Current Medicinal Chemistry - Online First

This study aimed to establish a PANoptosis related prognostic signature and identify potential prognostic markers and therapeutic targets for HCC.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. The survival rate of patients with HCC remains relatively low. PANoptosis can be mediated by lncRNA to involve the pathophysiology of HCC, but the mechanism is still unclear.

TCGA and GEO hepatocellular carcinoma databases and previous research results were used to construct the PANoptosis related risk model.

Based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, this study identified long non-coding RNAs (lncRNAs) associated with PANoptosis in HCC. Univariate, LASSO-Cox, and multivariate COX analyses were employed to gradually screen prognostic lncRNAs and construct prognostic models. Further analysis was conducted on the core lncRNA-AC034229.4.

A total of 8 differentially expressed lncRNAs closely correlated with HCC prognosis were discovered. A prognostic model comprising 6 lncRNAs (AC090192.2, LINC01703, AC034229.4, AC073352.1, AC004816.1, and AL136162.1) was established demonstrating good predictive ability for prognosis. Moreover, this prognostic model exhibited close associations with tumor immune microenvironment and immune checkpoints. Subsequent investigations revealed that AC034229.4 independently influenced HCC prognosis by regulating cell cycle progression and inhibiting the immune microenvironment response. Drug sensitivity analysis indicated that AC034229 .4 displayed sensitivity to various anticancer drugs as well. In addition, inhibition of AC034229 .4 expression suppressed HCC migration and invasion abilities.

This study generated a novel and efficient prognostic signature model while identifying AC034229 .4 as a promising diagnostic and prognostic biomarker in HCC.

The purpose of this study is to construct and validate a neuroendocrine differentiation-related molecular model for predicting prognosis in patients with prostate cancer (PCa).

Transcriptome data for PCa were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) websites. Differentially expressed neuroendocrine differentiation related genes (NDGs) were identified. By utilizing multivariate Cox analysis, a neuroendocrine differentiation-related molecular model for predicting prognosis was constructed and validated. The study investigated the novel model’s association with the tumor immune microenvironment, clinicopathological characteristics, tumor stemness, and anticancer treatment sensitivity. Additionally, preliminary experimental verifications of Diencephalon / Mesencephalon Homeobox 1 (DMBX1) were conducted.

Finally, we identified a total of 19 differentially expressed NDGs. A neuroendocrine differentiation-related molecular model was established and successfully validated both internally and externally. The high-risk group   exhibited   significantly   poorer biochemical recurrence-free survival (BCRFS) in the training, testing, and validating cohorts. The areas under the receiver operating characteristic curves for the training, testing, and validating cohorts were 0.825, 0.719, and 0.729, respectively. The tumor immune microenvironment, clinicopathological features, tumor stemness, and anti-cancer drug sensitivity was significantly different between high and low-risk patients. Preliminary experiments revealed that higher expression of DMBX1 significantly enhanced the proliferation, migration, and neuroendocrine differentiation of PCa cells.

This research developed a unique neuroendocrine differentiation-related molecular model that is highly suitable for predicting BCRFS. High DMBX1 expression may promote the development and neuroendocrine differentiation of prostate cancer.

Icaritin is a bioactive flavonol isolated from the Chinese medicinal herb Epimedium. The comprehensive understanding of antifibrotic effects and associated molecular mechanisms of icaritin remains incomplete. This study aims to explore the protective effects of icaritin against liver fibrosis and to further elucidate the mechanisms involved.

Human hepatic stellate LX-2 cells stimulated with TGF-β1 and a carbon tetrachloride (CCl4)-induced liver fibrosis mouse model were employed. In vitro assays were carried out to evaluate collagen type I (COL I) and α-smooth muscle actin (α-SMA) expression, while in vivo studies assessed fibrosis alleviation. Molecular mechanisms were explored via analysis of TGF-β1, phosphorylated Smad2/3, and HIF-1α protein levels using Western blotting.

Icaritin suppressed TGF-β1-induced COL I and α-SMA expression in LX-2 cells and ameliorated liver fibrosis in CCl4-treated mice. Mechanistically, it significantly reduced TGF-β1 levels, inhibited Smad2/3 phosphorylation, and downregulated HIF-1α protein expression in LX-2 cells.

Icaritin attenuated experimental liver fibrosis through the inhibition of the TGF-β/Smad and HIF-1α signaling pathways, highlighting its therapeutic potential for fibrotic liver diseases.

Inflammatory osteolysis often characterizes many orthopedic diseases, with an important role played by the overactivity of osteoclasts. This research endeavoured to investigate the effects of spinosin, a potent ingredient in traditional Chinese medicine, on Lipopolysaccharide (LPS)-induced osteoclast activity and formation to alleviate osteolysis.

Based on the molecular structure of spinosin, network pharmacology was used to predict its primary targets and mechanisms. LPS was used to stimulate pre-osteoclasts and to simulate an inflammatory environment. The effect of spinosin on osteoclast biology was subsequently examined via morphological study, qPCR, and Western blot (WB). Moreover, LPS-induced cranial osteolysis mice were utilized, followed by micro-CT analysis, to reveal the curative effects in vivo.

Network pharmacology and molecular docking suggested that EGFR and Akt might be the key targets for the efficacy of spinosin in inflammatory osteolysis. The results of in vitro experiments demonstrated that spinosin significantly inhibited osteoclast function and activity in the inflammatory environment, and this effect might be achieved through regulating EGFR-Akt signaling. The results of animal experiments also showed spinosin-protected mice against LPS-induced bone loss.

Spinosin can inhibit EGFR-mediated Akt phosphorylation, which in turn negatively affects downstream Nfatc1-mediated osteoclast-associated gene expression and subsequent osteoclast formation and functionality, mitigating the LPS-induced osteolysis. Our study proves that spinosin holds the promise of being an innovative drug to prevent inflammatory osteolysis.

Peripheral T-cell lymphoma (PTCL) is a rare and heterogeneous group of hematological malignancies. Treatment options are limited and often unsatisfactory, leading to a poor prognosis in most subtypes.

This study aimed to identify potential biomarker genes for PTCL and to explore the underlying mechanisms by integrating machine learning, Mendelian Randomization (MR), and experimental validation.

Microarray datasets (GSE6338, GSE14879, and GSE59307) were downloaded from the Gene Expression Omnibus database. Differential expression analysis was conducted to identify the Differentially Expressed Genes (DEGs) between patients with PTCL and controls. A machine learning algorithm was then used to further refine the selection of characteristic genes for PTCL. We integrated genome-wide association studies data with expression quantitative trait loci data to identify genes with potential causal relationships to PTCL. Functional analysis was performed to explore underlying mechanisms. Finally, the identified gene was validated in clinical samples from patients with PTCL and controls.

Based on 60 DEGs, the least absolute shrinkage and selection operator algorithm identified nine characteristic genes for PTCL. MR analysis revealed 203 genes with causal effects on PTCL, ultimately identifying one co-expressed gene: Basic Leucine Zipper ATF-like Transcription Factor 3 (BATF3). It demonstrated good predictive performance across various PTCL subtypes, with AUC values ranging from 0.7 to 1. Functional analysis suggested that BATF3 may play a role in PTCL through immune-related pathways. Experimental validation using clinical samples further suggested the potential of this biomarker gene in PTCL.

By combining machine learning, MR, and experimental validation, we identified and validated BATF3 as a promising biomarker of PTCL. These findings provide insights into the molecular mechanisms underlying PTCL and may inform the development of effective treatment strategies for this disease.

Metastasis is the leading cause of death in lung cancer patients. Pre-metastatic niche (PMN) plays an important role in pre-metastatic tumors. However, the development of clinical applications of PMN is still limited.

Expression data for lung adenocarcinoma (LUAD) patients and PMN-related genes were downloaded from the UCSC Xena website and GeneCards database, respectively. Multiple combinations based on machine learning algorithms were used to screen signature genes and construct a PMN-associated index. Spearman analysis explored the correlation between the PMN-associated index and immune cell infiltration. In addition, we analyzed the clinical value of the PMN-associated index based on drug sensitivity analysis and TIDE scores.

The enrichment analyses suggested that PMN-related genes were mainly enriched in the PI3K-Akt and HIF-1 signaling pathways. We chose random survival forest, Lasso, and multivariate Cox regression analyses to construct the PMN-associated index based on the results of multiple machine learning algorithms. Six signature genes (SNAI2, CXCR4, TNFSF11, ENG, TIMP1, and PDGFB) were screened to construct the PMN-associated index. KM analysis suggested that the survival probability was greater in the low PMN-associated index group than in the high PMN-associated index group. In addition, we confirmed that LUAD patients with a low PMN-associated index were more likely to benefit from immunotherapy.

We confirmed that the PMN-associated index is a valid predictor of prognosis, immune characteristics, and antitumor therapy efficacy in LUAD patients, which provides additional evidence for the potential clinical value of PMN development.