Current Medicinal Chemistry - Online First

A series of benzylidene derivatives of fenobam and its thio analogues (1-22) have been evaluated for their cytotoxicity against breast cancer (MCF-7, MDA-MB-231), ovarian cancer (A2780, SKOV-3) and cervical cancer (HELA) cell lines.

These compounds (1-22) exhibited 72-83% inhibition of Erk activity against the ovarian cancer cell line (A2780). Compounds 3 and 20 showed the highest DNA damage effect in Comet Assay against the A2780 cancer cell line as compared to the other tested analogues (4, 8, 11, 12, and 13) by using % Tail DNA and OTM. Compounds 3, 4, and 11 showed significant activities and selectivity towards COX-2 with 78%, 97%, and 89% inhibition, as compared to 17%, 57%, and 26% inhibition against COX-1 isoenzyme, respectively.

Interestingly, molecular docking scores were also in very good agreement with the experimental results regarding discriminating the selectivity index of the tested compounds against COX-1 & COX-2 enzymes. Further molecular dynamics (MD) simulation study revealed that the most selective compound, 13, binds with the COX-2 enzyme in a similar fashion to that of Rofecoxib, which was further supported by their MD-based free binding energies (MM-GBSA) of -49.76 ± 4.27 kcal/mol, and -44.84 ±3.78 kcal/mol, respectively.

Moreover, in silico ADMET predictions showed adequate properties for these compounds, making them promising leads worthy of further optimization.

HCC is a malignant tumor with high morbidity and mortality. Fibroblasts play a key role in the tumor microenvironment (TME). However, the transcriptional regulatory mechanisms of fibroblasts remained unclear in HCC.

The aim of this study was to explore the complex role of fibroblasts in hepatocellular carcinoma (HCC) and to reveal their transcriptional regulatory mechanisms.

The goal of this study was to discover potential prognostic markers for HCC by analyzing the genetic variations and differentiation process of fibroblasts.

Single-cell transcriptome data from the non-tumor liver site and primary tumor site of HCC were acquired from GSE149614, processed, and clustered using the Seurat pipeline. The inferCNV algorithm was applied to infer copy number variations (CNVs) in fibroblasts. Subsequently, the mechanism underlying the interaction between fibroblasts and other cells in the TME of HCC was analyzed using CellChat software. The trajectory of cellular differentiation of fibroblasts from normal state to malignant state was examined using Monocle 2. SCENIC analysis was performed to identify key transcription factors (TFs) in fibroblasts and assess their correlation with HCC prognosis. Finally, qRT-PCR and Transwell assays were carried out to analyze the mRNA expression and cell metastasis.

We identified a total of nine different cell types (B cells, cycling cells, endothelial cells, epithelial cells, fibroblasts, hepatocytes, macrophages, plasma cells, and T cells) based on the single-cell transcriptomic data of HCC. Among them, fibroblasts were highly enriched at the primary tumor site, and their number increased with advanced stages. In addition, significant deletions were detected on chromosome 6p of fibroblasts, and genes in this region were remarkably enriched in pathways associated with antigen processing and presentation. Intercellular communication showed that epithelial cells regulated fibroblasts the most. The differentiation of fibroblasts was mainly accompanied by a transition from normal to malignant state. Importantly, CEBPD and FOSB, the TFs most associated with the putative timing of fibroblasts, were under-expressed in human hepatocytes and showed a significant correlation with HCC prognosis. Overexpressed CEBPD inhibited HCC cell migration and invasion.

In conclusion, our study revealed that fibroblast recruitment and differentiation, as well as copy number loss at chromosome 6p, were associated with a higher degree of malignancy and immune dysfunction in HCC. The current discoveries provided new insights into the clinical treatment and diagnosis of HCC.

Infections linked to orthopedic trauma are common complications that place a significant strain on the healthcare system. Immediate identification of the infection and its severity is essential for providing effective treatment.

C-reactive Protein (CRP) is a commonly used inflammatory marker in orthopedic surgery and has proven to be a valuable biomarker for diagnosing and monitoring infections. Specifically, CRP aids in the early identification of postoperative infections. This research work has focused on developing a highly sensitive CRP biosensor using iron oxide nanomaterial-modified dielectric sensors.

Gold Urchin (GU)-conjugated aptamers and antibodies were used as probes and attached to the electrode via amine linkers. The aptamer-GU-antibody-modified electrode detected CRP at concentrations as low as 1 pg/mL, with an R² value of 0.9942. Furthermore, CRP-spiked serum exhibited an increase in current response at all concentrations of CRP, indicating selective detection of CRP. Additionally, control experiments using complementary sequences of the aptamer, relevant proteins, and non-immune antibodies did not enhance the current responses, confirming the specific identification of CRP.

The sensing strategy has enabled the detection of CRP at its lowest levels, facilitating the identification of infections during orthopedic surgery and subsequent treatment.

Globally, one of the main causes of cancer-related mortality is Colon Adenocarcinoma (COAD). In this study, a new special Immune Cell Functions (ICF) risk model was constructed using single-cell and bulk RNA sequencing data to develop a new understanding and clinical applications for COAD.

The immune function gene sets were downloaded from a literature reference, and the COAD single-cell dataset GSE146771 was downloaded from the Tumour Immune Single Cell Hub database. Using Lasso analysis, a multiple gene signature was made from the enrichment scores of immune function gene sets that were enriched in different ways. Robust validation of the signature was then performed in multiple independent cohorts. After that, we built the model using a 10-fold cross-test and evaluated its independence for clinical usage using a nomogram. We also investigated the connection between signature and immune function, genetic variation, immunotherapy, and the cancer immunological microenvironment. Lastly, we used qPCR and immunohistochemistry to examine the expression of the unreported model genes. To find the regulatory functions of unreported model genes, an EdU assay was employed.

First, 20 differentially enriched immune function gene sets were identified. Ten genes can be used as a risk profile to assess the prognosis of colon cancer, according to Lasso regression analysis. Signature performance was stable in both the training cohort and two independent GEO external cohorts, and risk scores were confirmed as independent prognostic factors. At the same time, our risk model continued to be highly predictive across various clinical clusters and clinical characteristics, such as immune checkpoints, tumour genome mutations, and chemotherapeutic drug resistance. Patients in the low-risk group have exhibited a higher chance of benefiting from immunotherapy, according to immunotherapy response research. qPCR and immunohistochemistry analysis have revealed SIRP5 expression as high in COAD tissues, while CMC1 and ASAH1 expression has been found to be low. According to the findings of the functional experiment, SIRP5, CMC1, and ASAH1 may control the ability of CRC cells to proliferate.

In this study, using scRNA-seq and bulk RNA-seq data, we created a risk model to predict the prognosis and effectiveness of immunotherapy in patients with COAD. In addition, we have discovered three model genes (SIRP5, CMC1, and ASAH1) that have not been reported before. These genes have the potential to be novel therapeutic targets in Colorectal Cancer (CRC). These findings suggest that this model could be used to evaluate the prognostic risk and identify potential targets for COAD patient treatment.

Indole is considered the most promising scaffold for anticancer drug design due to its high bioavailability, unique chemical properties, and broad spectrum of pharmacological action.

Twelve novel thiazole-containing the 5-fluoro-1,3-dihydro-2H-indol-2-one derivatives as sunitinib analogs were designed and synthesized, and their anticancer activity was evaluated against the NCI-60 cancer cell lines.

The thiazole-contained 5-fluoro-1,3-dihydro-2H-indol-2-one derivatives were synthesized using Knoevenagel condensation of 1,3-thiazole-5-carboxylic acid 1. Their anticancer activities were evaluated by NCI-60 one-dose screen assay. The molecular docking studies were performed using AutoDock tools and the AutoDock Vina programs. The ADMETlab 2.0 web server predicted the physicochemical properties of compounds.

Among the synthesized new 5-fluoro-2-oxindole derivatives, compound 3g demonstrated high antitumor activity (GI>70%) against eight types of cancer: leukemia, breast cancer, ovarian cancer, lung cancer, melanoma, CNS cancer, renal cancer, and colon cancer. The most activity was observed against breast cancer (T-47D, GI=96.17%), lung cancer (HOP-92, GI=95.95%), ovarian cancer (NCI/ADR-RES, GI=95.13%), and CNS cancer (SNB-75, GI=89.91%). The molecular docking results of compound 3g demonstrated the possibility of inhibiting VEGF2 receptors as his potential anticancer mechanism. The physicochemical properties predicted for compounds 3f and 3g showed positive results.

Compound 3g demonstrated high in vitro NCI-60 anticancer activity against nine cancer types and showed cell growth inhibition against leukemia, CNS, and breast cancer at 6 - 31% higher than Sunitinib, and may represent the basis for further modification of the thiazole-containing analogs of the anticancer drug Sunitinib.

The role of LNX1 antisense RNA 2 (LNX1-AS2) in lung adenocarcinoma (LUAD) remains unclear.

This study aimed to investigate the association between LNX1-AS2 and LUAD by employing bioinformatics analysis and experimental validation.

Statistical analysis and database interrogation were utilized to assess correlations among LNX1-AS2 expression, clinical characteristics of LUAD patients, prognostic factors, regulatory networks, and immune infiltration. LNX1-AS2 expression in LUAD cell lines was quantified using quantitative real-time polymerase chain reaction (qRT-PCR).

The study found significantly elevated levels of LNX1-AS2 expression in patients with LUAD. Furthermore, elevated LNX1-AS2 expression in LUAD patients did not significantly correlate with gender (p = 0.041) or race (p = 0.049). Importantly, high LNX1-AS2 expression levels were associated with poorer overall survival (OS, p = 0.042) and disease-specific survival (DSS, p = 0.040) in LUAD patients. Additionally, high LNX1-AS2 expression (p = 0.015) was independently correlated with OS in LUAD patients. The phenotype characterized by high LNX1-AS2 expression was also found to be enriched for asthma, allograft rejection, drug metabolism cytochrome P450, metabolism of xenobiotics by cytochrome P450, olfactory transduction, renin-angiotensin system, retinol metabolism, pentose and glucuronate interconversions, and porphyrin and chlorophyll metabolism. A significant correlation was identified between the expression levels of LNX1-AS2 and immune infiltration in the context of LUAD. Elevated expression of LNX1-AS2 was notably detected in LUAD cell lines as opposed to Beas-2B.

A noteworthy relationship was established among increased LNX1-AS2 expression in LUAD patients, unfavorable prognosis, and heightened immune infiltration. These findings suggest that the LNX1-AS2 gene could serve as a valuable prognostic indicator for LUAD and a potential predictor of response to immunotherapy.

Astragalus membranaceus has shown positive clinical efficacy in treating colorectal cancer (CRC).

This study aimed to identify the key active components of Astragalus and determine effective targets of these components in CRC patients.

We identified active components of Astragalus membranaceus and differentially expressed genes in traditional Chinese medicine systems pharmacology database and The Cancer Genome Atlas. Additionally, the enrichment analysis of differential target genes (DTGs) was performed using the R-package clusterProfiler. Immunocyte correlation analysis and non-coding regulatory network construction were performed for biomarkers using Spearman’s method and NetworkAnalyst. Finally, molecular docking of biomarkers and their corresponding molecule drugs was done with Autodock Vina software.

We identified 20 active components of Astragalus membranaceus and 1 403 target genes through screening. A total of 2 300 differentially expressed genes, and 3 035 hub genes in CRC were screened. The integration of the target genes with the significantly differentially expressed genes and Hub genes identified resulted in a total of 86 DTGs. Subsequently, the results showed 828 enriched GO biological processes, 184 enriched GO molecular functions, 59 enriched GO cellular components, and 46 enriched KEGG pathways. We also obtained a total of 143 PPI pairs involving 67 nodes. Additionally, we constructed 45 mRNA-TF pairs, 101 miRNA-mRNA pairs, and 200 miRNA-mRNA-TF triplets. Finally, molecular docking was performed for the active component quercetin with F2 and UGT1A1 and formic acid with FGA, AHSG, and KNG1.

This study identified the active components of Astragalus membranaceus and their corresponding targets in CRC. These findings provide robust evidence for precision drug therapy in patients with CRC.