Prognostic Values and Clinical Significance of S100 Family Member's Individualized mRNA Expression in Pancreatic Adenocarcinoma

S100s and HMGB1 Crosstalk in Pancreatic Cancer Tumors

Pancreatic cancer remains a disease that is very difficult to treat. S100 proteins are small calcium binding proteins with diverse intra- and extracellular functions that modulate different aspects of tumorigenesis, including tumor growth and metastasis. High mobility group box 1 (HMGB1) protein is a multifaceted protein that also actively influences the development and progression of tumors. In this study, we investigate the possible correlations, at the transcript level, between S100s and HMGB1 in pancreatic cancer. For this purpose, we calculated Pearson's correlations between the transcript levels of 13 cancer-related S100 genes and HMGB1 in a cDNA array containing 19 pancreatic cancer tumor samples, and in 8 human pancreatic cancer cell lines. Statistically significant positive correlations were found in 5.5% (5 out of 91) and 37.4% (34 of 91) of the possible S100/S100 or S100/HMGB1 pairs in cells and tumors, respectively. Our data suggest that many S100 proteins crosstalk in pancreatic tumors either with other members of the S100 family, or with HMGB1. These newly observed interdependencies may be used to further the characterization of pancreatic tumors based on S100 and HMGB1 transcription profiles.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME). Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME. Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties. Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

Diagnostic value and mechanism of plasma S100A1 protein in acute ischemic stroke: a prospective and observational study

Background

Plasma S100A1 protein is a novel inflammatory biomarker associated with acute myocardial infarction and neurodegenerative disease's pathophysiological mechanisms. This study aimed to determine the levels of this protein in patients with acute ischemic stroke early in the disease progression and to investigate its role in the pathogenesis of acute ischemic stroke.

Methods

A total of 192 participants from hospital stroke centers were collected for the study. Clinically pertinent data were recorded. The volume of the cerebral infarction was calculated according to the Pullicino formula. Multivariate logistic regression analysis was used to select independent influences. ROC curve was used to analyze the diagnostic value of AIS and TIA. The correlation between S100A1, NF-κB p65, and IL-6 levels and cerebral infarction volume was detected by Pearson correlation analysis.

Results

There were statistically significant differences in S100A1, NF-κB p65, and IL-6 among the AIS,TIA, and PE groups (S100A1, [230.96 ± 39.37] vs [185.85 ± 43.24] vs [181.47 ± 27.39], P < 0.001; NF-κB p65, [3.99 ± 0.65] vs [3.58 ± 0.74] vs [3.51 ± 0.99], P = 0.001; IL-6, [13.32 ± 1.57] vs [11.61 ± 1.67] vs [11.42 ± 2.34], P < 0.001). Multivariate logistic regression analysis showed that S100A1 might be an independent predictive factor for the diagnosis of disease (P < 0.001). The AUC of S100A1 for diagnosis of AIS was 0.818 (P < 0.001, 95% CI [0.749-0.887], cut off 181.03, Jmax 0.578, Se 95.0%, Sp 62.7%). The AUC of S100A1 for diagnosis of TIA was 0.720 (P = 0.001, 95% CI [0.592-0.848], cut off 150.14, Jmax 0.442, Se 50.0%, Sp 94.2%). There were statistically significant differences in S100A1, NF-κB p65, and IL-6 among the SCI,MCI, and LCI groups (S100A1, [223.98 ± 40.21] vs [225.42 ± 30.92] vs [254.25 ± 37.07], P = 0.001; NF-κB p65, [3.88 ± 0.66] vs [3.85 ± 0.64] vs [4.41 ± 0.45], P < 0.001; IL-6, [13.27 ± 1.65] vs [12.77 ± 1.31] vs [14.00 ± 1.40], P = 0.007). Plasma S100A1, NF-κB p65, and IL-6 were significantly different from cerebral infarction volume (S100A1, r = 0.259, P = 0.002; NF-κB p65, r = 0.316, P < 0.001; IL-6, r = 0.177, P = 0.036). There was a positive correlation between plasma S100A1 and IL-6 with statistical significance (R = 0.353, P < 0.001). There was no significant positive correlation between plasma S100A1 and NF-κB p65 (R < 0.3), but there was statistical significance (R = 0.290, P < 0.001). There was a positive correlation between IL-6 and NF-κB p65 with statistical significance (R = 0.313, P < 0.001).

Conclusion

S100A1 might have a better diagnostic efficacy for AIS and TIA. S100A1 was associated with infarct volume in AIS, and its level reflected the severity of acute cerebral infarction to a certain extent. There was a correlation between S100A1 and IL-6 and NF-κB p65, and it was reasonable to speculate that this protein might mediate the inflammatory response through the NF-κB pathway during the pathophysiology of AIS.

Influence of S100A2 in Human Diseases

S100 proteins are a family of low-molecular-weight proteins characterized by two calcium-binding sites with a helix-loop-helix (“EF-hand-type”) domain. The S100 family of proteins is distributed across various organs and can interact with diverse molecules. Among the proteins of the S100 family, S100 calcium-binding protein A2 (S100A2) has been identified in mammary epithelial cells, glands, lungs, kidneys, and prostate gland, exhibiting various physiological and pathological actions in human disorders, such as inflammatory diseases and malignant tumors. In this review, we introduce basic knowledge regarding S100A2 regulatory mechanisms. Although S100A2 is a tumor suppressor, we describe the various influences of S100A2 on cancer and inflammatory diseases.