Pheatmap package in R. The ESTIMATE package was applied to compute immune and stromal scores for CRC patients. PCA analysis was used to remove batch effects among different datasets and transform genome-wide DNA methylation profiling into methylation of tumor-infiltrating lymphocyte (MeTIL). We evaluated the mutation differences of the clusters using MOVICS, DeconstructSigs, and GISTIC packages. As for therapy, TIDE and SubMap analyses were carried out to forecast the immunotherapy response of the clusters, and chemotherapeutic sensibility was estimated based on the pRRophetic package. All results were verified in the TCGA and GEO data. Four immune clusters (ImmClust-CS1, ImmClust-CS2, ImmClust-CS3, and ImmClust-CS4) were identified for CRC. The four ImmClusts exhibited distinct TME compositions, cancer-associated fibroblasts (CAFs), functional orientation, and immune checkpoints. The highest immune, stromal, and MeTIL scores were observed in CS2, in contrast to the lowest scores in CS4. CS1 may respond to immunotherapy, while CS2 may respond to immunotherapy after anti-CAFs. Among the four ImmClusts, the top 15 markers with the highest mutation frequency were acquired, and CS1 had significantly lower CNA on the focal level than other subtypes. In addition, CS1 and CS2 patients had more stable chromosomes than CS3 and CS4. The most sensitive chemotherapeutic agents in these four ImmClusts were also found. IHC results revealed that CD29 stained significantly darker in the cancer samples, indicating that their CD29 was highly expressed in colon cancer. This work revealed the novel clusters based on TME for CRC, which would guide in predicting the prognosis, biological features, and appropriate treatment for patients with CRC.

SCG2: A Prognostic Marker That Pinpoints Chemotherapy and Immunotherapy in Colorectal Cancer.

Background: Fluorouracil (FU)-based chemotherapy regimens are indispensable in the comprehensive treatment of colorectal cancer (CRC). However, the heterogeneity of treated individuals and the severe adverse effects of chemotherapy results in limited overall benefit. Methods: Firstly, Weighted gene co-expression network analysis (WGCNA) identified modules tightly associated with chemotherapy response. Then, the in-house cohort and prognostic cohorts from TCGA and GEO were subjected to Cox proportional hazards model and survival analysis to ascertain the predictable function of SCG2 on the prognosis of CRC patients. Finally, we performed In vitro experiments, functional analysis, somatic mutation, and copy number variation research to explore the biological characteristics of SCG2. Results: We identified red and green as the modules most associated with chemotherapy response, in which SCG2 was considered a risky factor with higher expression predicting poorer prognosis. SCG2 expression in the APC non-mutation group was remarkably higher than in the mutation group. The mutation frequencies of amplified genes differed significantly between different SCG2 expression subgroups. Besides, CRC cell lines with SCG2 knockdown have reduced invasive, proliferative, and proliferative capacity. We discovered that the SCG2 high expression subgroup was the immune hot type and considered more suitable for immunotherapy. Conclusion: This study demonstrates the clinical significance and biological characteristics of SCG2, which could serve as a promising biomarker to identify patients who may benefit from chemotherapy and immunotherapy.

Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population.

BACKGROUND: The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization. METHODS: We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan-Meier survival curves were used in survival rate. RESULTS: In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001). CONCLUSION: We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.

Differential and Prognostic Significance of HOXB7 in Gliomas.

Diffuse glioma is the most common primary tumor of the central nervous system. The prognosis of the individual tumor is heavily dependent on its grade and subtype. Homeobox B7 (HOXB7), a member of the homeobox family, is abnormally overexpressed in a variety of tumors. However, its function in glioma is unclear. In this study, HOXB7 mRNA and protein expression levels were analyzed in 401 gliomas from the CGGA RNA-seq database (325 cases) and our hospital (76 cases). HOXB7 expression, at both mRNA and protein levels, were upregulated in glioblastoma (GBM) and isocitrate dehydrogenase 1 (IDH1) wild-type glioma tissues. Kaplan-Meier with log-rank test showed that patients with high HOXB7 expression had a poor prognosis (p < 0.0001). Moreover, HOXB7 protein was deleted in 90.9% (20/22) of oligodendrogliomas and 13.0% (3/23) of astrocytomas. The sensitivity and specificity of HOXB7 protein deletion in oligodendroglioma were 90.9% (20/22) and 87.0% (20/23), respectively. To verify the reliability of using HOXB7 in differentiating oligodendroglioma, we used 1p/19q fluorescence in situ hybridization (FISH) testing as a positive control. The Cohen's kappa coefficient of HOXB7 immunohistochemistry staining and 1p/19q FISH testing was 0.778 (95% CI: 0.594-0.962, p < 0.001). In conclusion, HOXB7 is an independent predictor of poor prognosis in all grade gliomas. Additionally, HOXB7 is also a highly sensitive and specific indicator to differentiate oligodendroglioma from astrocytoma.

Epigenetic Signaling of Cancer Stem Cells During Inflammation.

Malignant tumors pose a great challenge to human health, which has led to many studies increasingly elucidating the tumorigenic process. Cancer Stem Cells (CSCs) have profound impacts on tumorigenesis and development of drug resistance. Recently, there has been increased interest in the relationship between inflammation and CSCs but the mechanism underlying this relationship has not been fully elucidated. Inflammatory cytokines produced during chronic inflammation activate signaling pathways that regulate the generation of CSCs through epigenetic mechanisms. In this review, we focus on the effects of inflammation on cancer stem cells, particularly the role of signaling pathways such as NF-κB pathway, STAT3 pathway and Smad pathway involved in regulating epigenetic changes. We hope to provide a novel perspective for improving strategies for tumor treatment.