Resveratrol has been implicated in the differentiation and development of human umbilical cord mesenchymal stem cells. The differentiation of into esophageal fibroblasts is a promising strategy for esophageal tissue engineering. However, the pharmacological effect and underlying mechanism of resveratrol on human umbilical cord mesenchymal stem cells differentiation are unknown. Here, we investigated the effects and mechanism of resveratrol on the differentiation of human umbilical cord mesenchymal stem cells.
Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients. DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, "timeROC", and validated using GEO datasets. Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy. We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.
Metastatic colorectal cancer (mCRC) has a poor prognosis. Combining chemotherapy with targeted therapy constitutes a basic form of mCRC treatment. Immune checkpoint inhibitors have been recommended for microsatellite instability mCRC, while most patients harboring microsatellite stability (MSS) or proficient mismatch repair (pMMR) are less responsive to immunotherapy. Combinational targeted therapy, including poly-ADP ribose polymerase (PARP) inhibitors, has been considered a promising way to reverse immunotherapy resistance; however, there is no clear and consistent conclusions can be drawn from the current research. Here, we report the case of a 59-year-old woman diagnosed with stage IVB MSS mCRC who received three courses of capecitabine/oxaliplatin chemotherapy combined with bevacizumab as a first-line treatment, resulting in an overall evaluation of stable disease (-25.7%). However, the occurrence of adverse events of intolerable grade 3 diarrhea and vomiting forced the cessation of this therapy. A germline BRCA2 mutation was found by next-generation sequencing, and the patient further received a combination of olaparib, tislelizumab, and bevacizumab. This treatment regime resulted in a complete metabolic response and a partial response (-50.9%) after 3 months of treatment. Mild asymptomatic interstitial pneumonia and manageable hematologic toxicity were two adverse events associated with this combination therapy. This study provides new insights into the combination of PARP inhibitors and immunotherapy for MSS mCRC patients carrying germline BRCA2 mutations.
Cuproptosis and associated immune-related genes (IRG) have been implicated in tumorigenesis and tumor progression. However, their effects on lung adenocarcinoma (LUAD) have not been elucidated. Therefore, we investigated the impact of cuproptosis-associated IRGs on the immunotherapy response and prognosis of LUAD using a bioinformatical approach and in vitro experiments analyzing clinical samples. Using the cuproptosis-associated IRG signature, we classified LUAD into two subtypes, cluster 1 and cluster 2, and identified three key cuproptosis-associated IRGs, NRAS, TRAV38-2DV8, and SORT1. These three genes were employed to establish a risk model and nomogram, and to classify the LUAD cohort into low- and high-risk subgroups. Biofunctional annotation revealed that cluster 2, remarkably downregulating epigenetic, stemness, and proliferation pathways activity, had a higher overall survival (OS) and immunoinfiltration abundance compared to cluster 1. Real-time quantitative PCR (RT-qPCR) validated the differential expression of these three genes in both subgroups. scRNA-seq demonstrated elevated expression of NRAS and SORT1 in macrophages. Immunity and oncogenic and stromal activation pathways were dramatically enriched in the low-risk subgroup, and patients in this subgroup responded better to immunotherapy. Our data suggest that the cuproptosis-associated IRG signature can be used to effectively predict the immunotherapy response and prognosis in LUAD. Our work provides enlightenment for immunotherapy response assessment, prognosis prediction, and the development of potential prognostic biomarkers for LUAD patients.
Hepatocellular Carcinoma (HCC) features a complex pathophysiology and unpredictable immunosuppressive microenvironment, which limit the effectiveness of traditional therapies and lead to poor patient outcomes. Understanding the immune characteristics of HCC is essential for elucidating the immune microenvironment and developing more effective treatments. This study investigates the role of Peptidyl-prolyl isomerase H (PPIH) in HCC by analyzing its expression, prognosis, methylation levels, and relationship with immune cell infiltration. We utilized bulk sequencing and clinical data from UCSC Xena and the GTEx database for preprocessing and subsequent differential expression analysis of PPIH in tumor and adjacent normal tissues, evaluating prognostic parameters like overall survival and disease-free interval between low and high PPIH expression groups. Immune infiltration was analyzed via CIBERSORT and ssGSEA, while DNA methylation and somatic mutation analyses were performed using MExpress and "maftools", respectively, alongside in vitro and in vivo experiments to assess PPIH's functional roles. Our findings indicated that PPIH is significantly upregulated in various cancer types, correlating with poor patient prognosis, increased somatic mutations, and altered gene methylation patterns. High PPIH levels were linked to enhanced T regulatory (Treg) cell infiltration and a decline in Th17 cell populations, impacting vital pathways related to DNA damage repair and tumor proliferation. Furthermore, PPIH knockdown in vitro led to reduced cell viability, proliferation, and invasion while promoting apoptosis. In vivo, PPIH knockdown repressed tumor growth and modified the immune microenvironment by attenuating Th17 cell infiltration and potentially increasing Treg cell accumulation. This study emphasizes PPIH's critical role in HCC progression by facilitating tumor growth and survival while modulating the immune landscape, thereby positioning PPIH as a potential therapeutic target for HCC management.
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