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Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-β/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular mechanisms of action of hedgehog activation in TGF-β1-triggered EMT in PCCs (PANC-1 and BxPc-3). In addition, overexpression and shRNA techniques were used to evaluate the role of Smad4 in TGF-β1-treated PCCs. Our data showed that TGF-β1 promoted PCC invasion and infiltration via Smad2/3-dependent EMT. Hedgehog-Gli signaling axis in PCCs was activated upon TGF-β1 stimulation. Inhibition of hedgehog with cyclopamine effectively antagonized TGF-β1-induced EMT, thereby suggesting that the hedgehog signaling may act as a downstream cascade signaling of TGF-β1. As a key protein that assists the nuclear translocation of Smad2/3, Smad4 was highly expressed in PANC-1 cells, but not in BxPc-3 cells. Conversely, Gli1 expression was low in PANC-1 cells, but high in BxPc-3 cells. Furthermore, knockdown of Smad4 in PANC-1 cells by shRNA inhibited TGF-β1-mediated EMT and collagen deposition. Overexpression of Smad4 did not affect TGF-β1-mediated EMT due to the lack of significant increase in nuclear expression of Smad4. Importantly, Gli1 activity was upregulated by Smad4 knockdown in PANC-1 cells and downregulated by Smad4 overexpression in BxPc-3 cells, indicating that Gli1 may be a negative target protein downstream of Smad4. Thus, Smad4 regulates TGF-β1-mediated hedgehog activation to promote EMT in PCCs by suppressing Gli1 activity.

Neutrophil extracellular traps (NETs) and other factors play a significant role in impacting the prognosis of patients with Hepatocellular carcinoma (HCC). Nevertheless, further research is warranted to fully elucidate the prognostic implications of NETs in patients with HCC. We employed a hierarchical clustering technique to examine the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data and identified subtypes associated with NETs. Subsequently, we utilized LASSO regression analysis to identify a distinct gene expression pattern within these subtypes. The strength of this signature was further validated through analysis of TCGA-LIHC and International Cancer Genome Consortium-Liver Cancer (ICGC-LIRI-JP) data. Our findings resulted in the construction of a six-gene signature related to NETs, which can predict survival outcomes in HCC patients. To enhance the predictive accuracy of our tool, we developed a nomogram that integrates the NETs signature with clinicopathological characteristics. We validated the significance of NETs in HCC patients using qRT-PCR and immunohistochemistry assays, along with in vitro experiments targeting high-risk genes. Furthermore, our exploration of the immune microenvironment uncovered augmented immune-specific metrics within the low-risk cohort, implying potential disparities in immune-related attributes between the high-risk and low-risk contingents. In summary, the NETs signature we discovered serves as a valuable biomarker and provides guidance for personalized therapy in HCC patients.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive tumor and is associated with a poor prognosis. Treatment strategies for PDAC are largely ineffective primarily because of delay in its diagnosis and limited efficacy of systematic treatment. S100A2 is associated with the proliferation, migration, and differentiation of several tumors; however, its effects on PDAC and the associated molecular mechanisms remain to be explored. We studied the mechanisms underlying the effect of S100A2 on epithelial–mesenchymal transition (EMT) and metastasis in PDAC cells. We found that the level of S100A2 remarkably increased and was associated with poor PDAC prognosis. The overexpression of S100A2 in PANC-1 cells also induced EMT, in addition to increasing the invasion and migration of PDAC cells, whereas the knockdown of S100A2 markedly inhibited cell metastasis. Furthermore, S100A2 was found to enhance metastatic abilities in vivo. The overexpression of S100A2 increased SMAD4 expression, whereas the knockdown of S100A2 reduced SMAD4 expression. SMAD4 overexpression could effectively rescue the effects of S100A2 knockdown on EMT. S100A2 mechanistically activated the transforming growth factor (TGF)-β/Smad2/3 signaling pathway, upregulated SMAD4 expression, induced EMT, and increased PANC-1 cell metastasis. In conclusion, the S100A2/SMAD4 axis modulates EMT to accelerate PDAC development. Our results supplement and enrich the understanding of the pathogenesis underlying PDAC and provide a new theoretical basis and strategy targeting S100A2 for the diagnosis and treatment of PDAC.

BackgroundRenal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers.MethodsWe used R package “limma” to screen out differently expressed genes (DEGs) based on Epithelial-mesenchymal transformation (EMT), and carried out the protein interaction network and GO, KEGG enrichment analysis of DEGs. Secondly, the least absolute shrinkage and selection operator (LASSO), random forest tree (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify candidate diagnostic genes. ROC curves were plotted to evaluate the clinical diagnostic value of these genes. In addition, mRNA expression levels of candidate diagnostic genes were analyzed in control samples and renal fibrosis samples. CIBERSORT algorithm was used to evaluate immune cells level. Additionally, gene set enrichment analysis (GSEA) and drug sensitivity were conducted.ResultsAfter obtaining a total of 24 DEGs, we discovered that they were mostly involved in several immunological and inflammatory pathways, including NF-KappaB signaling, AGE-RAGE signaling, and TNF signaling. Five genes (COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA) were subsequently identified as biomarkers for renal fibrosis through machine learning, and their expression levels were confirmed by validation cohort data sets and in vitro RT-qPCR experiment. The AUC values of these five genes demonstrated significant clinical diagnostic value in both the training and validation sets. After that, CIBERSORT analysis showed that these biomarkers were strongly associated with immune cell content in renal fibrosis patients. GSEA also identifies the potential roles of these diagnostic genes. Additionally, diagnostic candidate genes were found to be closely related to drug sensitivity. Finally, a nomogram for diagnosing renal fibrosis was developed.ConclusionCOL4A2, CXCL1, TIMP1, VCAM1, and VEGFA are promising diagnostic biomarkers of tissue and serum for renal fibrosis.

There is little evidence determining whether elderly patients (from 70 to 90 years old) with triple-negative breast cancer could benefit from adjuvant chemotherapy (AC). This study explores the effect of AC in these population following surgery. A total of 4610 patients were identified in the Surveillance, Epidemiology, and End Results database (2010-2018). Multiple imputation by chained equations was performed to impute missing data. Inverse probability of treatment weighting (IPTW) was applied to reduce the selection bias. IPTW-adjusted Kaplan-Meiers survival analysis and Cox proportional hazards models were performed to compare breast cancer specific survival (BCSS) and overall survival (OS) in the two treatment groups. The patients were classified into the chemotherapy (n=1989) and the observation (n=2621) groups. The percentage of patients receiving AC versus observation increased significantly from 2010 to 2018 (estimated annual percentage change, 1.49%; 95%CI, 0.75-2.16%, p=0.002). The 5-year IPTW-adjusted rates of BCSS and OS in AC group were better than that in observation group (BCSS: 82.32% vs. 78.42%, p=0.010; OS: 75.54% vs. 64.65%, p0.050). In conclusion, we presented a BCSS and OS benefit from AC in elderly patients with triple-negative breast cancer (TNBC). AC remained a reasonable treatment approach in these specific patients. For the patients with T1ab, de-escalated treatment would be administrated with caution.

Purpose: Chemotherapy is the clinically recommended treatment for patients with operable metaplastic breast carcinoma (MBC); however, its impact remains controversial. This study investigated the possible role of chemotherapy in the treatment of MBC.Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify the operable MBC patients. The competing risk analysis along with the propensity score matching (PSM) method was performed to evaluate the effect of chemotherapy. Moreover, a competing risk nomogram was built to identify prognosis in patients with MBC.Results: Of the 1137 patients with MBC, 775 received chemotherapy and 362 did not receive chemotherapy. The 5-year cumulative incidence of breast cancer-specific death (BCSD) showed similar outcomes in both the Chemo and No-Chemo groups (21.1 vs. 24.3%, p = 0.57). Chemotherapy showed no apparent association with BCSD (HR, 1.07; 95% CI, 0.72–1.60; p = 0.72), even after subgroup analysis or PSM. Race, tumor size, lymph node status, and radiation were identified as the significant factors for MBC after a penalized variable selection process. In addition, a competing risk nomogram showed relatively good accuracy of prediction with a C-index of 0.766 (95% CI, 0.700–0.824).Conclusion: Our findings demonstrated that chemotherapy did not improve BCSD for operable MBC patients. Thus, it may indicate the need to reduce exposure to the current chemotherapy strategies for patients with resectable MBC. Additionally, some novel treatment strategies are required urgently to identify and target the potential biomarkers.

Background The microRNAs (miRNAs) have been validated as prognostic markers in many cancers. Here, we aimed at developing a miRNA-based signature for predicting the prognosis of esophagus adenocarcinoma (EAC). Methods The RNA-sequencing data set of EAC was downloaded from The Cancer Genome Atlas (TCGA). Eighty-four patients with EAC were classified into a training set and a test set randomly. Using univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO), we identified prognostic factors and constructed a prognostic miRNA signature. The accuracy of the signature was evaluated by the receiver operating characteristic (ROC) curve. Result In general, in the training set, six miRNAs (hsa-mir-425, hsa-let-7b, hsa-mir-23a, hsa-mir-3074, hsa-mir-424 and hsa-mir-505) displayed good prognostic power as markers of overall survival for EAC patients. Relative to patients in the low-risk group, those assigned to the high-risk group according to their risk scores of the designed miRNA model displayed reduced overall survival. This 6-miRNA model was validated in test and entire set. The area under curve (AUC) for ROC at 3 years was 0.959, 0.840, and 0.868 in training, test, and entire set, respectively. Molecular functional analysis and pathway enrichment analysis indicated that the target messenger RNAs associated with 6-miRNA signature were closely related to several pathways involved in carcinogenesis, especially cell cycle. Conclusion In summary, a novel 6-miRNA expression-based prognostic signature derived from the EAC data of TCGA was constructed and validated for predicting the prognosis of EAC.

Background: Renal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers. Methods: We used R package "limma" to screen out differently expressed genes (DEGs) based on Epithelial-mesenchymal transformation (EMT), and carried out the protein interaction network and GO, KEGG enrichment analysis of DEGs. Secondly, the least absolute shrinkage and selection operator (LASSO), random forest tree (RF), and support vector machine-recursive feature elimination (SVMRFE) algorithms were used to identify candidate diagnostic genes. ROC curves were plotted to evaluate the clinical diagnostic value of these genes. In addition, mRNA expression levels of candidate diagnostic genes were analyzed in control samples and renal fibrosis samples. CIBERSORT algorithm was used to evaluate immune cells level. Additionally, gene set enrichment analysis (GSEA) and drug sensitivity were conducted. Results: After obtaining a total of 24 DEGs, we discovered that they were mostly involved in several immunological and inflammatory pathways, including NFKappaB signaling, AGE-RAGE signaling, and TNF signaling. Five genes (COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA) were subsequently identified as biomarkers for renal fibrosis through machine learning, and their expression levels were confirmed by validation cohort data sets and in vitro RT-qPCR experiment. The AUC values of these five genes demonstrated significant clinical diagnostic value in both the training and validation sets. After that, CIBERSORT analysis showed that these biomarkers were strongly associated with immune cell content in renal fibrosis patients. GSEA also identifies the potential roles of these diagnostic genes. Additionally, diagnostic candidate genes were found to be closely related to drug sensitivity. Finally, a nomogram for diagnosing renal fibrosis was developed. Conclusion: COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA are promising diagnostic biomarkers of tissue and serum for renal fibrosis. [ABSTRACT FROM AUTHOR]

The life-cycle assessment method, which originates from general products and services, has gradually come to be applied to investigations of the life-cycle carbon emissions (LCCE) of buildings. A literature review was conducted to clarify LCCE implications, calculations, and reductions in the context of buildings. A total of 826 global building carbon emission calculation cases were obtained from 161 studies based on the framework of the building life-cycle stage division stipulated by ISO 21930 and the basic principles of the emission factor (EF) approach. The carbon emission calculation methods and results are discussed herein, based on the modules of production, construction, use, end-of-life, and supplementary benefits. According to the hotspot distribution of a building’s carbon emissions, carbon reduction strategies are classified into six groups for technical content and benefits analysis, including reducing the activity data pertaining to building materials and energy, reducing the carbon EFs of the building materials and energy, and exploiting the advantages of supplementary benefits. The research gaps and challenges in current building LCCE studies are summarized in terms of research goals and ideas, calculation methods, basic parameters, and carbon reduction strategies; development suggestions are also proposed.

Abstract In this paper we investigated SF6 decomposition behaviour of typical insulation defects in gas‐solid insulated converter transformer valve‐side bushing under partial discharge (PD). Additionally, we compared the results with that of gas insulated switchgear (GIS). The results indicated that SF6 decomposition behaviour varies with the insulation defects type. For metal protrusion defects, the proportion ranking of CO2 and SO2 can be related to whether the needle tip is wrapped with epoxy resin‐impregnated paper (ERIP). The proportion of carbon‐containing products is much higher under contamination defect than under other defects. Moreover, the proportion of carbon‐containing components is higher under flashover condition than under PD condition and is accompanied by the appearance of H2S and CS2. However, the yield of SO2F2 is always low. Compared to negative DC voltages, positive DC voltages have a higher proportion of carbon‐containing components, mainly in the form of more CO2 and less SOF2. Compared to the PD decomposition behaviour of typical insulation defects in GIS, when PD involves ERIP, the proportion of carbon‐containing products, especially CO2, is significant higher, while the proportion of sulphur containing products, especially SO2F2, is lower.

A polarized light microscope (PLM) was utilized to examine the optical textures of mesophase pitch (MP) and MP-derived fibers, which aimed to reveal the arrangement and orientation characteristics of pitch molecules and to clarify the evolution and transformation mechanism of carbonaceous microcrystalline from pitch fibers to graphitized fibers. The results found that there were distinct optical textures in MP, where one side exhibited a transition from a flattening plane to a mountain-like undulating plane. This transition corresponded to the arrangement of pitch molecules, resembling stacked lamellar structures reminiscent of curved paper. Meanwhile, the optical textures of fibers revealed that the blue substance was wrapped around the red grain-like domains in the longitudinal section and confirmed that the red part belonged to the pyridine insoluble fraction of MP and the blue part belonged to its pyridine-soluble fraction. After graphitization, the red part was transformed into graphite sheets and the blue part was transformed into an amorphous carbon layer which was wrapped around the graphite sheets, forming a carbonaceous microcrystalline package-like bag. Therefore, this study provided a comprehensive interpretation of the structural evolution mechanism of MP and MP-derived fibers based on their macro-optical textures and micro-nanostructures.

The high mobility group A1 (HMGA1) protein is associated with poor prognosis in patients with a wide range of cancers. However, the affect of HMGA1 on the risk of mortality from breast cancer (BC) has not been fully characterized. In the present retrospective multiple center study, the HMGA1 expression level was determined by performing immunohistochemistry on surgical tissue samples of 273 BC specimens from the Second Affiliated Hospital of Zhejiang University (Zhejiang, China) and 310 BCs from the National Engineering Center for Biochip (Shanghai, China). Kaplan-Meier analysis and Cox proportional hazard model were employed to analyze the survivability. HMGA1 expression was significantly associated with tumor histological degree and body mass index (BMI). However, HMGA1 expression showed no prognostic value in patients with BC. Combined evaluation of HMGA1 expression and high BMI (≥24 kg/m2) predicted worse overall survival of BC. Therefore, HMGA1 and BMI were considered to serve synergistic roles in the development and progression of BC, and combined evaluation of HMGA1 expression and high BMI may be an effective marker in predicting poor prognosis of BC patients. [ABSTRACT FROM AUTHOR]