Involvement of S100A6/S100A11 in T-Cell Immune Regulatory in HCC Revealed by Single Cell RNA-seq.

Background: Immunotherapy plays a significant role in the treatment of hepatocellular carcinoma (HCC). Members of the S100 protein family (S100s) have been widely implicated in the pathogenesis and progression of tumors. However, the exact mechanism by which S100s contribute to tumor immunity remains unclear. Methods: To explore the role of S100s in HCC immune cells, we collected and comparatively analyzed single-cell RNA sequencing (scRNA-seq) data of HCC and hepatitis B virus-associated HCC. By mapping cell classification and searching for S100s binding targets and downstream targets. Results: S100A6/S100A11 was differentially expressed in tumor T cells and involved in the nuclear factor (NF) κB pathway. Further investigation of the TCGA dataset revealed that patients with low S100A6/S100A11 expression had a better prognosis. Temporal cell trajectory analysis showed that the activation of the NF-κB pathway is at a critical stage and has an important impact on the tumor microenvironment. Conclusion: Our study revealed that S100A6/S100A11 could be involved in regulating the differentiation and cellular activity of T-cell subpopulations in HCC, and its low expression was positively correlated with prognosis. It may provide a new direction for immunotherapy of HCC and a theoretical basis for future clinical applications.

IGF2BP2-related modification patterns in pancreatic cancer: A machine learning-driven approach towards personalized treatment.

Pancreatic cancer (PC) is a malignant digestive system tumor with a very poor prognosis. N6-methyladenosine (m6A) is mediated by a variety of readers and participates in important regulatory roles in PC. Based on TCGA_PAAD, ICGC_AU_PAAD, ICGC_CA_PAAD, GSE28735 and GSE62452 datasets, We mapped the multi-omics changes of m6A readers in PC and found that m6A readers, especially IGF2BP family genes, had specific changes and were significantly associated with poor prognosis. An unsupervised consensus clustering algorithm was used to explore the correlation between specific expression patterns of m6A readers in PC and enrichment pathways, tumor immunity and clinical molecular subtypes. Then, the principal component analysis (PCA) algorithm was used to quantify specific expression patterns and screen core genes. Machine learning algorithms such as Bootstrapping and RSF were used to quantify the expression patterns of core genes and construct a prognostic scoring model for PC patients. What's more, pharmacogenomic databases were used to screen sensitive drug targets and small molecule compounds for high-risk PC patients in an all-around and multi-angle way. Our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy based on IGF2BP2-mediated m6A modification patterns.

Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis.

Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.

Constructing an APOBEC-related gene signature with predictive value in the overall survival and therapeutic sensitivity in lung adenocarcinoma.

Background: APOBEC family play an important role in cancer mutagenesis and tumor development. The role of APOBEC family in lung adenocarcinoma (LUAD) has not been studied comprehensively. Materials and methods: The expression data of pan-cancer as well as LUAD was obtained from public databases. The expression level of APOBEC family genes was analyzed in different normal and cancer tissues. APOBEC mutagenesis enrichment score (AMES) was utilized to evaluate the APOBEC-induced mutations and the relation of APOBEC with genomic instability. Gene set enrichment analysis was used to identify differentially enriched pathways. Univariate Cox regression and Lasso regression were applied to screen key prognostic genes. The immune cell infiltration was estimated by CIBERSORT. RT-qPCR assay, CCK-8 and Transwell assay were conducted to explore gene expression and lung cancer cell invasion. Results: Cancer tissues had significantly altered expression of APOBEC family genes and the expression patterns of APOBEC family were different in different cancer types. APOBEC3B was the most aberrantly expressed in most cancer types. In LUAD, we observed a significantly positive correlation of AMES with intratumor heterogeneity (ITH), tumor neoantigen burden (TNB), and tumor mutation burden (TMB). High AMES group had high mutation counts of DNA damage repair pathways, and high enrichment of cell cycle and DNA repair pathways. We identified four prognostic genes (LYPD3, ANLN, MUC5B, and FOSL1) based on AMES, and constructed an AMES-related gene signature. The expressions of four genes were enhanced and accelerated the invasion ability and viability of lung cancer cells. Furthermore, we found that high group increased oxidative stress level. Conclusions: APOBEC family was associated with genomic instability, DNA damage-related pathways, and cell cycle in LUAD. The AMES-related gene signature had a great potential to indicate the prognosis and guide immunotherapy/chemotherapy for patients suffering from LUAD.

Clinical Analysis of C-Shaped Embedded Pancreaticojejunostomy in Pancreaticoduodenectomy.

Background: Comparing the effects of C-shaped embedded anastomosis and pancreatic duct-jejunal mucosal anastomosis on the incidence of pancreatic fistula after pancreaticoduodenectomy (PD) to find a better pancreaticojejunal anastomosis method that can reduce the occurrence of complications during the operation and benefit the patients. Methods: A retrospective subresearch method was used to select the clinical data of patients who have undergone pancreaticoduodenectomy in our hospital from December 2019 to March 2021. The indicators to be collected for this study include gender, age, body mass index, preoperative liver function (total bilirubin, alanine aminotransferase, and albumin), preoperative comorbidities (diabetes, chronic pancreatitis), and pancreatic condition (texture, pancreatic duct diameter). The patients were divided into two groups according to the method of pancreaticojejunostomy: C-shaped embedded anastomosis group (n = 38) and pancreatic duct-jejunal mucosal anastomosis group (n = 30). The duration of pancreaticojejunostomy, biliary-enteric anastomosis, gastrointestinal anastomosis, intraoperative blood loss, upper abdominal surgery history, pathological type, intraoperative blood loss, pancreaticojejunostomy time, combined pancreatic fistula, biliary fistula, hemorrhage, and abdominal infection were observed and compared. According to the different methods of pancreaticojejunostomy during operation, they were divided into group A: C-shaped embedded pancreaticojejunostomy group (38 cases), and group B: pancreatic duct-jejunal mucosal anastomosis group (30 cases). The postoperative complications were compared between the two groups, and the observed indicators were analyzed with statistical methods. Results: The average pancreaticojejunostomy time in group A was 32.13 ± 4.52 min, and the average pancreaticojejunostomy time in group B was 43.23 + 4.31 min. The difference was statistically significant (p < 0.05). Neither group A nor group B had a grade C fistula. The incidence of biochemical fistula in group A was 21.05% (8/38), and the incidence of biochemical fistula in group B was 13.3% (4/30). The difference was not statistically significant (p > 0.05). The incidence of grade B fistula in group A was 5.20% (2/38), and the incidence of grade B fistula in group B was 26.67% (8/30). The difference was statistically significant (p < 0.05). There were no perioperative deaths in the two groups. Conclusion: According to the results of data analysis, it can be seen that both the two types of pancreaticojejunostomy have good clinical effects, but that in terms of reducing the grade of pancreatic fistula, the C-shaped embedded pancreaticojejunostomy is obviously better and safer. At the same time, the C-shaped embedded pancreaticojejunostomy can shorten the time of pancreaticojejunostomy and is easier to operate, thus worthy of clinical promotion.

Murine Chronic Pancreatitis Model Induced by Partial Ligation of the Pancreatic Duct Encapsulates the Profile of Macrophage in Human Chronic Pancreatitis.

Immune responses are an integral part of the pathogenesis of pancreatitis. Studies applying the mouse model of pancreatitis induced by partial ligation of the pancreatic duct to explore the pancreatic immune microenvironment are still lacking. The aim of the present study is to explore the macrophage profile and associated regulatory mechanisms in mouse pancreatitis, as well as the correlation with human chronic pancreatitis (CP). In the present study, the mouse model of pancreatitis was induced by partial ligation of the pancreatic duct. Mice in the acute phase were sacrificed at 0, 4, 8, 16, 32, 72 h after ligation, while mice in the chronic phase were sacrificed at 7, 14, 21, 28 days after ligation. We found that the pancreatic pathological score, expression of TNF-α and IL-6 were elevated over time and peaked at 72h in the acute phase, while in the chronic phase, the degree of pancreatic fibrosis peaked at day 21 after ligation. Pancreatic M1 macrophages and pyroptotic macrophages showed a decreasing trend over time, whereas M2 macrophages gradually rose and peaked at day 21. IL-4 is involved in the development of CP and is mainly derived from pancreatic stellate cells (PSCs). The murine pancreatitis model constructed by partial ligation of the pancreatic duct, especially the CP model, can ideally simulate human CP caused by obstructive etiologies in terms of morphological alterations and immune microenvironment characteristics.

Impact of the tumor immune microenvironment on the outcome of pancreatic cancer: a retrospective study based on clinical pathological analysis.

Background: The cancerous microenvironment, characterized by the infiltration of CD4+ and CD8+ T cells, play a critical role in regulating the progression of cancer and treating efficiency of immunotherapy. However, the distribution of these cells and their associated cytokines in the tumor microenvironment of pancreatic cancer (PC) are not yet fully understood. Our study aims to analyze the contents of CD4+IL-17+ and CD8+ T cells in PC and their relationship with the clinicopathological features and survival outcomes of patients. Methods: PC tissues and adjacent tissues were retrospectively collected from 40 patients in our hospital. The expression of CD4, IL-17, and CD8 in histological samples was measured by immunohistochemistry. The correlation between CD4, IL-17, and CD8 expression and clinical characteristics was analyzed using Kaplan-Meier survival analysis. The risk factors affecting the outcome of PC were examined by the Cox proportional hazards model, then a nomogram predicting the survival of PC using these risk factors was established. Results: The content of CD4+IL-17+ T cells in PC tissues was significantly higher than that in adjacent normal tissues, while the number of CD8+ T cells was significantly lower than that in adjacent normal tissues (P<0.01). CD4+ T cells in PC tissues was significantly associated with TNM stage and lymph node metastasis (P<0.05). IL-17 and CD8 were significantly associated with histological grade, TNM stage, local infiltration, and lymph node metastasis (P<0.05). The median survival times (MSTs) of CD4 positive and negative patients were 13.2 and 21.4 months, respectively. The MSTs of IL-17 positive and negative patients were 10.4 and 24.8 months, respectively. The MSTs were 21.9 and 11.8 months for CD8 positive and negative patients, respectively (P<0.05). The Cox regression model demonstrated that TNM staging, lymph node metastasis, and CD4+IL-17+ and CD8+ T cells affected PC prognosis (P<0.05). The nomogram showed that the survival probability was reduced in patients with TNM stage III to IV, lymph node metastasis, high CD4+IL-17+ level, and low CD8+ expression. Conclusions: CD4+IL-17+ and CD8+ T cells in PC tissues are associated with TNM staging, lymph node metastasis, and MST, and can be used as new prognostic indicators for PC.

Risk factors and prognostic index model for pancreatic cancer.

BACKGROUND: Pancreatic cancer is a highly malignant tumor with poor prognosis. Chronic inflammation contributes to the progression of pancreatic cancer. However, few studies have examined the prognostic role of inflammatory markers in this cancer. Our study sought to analyze the prognostic risk factors of and construct a prognostic index (PI) model using inflammatory markers for pancreatic cancer. METHODS: Forty-eight patients diagnosed with pancreatic cancer at our hospital were selected for this retrospective analysis. Data on the general clinical characteristics, tumor-related features, blood index factors, and treatment methods were collected. The Cox proportional-hazards model was used to analyze the factors affecting the prognosis, and the Kaplan-Meier analysis was used to draw the survival curve. RESULTS: The median overall survival time was 14.5 months, and the 1-, 2-, and 3-year survival rates were 20.83% (10/48), 6.25% (3/48), and 4.17% (2/48), respectively. The univariate analysis showed that tumor grade, vascular invasion, adjacent tissue invasion, lymph node metastasis, tumor-node-metastasis (TNM) stage, the neutrophil-lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR), and the lymphocyte-monocyte ratio (LMR) were significantly correlated with the median survival of pancreatic cancer patients (P<0.05). The Cox regression equation showed that tumor grade III-IV (X1), vascular invasion (X2), TNM stage III-IV (X3), a NLR >3.8 (X4), and a PLR >182.1 (X5) were independent risk factors affecting the prognosis of patients with pancreatic cancer (all P<0.05). The prognostic model for pancreatic cancer can be expressed as: PI =3.521X1+4.157X2+1.282X3+2.441X4+6.015X5. Patients with tumor grade I-II, non-vascular invasion, TNM stage I-II, a NLR ≤3.8, and a PLR ≤182.1 exhibited a higher 1-year survival rate. The areas under the receiver operating characteristic (ROC) curves for the NLR >3.8 and the PLR >182.1 were 0.778 and 0.713, respectively. CONCLUSIONS: Tumor grade, vascular invasion, TNM staging, the NLR, and the PLR are independent risk factors affecting the prognosis of pancreatic cancer patients. The NLR and PLR have good clinical value in predicting the survival outcomes of pancreatic cancer patients.

Ferroptosis-Related IncRNAs Are Prognostic Biomarker of Overall Survival in Pancreatic Cancer Patients.

Pancreatic cancer (PC) is one of the most lethal malignancies, the mortality and morbidity of which have been increasing over the past decade. Ferroptosis, a newly identified iron-dependent cell death pattern, can be induced by iron chelators and small lipophilic antioxidants. Nonetheless, the prognostic significance of ferroptosis-related lncRNAs in PC remains to be clarified. We obtained the lncRNA expression matrix and clinicopathological information of PC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets in the current study. Firstly, we conducted Pearson correlation analysis to delve into the ferroptosis-related lncRNAs, and univariate Cox analysis was implemented to examine the prognostic values in PC patients. Twenty-three prognostic ferroptosis-related lncRNAs were confirmed and loaded into the least absolute shrinkage and selection operator Cox (LASSO-Cox) analysis, then a ferroptosis-related lncRNA prognostic marker (Fe-LPM) was established in the TCGA dataset. Risk scores of patients were calculated and segregated PC patients into low-risk and high-risk subgroups in each dataset. The prognostic capability of Fe-LPM was also confirmed in the ICGC dataset. Gene set enrichment analysis (GSEA) revealed that several ferroptosis-related pathways were enriched in low-risk subgroups. Furthermore, we adopted a multivariate Cox regression to establish a nomogram based on risk score, age, pathological T stage and primary therapy outcome. A competing endogenous RNA (ceRNA) network was also created relied on four of the twenty-three ferroptosis-related lncRNAs. In conclusion, the eight Fe-LPM can be utilized to anticipate the overall survival (OS) of PC patients, which are meaningful to guiding clinical strategies in PC.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Interplay of four types of RNA modification writers revealed distinct tumor microenvironment and biological characteristics in pancreatic cancer.

Background: Pancreatic cancer (PC) is one of the most lethal malignancies and carries a dismal mortality and morbidity. Four types of RNA modification (namely m6A, m1A, APA and A-to-I) could be catalyzed by distinct enzymatic compounds ("writers"), mediating numerous epigenetic events in carcinogenesis and immunomodulation. We aim to investigate the interplay mechanism of these writers in immunogenomic features and molecular biological characteristics in PC. Methods: We first accessed the specific expression pattern and transcriptional variation of 26 RNA modification writers in The Cancer Genome Atlas (TCGA) dataset. Unsupervised consensus clustering was performed to divide patients into two RNA modification clusters. Then, based on the differentially expressed genes (DEGs) among two clusters, RNA modification score (WM_Score) model was established to determine RNA modification-based subtypes and was validated in International Cancer Genome Consortium (ICGC) dataset. What's more, we manifested the unique status of WM_Score in transcriptional and post-transcriptional regulation, molecular biological characteristics, targeted therapies and immunogenomic patterns. Results: We documented the tight-knit correlations between transcriptional expression and variation of RNA modification writers. We classified patients into two distinct RNA modification patterns (WM_Score_high and _low), The WM_Score_high subgroup was correlated with worse prognosis, Th2/Th17 cell polarization and oncogenic pathways (e.g. EMT, TGF-ß, and mTORC1 signaling pathways), whereas the WM_Score_low subgroup associated with favorable survival rate and Th1 cell trend. WM_Score model also proved robust predictive power in interpreting transcriptional and post-transcriptional events. Additionally, the potential targeted compounds with related pathways for the WM_Score model were further identified. Conclusions: Our research unfolds a novel horizon on the interplay network of four RNA modifications in PC. This WM_Score model demonstrated powerful predictive capacity in epigenetic, immunological and biological landscape, providing a theoretical basis for future clinical judgments of PC.

DSCR9/miR-21-5p axis inhibits pancreatic cancer proliferation and resistance to gemcitabine via BTG2 signaling.

The outcome of pancreatic adenocarcinoma (PAAD) patients is poor, given resistance to gemcitabine. Long noncoding RNA (lncRNA) has been implicated in the carcinogenesis of pancreatic cancer; however, its function and mechanism in PAAD resistance to gemcitabine (GEM) are yet unknown. Herein, we demonstrate that lncRNA DSCR9 is significantly reduced in PAAD in vitro and in vivo. CCK-8, BrdU and flow cytometry assays show that overexpression of DSCR9 markedly suppresses pancreatic cancer cell proliferation and invasion, and promotes apoptosis under gemcitabine treatment. BTG2 acts as a tumor suppressor by reducing the proliferation and invasion of pancreatic cancer cells and increasing gemcitabine-induced apoptosis. Immunofluorescence (IF) staining combined with fluorescence in situ hybridization (FISH) of pancreatic cancer tissues shows that DSCR9 and BTG2 are both increased in pancreatic cancer tissues. Luciferase assay shows that miR-21-5p simultaneously binds to DSCR9 and 3'UTR of BTG2; DSCR9 relieves miR-21-5p-induced inhibition of BTG2 by competing with BTG2 for miR-21-5p binding. Overexpression of miR-21-5p enhances the invasiveness of pancreatic cancer cells by promoting cancer cell proliferation and invasion and attenuating gemcitabine-induced apoptosis. Overexpression of miR-21-5p attenuates the effect of DSCR9 overexpression on BTG2 expression and invasiveness of pancreatic cancer cells. Finally, miR-21-5p expression is increased, while BTG2 expression is decreased in pancreatic cancer tissues. miR-21-5p is negatively correlated with DSCR9 and BTG2. In conclusion, the DSCR9/miR-21-5p/BTG2 axis modulates pancreatic cancer proliferation, invasion, and gemcitabine resistance.

Tamoxifen therapy benefit predictive signature combining with prognostic signature in surgical-only ER-positive breast cancer.

Tamoxifen treatment is important assistant for estrogen-receptor-positive breast cancer (BRCA) after resection. This study aimed to identify signatures for predicting the prognosis of patients with BRCA after tamoxifen treatment. Data of gene-specific DNA methylation (DM), as well as the corresponding clinical data for the patients with BRCA, were obtained from The Cancer Genome Atlas and followed by systematic bioinformatics analyses. After mapping these DM CPG sites onto genes, we finally obtained 352 relapse-free survival (RFS) associated DM genes, with which 61,776 gene pairs were combined, including 1,614 gene pairs related to RFS. An 11 gene-pair signature was identified to cluster the 189 patients with BRCA into the surgical low-risk group (136 patients) and high-risk group (53 patients). Then, we further identified a tamoxifen-predictive signature that could classify surgical high-risk patients with significant differences on RFS. Combining surgical-only prognostic signature and tamoxifen-predictive signature, patients were clustered into surgical-only low-risk group, tamoxifen nonbenefit group, and tamoxifen benefit group. In conclusion, we identified that the gene pair PDHA2-APRT could serve as a potential prognostic biomarker for patients with BRCA after tamoxifen treatment.

Characterisation of anthocyanidin reductase from Shuchazao green tea.

BACKGROUND: Flavan-3-ols, which account for approximately 700-800 g kg(-1) of tea polyphenols, exert many health-promoting effects. Anthocyanidin reductase (ANR) is an important enzyme involved in the biosynthesis of flavan-3-ols in the tea plant. The purpose of this study was to establish a suitable method for the determination of ANR activity. RESULTS: Thin layer chromatography (TLC), high-performance liquid chromatography (HPLC) and mass spectrometry (MS) analyses showed that cyanidin and delphinidin were converted into epicatechin and epigallocatechin respectively via ANR by using reduced nicotinamide adenine dinucleotide phosphate (NADPH) as a coenzyme in the tea plant. In order to measure ANR activity via NADPH concentration changes at 340 nm, several interference factors were studied. The interferences from the high background absorbance of substrate and coenzyme and the oxidation reaction of substrate and product were excluded by devising control experiments, decreasing substrate and coenzyme concentrations or adding antioxidants. The optimal pH and concentrations of substrate and NADPH were chosen such that the ANR assays were carried out at 45 °C for 25 min in a total volume of 1.5 mL of reaction mixture containing 0.1 mol L(-1) phosphate buffer (pH 6.5), 0.0667 mmol L(-1) cyanidin, 1 mmol L(-1) NADPH, 0.53 mmol L(-1) ascorbic acid and 150 µg total protein. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) analysis showed that the trends in ANR gene expression corresponded with the enzyme activity in leaves at different development stages. CONCLUSION: The proposed method is simple, rapid, sensitive and suitable for the determination of ANR activity in the tea plant.