Current Medicinal Chemistry - Online First

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.

Sepsis remains a leading cause of global morbidity and mortality.

To identify candidate biomarkers that may be mechanistically related to the pathogenesis of sepsis.

The Gene Expression Omnibus database was leveraged to identify differentially expressed genes (DEGs) between the healthy control and septicemia groups. Genes causally related to sepsis were probed through the integration of GWAS and expression quantitative trait loci (eQTL) data in a two-sample Mendelian randomization (MR) analysis. A set of key sepsis-related genes was then selected based on the overlap between these putative causal genes and the DEGs. These genes were then subjected to enrichment analyses, testing set validation, and analyses of their expression dynamics in clinical samples.

An examination of the overlap between 228 sepsis-related DEGs identified in the training dataset and 275 candidate causal genes linked to sepsis derived from the MR analysis led to the selection of four overlapping (SLC22A15, IL5RA, HDC, and SLC46A2) that may play a key role in sepsis. Enrichment analyses indicated that these genes were involved in the regulation of histidine metabolism and immune/inflammatory responses. In immune cell infiltration analyses, these genes were positively correlated with inflammatory response activation and the suppression of adaptive immunity. Consistent findings were obtained through qPCR verification in clinical samples.

These results offer potential insight into the mechanisms that govern septicemia and thus suggest a promising series of candidates that may be amenable to targeting to prevent or treat sepsis more effectively.

The emergence of the monkeypox virus (MPXV) as a zoonotic threat has necessitated the development of effective treatments, particularly after it spread to regions outside of Central and Western Africa, such as the 2003 outbreak in the United States. Our groundbreaking study identifies CDK1 and TOP2A as key proteins in the pathogenesis of MPXV infection, utilizing network pharmacology to target these proteins for the first time. CDK1 and TOP2A, previously known for their roles in cell reprogramming, emerge as critical targets in our strategy to combat the virus.

By targeting CDK1 and TOP2A, proteins integral to cell reprogramming, with small molecules identified in our study, such as carnosic acid, rosmarinic acid, and coclaurine, we propose a novel method not only to inhibit the replication of the monkeypox virus but also to harness cellular plasticity for therapeutic purposes. The identification and targeting of these proteins with specific compounds disrupt the virus's life cycle and simultaneously enhance the efficiency of cell reprogramming.

This dual-action approach leverages the inherent plasticity of cellular reprogramming processes to combat the virus, showcasing a pioneering step in the use of regenerative medicine principles for antiviral strategies. Moreover, molecular docking and dynamic simulations strengthen our findings by demonstrating a strong binding affinity between TOP2A and CDK1, validating the synergistic effects of our identified small molecules.

Our research thus opens new avenues for addressing viral threats like monkeypox, utilizing the convergence of virology, network pharmacology, and cellular reprogramming to pave the way for innovative treatments.

Collagen type IV alpha 1 chain (COL4A1), which has been proven to be a potential biomarker in Gastric Cancer (GC), but its role in tumors and the tumor microenvironment (TME) needs further explanation.

We analysed the relationship between COL4A1 and clinical characteristics based on The Cancer Genome Atlas (TCGA) database and verified by tissue microarrays as well as GC cell lines using immunohistochemistry, Q-PCR, Western blot, cell proliferation assays, colony formation assays, cell invasion and migration assays. The immune infiltration and drug sensitivity information between high and low COL4A1 expression were analysed by R package and pRRophetic package. Finally, we established a nomogram based on COL4A1 expression using the bootstrap method.

COL4A1 was overexpressed in gastric carcinoma compared with normal gastric tissue, indicating a poor prognosis of GC patients in the TCGA database which were also validated by GC tissue microarrays. GO, KEGG and hallmark enrichment analyses indicated that COL4A1 was mainly associated with the extracellular matrix than malignant proliferation. By siRNA transfection, we found that COL4A1 knockdown inhibited cell colony formation, invasion and migration but did not affect cell proliferation, similar to previous results. Immune infiltration and drug sensitivity analysis showed that COL4A1 was negatively correlated with antitumor immunity and positively correlated with multidrug resistance. By developing a nomogram model based on 8 risk factors, including COL4A1, patients with better clinical outcomes could be accurately distinguished.

COL4A1 is identified as a prognostic marker and potential therapeutic target in gastric cancer. Its overexpression correlates with poor prognosis, tumor invasion, and immunosuppression. A nomogram based on COL4A1 can predict patient outcomes. Future research should validate these findings and explore targeted therapies.

ZNF280A, a pivotal member of the zinc finger protein family, is significantly involved in vital cellular functions including cell proliferation, programmed cell death, cellular invasion, metastasis, and resistance to therapeutic drugs across various malignancies. However, its comprehensive role in pan-cancer has not been thoroughly investigated.

This research aims to elucidate the oncogenic and immunological functions of ZNF280A across different types of cancer. We conducted an extensive analysis of ZNF280A expression levels, prognostic significance, functional pathways, methylation status, and interactions with immune cells, while also examining immune infiltration patterns and responses to immunotherapy using diverse databases.

Our findings reveal that ZNF280A expression is significantly upregulated in numerous cancers, correlating with adverse patient prognosis. This association appears to be linked to its involvement in key cancer-related pathways, including the Ras signaling pathway, and its correlation with ZNF280A methylation levels, microsatellite instability (MSI), tumor mutational burden (TMB), and the dynamics of immune cells. Notably, ZNF280A seems to undermine anti-tumor immunity and the effectiveness of immunotherapeutic approaches by promoting the infiltration of immune cells and compromising the functionality of cytotoxic T lymphocytes.

These findings suggest that ZNF280A holds promise as a valuable indicator for forecasting patient outcomes and assessing the effectiveness of immunotherapy, thereby opening avenues for further exploration into targeted therapeutic approaches.

Ovarian cancer (OV) is one of the deadliest gynecologic cancers, and approximately 75% of serous ovarian cancer (SOC) patients are diagnosed at advanced stages due to the lack of effective biomarkers.

Immunogenic cell death (ICD) has been investigated in many comprehensive studies, and the role of ICD in ovarian cancer and its impact on immunotherapy is not yet known.

The NMF clustering analysis was employed to categorize OV samples into different subgroups. Survival, mutation, and CNV analyses were performed in these clusters. ESTIMATE, CIBERSORT, TIDE, and drug sensitivity analyses (based on GDSC) were also performed on the subtypes. Then, differentially expressed immunogenic cell death genes (DE-ICDGs) in OV were obtained by crossing the DEGs between cluster3 vs. cluster1, DEGs from the TCGA-GTEx dataset, and DEGs from the GSE40595 dataset. Functional enrichment analysis of DE-ICDGs was then performed. The signature genes related to the prognosis of OV in three OV datasets were excavated by drawing Kaplan-Meier curves. Finally, quantitative real-time PCR (qRT-PCR) was performed to verify the expression trends of the signature genes.

The NMF clustering analysis categorized OV samples into three distinct groups according to the expression levels of ICDGs, with differential analysis indicating that Cluster3 represented the subgroup with high ICD expression. Mutation and CNV analysis did not differ significantly between clusters, but Amp and Del's numbers did. Immuno-infiltration analysis revealed that cluster3 showed significant differences from cluster1 and cluster2. Immunotherapy and drug sensitivity analysis showed differences in immunotherapy and chemotherapy sensitivity between the clusters. The DEGs in cluster3 vs. cluster1, TCGA-GTEx dataset and GSE40595 dataset were intersected to obtain a total of 71 DE-ICDGs, and functional enrichment result suggested that the DE-ICDGs were significantly correlated with inflammatory response, complement system and positive regulation of cytokine production. 2 DE-ICDGs (FN1 and LUM) were identified that were associated with OV prognosis and were validated significantly down-regulated in the SOC group with PCR.

We identified ICD-associated subtypes of OV and mined 2 OV prognostic genes (FN1 and LUM) associated with ICD, which may have important implications for OV prognosis and therapy.