In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

B-1 derived anti-Thy-1 B cells in old aged mice develop lymphoma/leukemia with high expression of CD11b and Hamp2 that different from TCL1 transgenic mice

Human old aged unmutated chronic lymphocytic leukemia U-CLL are the TCL1+ZAP70+CD5+ B cells. Since CD5 makes the BCR signaling tolerance, ZAP70 increased in U-CLL not only TCL1+ alone. In mice, TCL1 (TCL1A) is the negative from neonate to old aged, as TC-. VH8-12/Vk21-5 is the anti-thymocyte/Thy-1 autoreactive ATA B cell. When ATA μκTg generation in mice, ATA B cells are the neonate generated CD5+ B cells in B-1, and in the middle age, CD5+ can be down or continuously CD5+, then, old aged CLL/lymphoma generation with increased CD11b in TC-ZAP70-CD5- or TC-ZAP70+CD5+. In this old aged TC-ATA B microarray analysis showed most similar to human CLL and U-CLL, and TC-ZAP70+CD5+ showed certain higher present as U-CLL. Original neonate ATA B cells showed with several genes down or further increase in old aged tumor, and old aged T-bet+CD11c+, CTNNB1hi, HMGBhi, CXCR4hi, DPP4hi and decreased miR181b. These old aged increased genes and down miR181b are similar to human CLL. Also, in old age ATA B cell tumor, high CD38++CD44++, increased Ki67+ AID+, and decreased CD180- miR15Olow are similar to U-CLL. In this old aged ATA B, increased TLR7,9 and Wnt10b. TC+Tg generated with ATAμκTg mice occurred middle age tumor as TC+ZAP70-CD5+ or TC+ZAP70+CD5+, with high NF-kB1, TLR4,6 and Wnt5b,6 without increased CD11b. Since neonatal state to age with TC+Tg continuously, middle age CLL/lymphoma generation is not similar to old aged generated, however, some increased in TC+ZAP70+ are similar to the old age TC- ATA B tumor. Then, TC- ATA B old age tumor showed some difference to human CLL. ATA B cells showed CD11b+CD22++, CD24 down, and hepcidin Hamp2++ with iron down. This mouse V8-12 similar to human V2-5, and V2-5 showed several cancers with macrophages/neutrophils generated hepcidin+ ironlow or some showed hepcidin- iron+ with tumor, and mouse V8-12 with different Vk19-17 generate MZ B cells strongly increased macrophage++ in old aged and generated intestine/colon tumor. Conclusion, neonate generated TC-ATA B1 cells in old aged tumor generation are CD11b+ in the leukemia CLL together with lymphoma cancer with hepcidin-related Hamp2++ in B-1 cell generation to control iron.

Role of exosome-mediated molecules SNORD91A and SLC40A1 in M2 macrophage polarization and prognosis of ESCC

BACKGROUND

Exosome-mediated interaction serves as a significant regulatory factor for M2 macrophage polarization in cancer.

METHODS

All accessible data were acquired from The Cancer Genome Atlas (TCGA) database and analyzed using R software. Molecules implicated in exocrine secretion were amassed from the ExoCarta database. Our research initially quantified the immune microenvironment in Esophageal Squamous Cell Carcinoma (ESCC) patients based on the expression profile sourced from the TCGA database. Additionally, we delved into the biological role of M2 macrophages in ESCC via Gene Set Enrichment Analysis (GSEA).

RESULTS

We observed that patients with high M2 macrophage infiltration typically have a poorer prognosis. Subsequently, a total of 1457 molecules were identified, with 103 of these molecules believed to function through exocrine mechanisms, as supported by data from the ExoCarta database. SNORD91A and SLC40A1 were ultimately pinpointed due to their correlation with patient prognosis. Moreover, we investigated their potential roles in ESCC, including biological enrichment, immune infiltration, and genomic instability analysis.

CONCLUSIONS

Our study identified exosome-associated molecules, namely SNORD91A and SLC40A1, which notably impact ESCC prognosis and local M2 macrophage recruitment, thereby presenting potential therapeutic targets for ESCC.

HAMP as a Potential Diagnostic, PD-(L)1 Immunotherapy Sensitivity and Prognostic Biomarker in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains a global medical problem. Programmed cell death protein 1 (PD-1) is a powerful weapon against many cancers, but it is not sensitive to some patients with HCC. We obtained datasets from the Gene Expression Omnibus (GEO) database on HCC patients and PD-1 immunotherapy to select seven intersecting DEGs. Through Lasso regression, two intersecting genes were acquired as predictors of HCC and PD-1 treatment prognosis, including HAMP and FOS. Logistic regression was performed to build a prediction model. HAMP had a better ability to diagnose HCC and predict PD1 treatment sensitivity. Further, we adapted the support vector machine (SVM) technique using HAMP to predict triple-classified outcomes after PD1 treatment in HCC patients, which had an excellent classification ability. We also performed external validation using TCGA data, which showed that HAMP was elevated in the early stage of HCC. HAMP was positively correlated with the infiltration of 18 major immune cells and the expression of 2 important immune checkpoints, PDCD1 and CTLA4. We discovered a biomarker that can be used for the early diagnosis, prognosis and PD1 immunotherapy efficacy prediction of HCC for the first time and developed a diagnostic model, prognostic model and prediction model of PD1 treatment sensitivity and treatment outcome for HCC patients accordingly.

Leonurine suppresses prostate cancer growth in vitro and in vivo by regulating miR-18a-5p/SLC40A1 axis

Prostate cancer is a leading cause of cancer-associated death in males. Leonurine (Leo) is a pleiotropic anti-tumor agent isolated from traditional Chinese herb that was used in gynecologic treatments. However, its pharmacological effect against prostate cancer progression remains unclear. Here, we showed that Leo dose dependently inhibited prostate cancer cell proliferation, promoted cell apoptosis, and induced cell cycle arrest. Moreover, we noticed that miR-18a-5p was downregulated and the solute carrier family 40 member 1 (SLC40A1) is upregulated by Leo treatment. SLC40A1 knockdown by siRNA abrogated the inhibitory effect of Leo on prostate cancer progression. Notably, Leo also significantly inhibited prostate cancer progression in a subcutaneous xenograft tumor mouse model in vivo. This study further unveiled the mechanism by which Leo inhibited prostate cancer progression, which provides a promising potential for its future clinical application.

Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data

Cell transplantation is an effective method for compensating for the loss of liver function and improve patient survival. However, given that hepatocytes cultivated in vitro have diverse developmental processes and physiological features, obtaining hepatocytes that can properly function in vivo is difficult. In the present study, we present an advanced computational analysis on single-cell transcriptional profiling to resolve the heterogeneity of the hepatocyte differentiation process in vitro and to mine biomarkers at different periods of differentiation. We obtained a batch of compressed and effective classification features with the Boruta method and ranked them using the Max-Relevance and Min-Redundancy method. Some key genes were identified during the in vitro culture of hepatocytes, including CD147, which not only regulates terminally differentiated cells in the liver but also affects cell differentiation. PPIA, which encodes a CD147 ligand, also appeared in the identified gene list, and the combination of the two proteins mediated multiple biological pathways. Other genes, such as TMSB10, TMEM176B, and CD63, which are involved in the maturation and differentiation of hepatocytes and assist different hepatic cell types in performing their roles were also identified. Then, several classifiers were trained and evaluated to obtain optimal classifiers and optimal feature subsets, using three classification algorithms (random forest, k-nearest neighbor, and decision tree) and the incremental feature selection method. The best random forest classifier with a 0.940 Matthews correlation coefficient was constructed to distinguish different hepatic cell types. Finally, classification rules were created for quantitatively describing hepatic cell types. In summary, This study provided potential targets for cell transplantation associated liver disease treatment strategies by elucidating the process and mechanism of hepatocyte development at both qualitative and quantitative levels.