Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.

Objective: Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy. Methods: We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC. Results: We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines. Conclusion: The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

New insights into endophytic fungi diversity and their potential correlation with polyphyllin levels of Paris polyphylla var. yunnanensis.

Endophytes confer fitness advantages to host plants. However, the ecological communities of endophytic fungi in the different tissues (rhizomes, stems, and leaves) of Paris polyphylla and the relationship of their endophytic fungi with polyphyllin levels remain unclear. In this study, the community diversity and differences of endophytic fungi in the rhizomes, stems, and leaves of P. polyphylla var. yunnanensis were investigated, and a comprehensively diverse community of endophytic fungi was represented, including 50 genera, 44 families, 30 orders, 12 classes, and 5 phyla. Distributions of endophytic fungi differed greatly across the three tissues, with six genera common to all tissues, and 11, 5, and 4 genera specific to the rhizomes, stems, and leaves, respectively. Seven genera showed a significantly positive correlation to polyphyllin contents, indicating their potential roles in polyphyllin accumulation. This study provides valuable information for further research of the ecological and biological functions of endophytic fungi of P. polyphylla.

Overexpression of SRD5A3 in Hepatocellular Carcinoma and Its Molecular Mechanism: A Study of Bioinformatics Exploration Analysis with Experimental Verification.

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and more prevalent among males than females. However, the biological role of enzyme 5α-reductase (SRD5A3), which plays a critical role in the androgen receptor signaling pathway during HCC development, remains poorly understood. Methods: ONCOMINE, GEPIA, UALCAN, and Kaplan-Meier Plotter were used to analyze the expression and prognostic value of SRD5A3 in HCC. STRING and Metascape were applied to analyze potential target and molecular pathways underlying SRD5A3 in HCC. A real-time quantitative reverse transcription-polymerase chain reaction was used to validate the downstream target expression of SRD5A3. Results: The expression of SRD5A3 was significantly overexpressed in HCC tissues compared with normal tissues, while the expression of SRD5A1 and SRD5A2 were downregulated in multiple public datasets. It may be that the low methylation of the SRD5A3 promoter leads to its overexpression. The level of SRD5A3 tended to be higher expressed in clinical samples with advanced stage and positive node metastasis. Furthermore, the patients with higher SRD5A3 were remarkably associated with poorer overall survival and disease-free survival in the TCGA data. In addition, the increased mRNA expression of SRD5A3 could predict poorer overall survival in Kaplan-Meier Plotter database including different patient cohorts. Moreover, HCC patients with higher level of SRD5A3 had significantly shorter recurrence-free survival, progression-free survival, and disease-specific survival. Furthermore, enrichment analysis demonstrated that multiple processes, such as steroid hormone biosynthesis, lipid biosynthetic process, and androgen metabolic process, were affected by SRD5A1-3 alterations. In vitro experiments showed that the expression of SRD5A3 was increased in HCC tissues than that in adjacent tissues. SRD5A3 silencing promoted the expression of DOLK in two HCC cell lines. Conclusions: This study identified SRD5A3/DOLK as a novel axis to regulate HCC development.

Circ-ITCH sponges miR-214 to promote the osteogenic differentiation in osteoporosis via upregulating YAP1.

Osteoporosis is the most prevailing primary bone disease and a growing health care burden. The aim of this study was to clarify the functional roles and mechanisms of the circ-ITCH regulating osteogenic differentiation of osteoporosis. Circ-ITCH and yes-associated protein 1 (YAP1) levels were downregulated, but the miR-214 level was upregulated in osteoporotic mice and patients. Knockdown of circ-ITCH inhibited the alkaline phosphatase (ALP) activity, mineralized nodule formation, and expression of runt-related transcription factor 2 (RUNX2), osteopontin (OPN), and osteocalcin (OCN) during osteogenic induction. Furthermore, miR-214 was a target of circ-ITCH, knockdown of miR-214 could impede the regulatory effects of sh-circ-ITCH on osteogenic differentiation. Moreover, miR-214 suppressed hBMSCs osteogenic differentiation by downregulating YAP1. Finally, in vivo experiments indicated that overexpression of circ-ITCH could improve osteogenesis in ovariectomized mice. In conclusion, circ-ITCH upregulated YAP1 expression to promote osteogenic differentiation in osteoporosis via sponging miR-214. Circ-ITCH could act as a novel therapeutic target for osteoporosis.