The expression of CCL17 and potential prognostic value on tumor immunity in thyroid carcinoma based on bioinformatics analysis.

Although CCL17 has been reported to exert a vital role in many cancers, the related studies in the thyroid carcinoma have never reported. As a chemokine, CCL17 plays a positive role by promoting the infiltration of immune cells into the tumor microenviroment (TME) to influence tumor invasion and metastasis. Therefore, this study is aimed to investigate the association of CCL17 level with potential prognostic value on tumor immunity in the thyroid carcinoma (THCA) based on the bioinformatics analysis. GEPIA database was applied to analyze CCL17 mRNA expression in THCA data from TCGA database. Through the collection of the data, totally 500 tumor and 57 normal tissue samples were taken for the study. According to survival status and survival time in 500 tumor samples and CCL17 expression from RNA-seq data, all patients were categorized as high- expression (n = 64) and low-expression (n = 436) groups using X-tile program. Next, the association of CCL17 with survival in the thyroid carcinoma patients was examined by using the Kaplan-Meier plotter database. Then, weighted gene co-expression network (WGCNA) was employed to analyze the 1424 DEGs to classify 9 modules. Besides, STRING database was used to obtain the hub genes. GO and KEGG database were employed to explore blue module genes enrichment situations. In addition, TISIDB was used to analyze the relationship of CCL17 expression with tumor-infiltrating lymphocytes proportion, immunostimulators, and major histocompatibility complexes in THCA. The correlation of CCL17 with 22 TIIC subtypes was evaluated by ESTIMATE and CIBERSORT databases. The association of CCL17 level with gene marker of immune cells in THCA was analyzed by GEPIA and TIMER databases. Finally, immunohistochemistry was applied to validate CCL17 expression in 21 tumor and para-carcinoma tissue samples. CCL17 expression in tumors was significantly up-regulated relative to non-carcinoma samples. Patients from CCL17 high-expression group had significantly decreased overall survival compared with low-expression group, which has a significantly importantly potential prognostic value. Moreover, CCL17 and clinical characteristics were analyzed, suggesting that CCL17 expression significantly increased among patients of advanced stage, with advanced T classification, advanced N classification, and higher CCR4 expression. Based on WGCNA, expression of 1424 DEGs in blue module with 258 genes was negatively related to dismal survival and clinical lymph node metastasis in THCA patients. Moreover, CCR4 and CCL17 genes were identified as hub genes within blue module. CCL17 high-expression had greater ImmuneScore, StromalScore and ESTIMATEScore, while lower TumorPurity compared to the CCL17 low-expression. Then, GO and KEGG database were used to analyze blue module genes enrichment situations. The result showed that genes in blue module were associated with cytokine-cytokine receptor interaction, chemokine, and PI3K - Akt pathways. The results of tumor-infiltrating lymphocytes proportion, immunostimulators, and major histocompatibility complexes were significantly positive in CCL17 high-expression. Our findings showed that B cells naïve, T cells CD4 memory resting, T cells CD8, T cells regulatory (Tregs), and dendritic cells resting were the main immune components of THCA tumor microenvironment (TME). CCL17 high-expression in TC was significantly positively related to expression of immune cell gene markers. The result of immunohistochemistry demonstrated that CCL17 expression in tumor tissues significantly increased compared with para-carcinoma tissues. CCL17 high-expression was significantly positively associated with age and advanced N classification, suggesting that CCL17 could accelerate tumor progression by promoting the lymph node metastasis. CCL17 high-expression in THCA tumor microenvironment (TME) accelerates local infiltration of immune cells and enhances anticancer immunity, resulting in worse survival of patients and exerting potential prognostic value on tumor immunity in THCA.

Gu X ，Chen B ，Zhang S ，Zhai X ，Hu Y ，Ye H ... -  《Scientific Reports》

[Pan-cancer analysis of ubiquitin-specific protease 7 and its expression changes in the carcinogenesis of scar ulcer].

Zhang SY ，Ruan JJ ，Jin DM ，Chen N ，Xie WG ，Ruan QF ... -  《-》

CTSO and HLA-DQA1 as biomarkers in sepsis-associated ARDS: insights from RNA sequencing and immune infiltration analysis.

The onset of sepsis frequently coincides with acute respiratory distress syndrome (ARDS), which constitutes a significant contributor to severe acid-base disturbances in septic patients. In the pathogenesis of sepsis, it conducts a crucial role. lysosomal metabolic disorders and immune imbalance conduct a pivotal role. Despite extensive research into the alterations in immune status during sepsis, few studies have been reported to thoroughly examine the association between lysosomes and sepsis. As a result, this study is predominantly Intended to delve into the link between lysosome-related genes and alterations in the lysosome in the immune microenvironment from the standpoint of bioinformatics in sepsis. The Registration Number was ChiCTR1900021261. Registration Date is 2019/02/04. Method Sepsis data source: Sepsis data was collected from previous clinical data and sequencing results (Originated from BGI Shenzhen Co., Ltd.) and the GO database was utilized for data collection of lysosome-related genes. Differential expression genes (DEGs) were screened on clinical sequencing data by employing IDEP 0.93 software subsequent to quality control. Afterwards, enrichment analysis was conducted by adopting Gene Set Enrichment Analysis (GSEA) and Weighted Gene Co expression Network Analysis (WGCNA), followed by cross referencing of lysosomal genes to identify DEGs associated with lysosomes. GO and KEGG pathway analysis wereperformed subsequently. The genes obtained from PLSGs and WGCNA by Creating a PPI network entails the following steps: the points were intersected at first. Afterwards, CytoHubba and MCODE analysis were performed by utilizing cytoscape software. Next, the intersection was taken to confirm Hub gene sequences, and subsequently the central DEGs tightly associated with existing CTD scores. Notwithstanding the fact that the causes of sepsis are multifaceted, ARDS can often trigger the development of sepsis in numerous cases. Simultaneously, with an aim to predict transcription factor levels in the central nervous system, Cytoscape software was adopted DEGs and to find relevant target miRNAs in the miRWalk database, and a correlated regulatory network was established accordingly. The SEPSIS immune infiltration model was constructed by employing ImmuCellAI software. Afterwards, the association between DEGs and immune microenvironment abundance was constructed by adopting Spearman's method. Last but not least, it is worth noting that single-cell sequencing has been validated as a method to analyze hub gene expression in immune cells of sepsis patients, enabling the selection of key genes that are closely associated with predictive outcomes. Result When acute respiratory distress syndrome (ARDS) is present, the differentially expressed genes (DEGs) are implicated in lysosomal metabolism and the regulation of the immune microenvironment. Six hub DEGs were bound up with sepsis or was attributable to the examinations. On top of that, it was determined that the patients had acute respiratory distress syndrome. The associated immune analysis illustrated a remarkable augment in T cell infiltration in the immune microenvironment of sepsis, while the infiltration relative to DC was reduced at certain level. Positive correlations were found between the two by employing Spearman analysis between hub DEGs and the regulatory role of immune cells. Moreover, it was universally acknowledged that anti-inflammatory immune cells were responsible for the negative correlation. On the basis of single-cell sequencing, it has been determined that CTSO and HLA-DQA1 were expressed in immune cells in sepsis. Aside from that, the survival-death curve direction suggested that they could be utilized as core genes for predicting sepsis-related prognosis analysis. Conclusion An analysis of this study demonstrates the interaction between sepsis lysosome-related metabolism and changes by understanding the pathogenesis of immune cells in the microenvironment. On this basis, we can develop new clinical diagnostics and therapeutic approaches of sepsis and identifying drug targets. Nonetheless, ARDS and sepsis can differ simply by the difference in site of infection; as the etiology of numerous ARDS cases is quite complex, progression to sepsis can occur if infection exacerbates or other complications arise, meeting the diagnostic criteria of sepsis 3.0.

Shen YZ ，Yao YD ，Li HL ，Li Y ，Hu YC ... -  《BMC INFECTIOUS DISEASES》

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Wang SY ，Wang YX ，Guan LS ，Shen A ，Huang RJ ，Yuan SQ ，Xiao YL ，Wang LS ，Lei D ，Zhao Y ，Lin C ，Wang CP ，Yuan ZP ... -  《BMC Medical Genomics》

[Exploration of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics and the depiction of their immune profiles characterization].

Li M ，Mei Y ，Pan A 《-》