Current Medicinal Chemistry - Current Issue

Lung Squamous Cell Carcinoma (LUSC), a major subtype of non-small cell lung cancer, presents significant treatment challenges due to limited targeted therapy options. This study aims to identify novel therapeutic targets to improve therapeutic strategies for LUSC.

By employing bulk RNA sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), survival analysis, and Mendelian Randomization (MR), we pinpointed genes with prognostic relevance to LUSC. These genes were further scrutinized for their therapeutic potential through LASSO regression, Protein-Protein Interaction (PPI) network analysis, and immune infiltration assessments. To delve into the roles and cell-specific expressions of these genes within the LUSC microenvironment, pathway enrichment analysis, single-cell RNA sequencing (scRNA-seq), and pseudotime analysis were conducted.

Our integrative approach identified 23 prognostically significant therapeutic targets, categorized into tier-one, tier-two, and tier-three genes based on their potential therapeutic relevance. Functional enrichment analyses highlighted the significant role of these genes in immune response regulation, particularly in T-cell receptor signaling and the complement system. scRNA-seq analysis revealed cell-type-specific expression patterns and pseudotime analysis provided insights into cellular heterogeneity and developmental trajectories in LUSC.

In this study, we identified 3 tier-one genes (MCM6, C4B, CTC-463A16.1), 7 tier-two genes (C4A, HLA-DRB9, LIMS2, LINC00654, MYO7B, SIGLEC5, TIE1), and 13 tier-three genes (AC007743.1, AC147651.4, ALDH2, BTN3A2, BTNL9, CCR1, GIPC3, HLA-DQB1, ICAM5, LIMD1, PM20D1, RP11-302L19.3, RP11-768F21.1).

The role of solute carrier family 6 member 17 (SLC6A17) in lung adenocarcinoma (LUAD) is unclear.

To address this gap in knowledge, we employed bioinformatics analysis and experimental validation.

This research aimed to scrutinize the expression patterns of the SLC6A17 gene across a spectrum of cancers and specifically within LUAD, utilizing data extracted from The Cancer Genome Atlas (TCGA). The correlation between SLC6A17 expression and LUAD prognosis was investigated to assess its diagnostic relevance. The study delved into the possible regulatory mechanisms of SLC6A17, focusing on its links to immune cell infiltration and drug response in LUAD. The examination of SLC6A17 expression was extended to single-cell sequencing data in LUAD, alongside an evaluation of the gene's genomic alterations and clinical implications within this disease context. Validation of SLC6A17 expression levels was conducted using datasets from GSE87340 and various cell lines, employing quantitative real-time polymerase chain reaction (qRT-PCR) techniques.

SLC6A17 exhibited aberrant expression in both pan-cancer and LUAD. Increased expression of SLC6A17 in LUAD patients was significantly associated with poorer overall survival (p = 0.008), progress-free survival (p = 0.019), and disease specific survival (p = 0.030). In LUAD patients, the levels of SLC6A17 expression were found to be a significant standalone indicator of prognosis, with a p-value of 0.031. SLC6A17 exhibited associations with various pathways, including focal adhesion, ECM receptor interaction, cell cycle, linoleic acid metabolism, pathways in cancer, and more. SLC6A17 expression demonstrated correlations with immune infiltration in LUAD. SLC6A17 expression revealed a notably inverse relationship with several substances, including AR-42, T0901317, tubastatin A, SB52334, and amuvatinib, within the context of LUAD. SLC6A17 was found to be significantly positively regulated in LUAD cell lines.

These findings suggest that SLC6A17 indicates the potential of a potential prognostic biomarker and immunotherapeutic target for patients with LUAD.

Stanniocalcin 1 (STC1) has been implicated in cancer pathogenesis, yet its pan-cancer implications and mechanistic roles in tumor progression and immune modulation remain incompletely characterized. The clinical relevance of STC1 in predicting prognosis and its interaction with tumor immune microenvironment components requires systematic investigation.

This study aims to establish the pan-cancer prognostic significance of STC1 and elucidate its associations with immunological characteristics, including immune checkpoint proteins, tumor mutational burden (TMB), microsatellite instability (MSI), and immune cell infiltration. This study focuses specifically on validating its role in the pathogenesis of gastric adenocarcinoma (STAD).

Multi-omics analysis was performed using TCGA pan-cancer datasets and bioinformatics tools (UALCAN, cBioPortal, HPA, GTA). Experimental validation included multiplex fluorescence staining of STAD tissue microarrays (n=30) and Western blot analysis of STAD cell lines. Key parameters analyzed encompassed clinical outcomes, cancer stemness indices, neoantigen load, and epithelial-mesenchymal transition (EMT) signatures.

Pan-cancer analysis revealed significant STC1 overexpression in 18/33 cancer types (54.5%), particularly in prostate adenocarcinoma (94% deep deletions). STC1 expression correlated with poor prognosis (HR=1.32, p<0.01), elevated TMB (r=0.43), and MSI (r=0.38) across multiple malignancies. Single-cell RNA sequencing demonstrated a strong association with EMC (NES=2.18, FDR<0.001). In STAD, this study confirmed 3.7-fold protein overexpression (p=0.008) and identified positive correlations with CD8⁺ T cell infiltration (r=0.62, p=0.002) and CD4⁺ T cell infiltration (r=0.58, p=0.004).

This multi-modal study establishes STC1 as a novel pan-oncogenic factor with dual roles in tumor progression (via EMT and stemness regulation) and immune microenvironment remodeling. The strong association with immune checkpoints (PD-L1, CTLA4) and T cell infiltration patterns positions STC1 as a promising immunotherapeutic target, particularly in STAD and MSI-high cancers. These findings provide mechanistic insights for developing STC1-directed therapeutic strategies.

Regulatory T cells (Tregs) play an important role in the tumor microenvironment (TME). Currently, there have been no studies of Treg-related genes (TRGs) in lung adenocarcinoma (LUAD).

We integrated the Cancer Genome Atlas (TCGA) dataset with the Gene Expression Omnibus (GEO) dataset and divided the TCGA-GEO dataset patient samples into different cohorts by unsupervised clustering analysis based on the expression of TRGs in LUAD. By analyzing the TME characteristics of different cohorts, we assessed immune cell infiltration and function. In addition, we constructed Cox risk proportional regression models based on TRGs to predict patient prognosis.

The results of unsupervised cluster analysis classified the TCGA-GEO dataset as “immune desert”, “immune evasion” and “immune inflammation”. Moreover, there was a significant survival differential among the three cohorts (p-value < 0.05). Based on the expression of 61 TRGs in LUAD, we screened TFRC, CTLA4, IL1R2, NPTN NPTN and METTL7A to construct a Cox risk proportional regression model to divide the TCGA-GEO dataset into a training cohort and a test cohort. Survival was significantly worse in the high-risk group than in the low-risk group in both the training and test cohorts (p-value < 0.05). Finally, the nomogram scoring system constructed by integrating the model risk scores with clinical parameters can well predict the 1, 3 and 5 year survival of patients.

In conclusion, based on our analysis of the TRGs of LUAD patients, we can classify the patient TME into different immune statuses, which provides insights into adopting appropriate treatment regimens for different patients.

This study aimed to develop, characterize, optimize, and evaluate the in vitro ex vivo drug release and stability of miconazole nitrate (MN)-loaded nanocrystal for topical drug delivery. MN is an antifungal agent with poor oral bioavailability and significant first-pass metabolism, necessitating alternative administration routes. Nanoformulations with lipidic/polymeric nanoparticles can overcome conventional system formulation limitations. However, it resulted in controlled MN drug release for up to 48 h and greater skin flux than did a 1% MN solution. This study aimed to identify optimized, stable, and effective in vitro/ex vivo MN-loaded nanocrystal-based hydrogels for topical drug delivery.

The nanocrystals (PN1-PN12) were developed via the precipitation method using Pluronic F-127 as a nonionic copolymer surfactant and stabilizer. The compatibility was evaluated via differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD), and Fourier transform infrared spectroscopy (FT-IR). With the help of the zetasizer, particle size, PDI, and Zeta Potential are determined. The drug in-vitro release was determined using the dialysis bag method. Carbopol 934-P and methylparaben were dissolved in distilled water with heat and constant stirring to prevent agglomeration. Permeation experiments used excised abdominal skin from Wistar rats euthanized by cervical dislocation.

The highest solubility was found in PF-127, followed by Pluronic F68. Nanocrystals were prepared via the antisolvent precipitation method. The new diffraction pattern of the nanocrystals confirms their crystalline nature and complexation with the polymer, supporting the DSC and FT-IR findings. The developed nanocrystal shows a subtle shift from 1587 to 1589 cm^-1, with no significant changes in the vibrational frequencies of the physical mixture. The PN5 formulation, with a small PS of 303.4 nm, a low PDI of 0.248, the highest drug content of 99.23 ± 5.23%, and a % cumulative drug release of 92.32 ± 3.27, was selected for further characterization. The PN5 formulations were stored under various conditions for 3 months, resulting in consistent particle sizes. SEM images revealed long, crystalline MN structures and needle-like nanocrystals. PN5 was optimized for developing a topical nanocrystal gel (PG1), which provided sustained drug release and retained significantly more drug than the other formulations did. PG1 remained stable during the 3-month storage.

The PN5 formulation, optimized for developing a topical nanocrystal gel, resulted in consistent particle size, sustained drug release, and stability over 3 months.

This investigation delves into the diagnosis of COVID-19, using X-ray images generated by way of an effective deep learning model. In terms of assessing the COVID-19 diagnosis learning model, the methods currently employed tend to focus on the accuracy rate level, while neglecting several significant assessment parameters. These parameters, which include precision, sensitivity and specificity, significantly, F1-score, and ROC-AUC influence the performance level of the model. In this paper, we have improved the InceptionResNet and called Enhanced InceptionResNet with restructured parameters termed, “Enhanced InceptionResNet,” which incorporates depth-wise separable convolutions to enhance the efficiency of feature extraction and minimize the consumption of computational resources.

For this investigation, three residual network (ResNet) models, namely ResNet, InceptionResNet model, and the Enhanced InceptionResNet with restructured parameters, were employed for a medical image classification assignment. The performance of each model was evaluated on a balanced dataset of 2600 X-ray images. The models were subsequently assessed for accuracy and loss, as well subjected to a confusion matrix analysis.

The Enhanced InceptionResNet consistently outperformed ResNet and InceptionResNet in terms of validation and testing accuracy, recall, precision, F1-score, and ROC-AUC demonstrating its superior capacity for identifying pertinent information in the data. In the context of validation and testing accuracy, our Enhanced InceptionResNet repeatedly proved to be more reliable than ResNet, an indication of the former’s capacity for the efficient identification of pertinent information in the data (99.0% and 98.35%, respectively), suggesting enhanced feature extraction capabilities.

The Enhanced InceptionResNet excelled in COVID-19 diagnosis from chest X-rays, surpassing ResNet and Default InceptionResNet in accuracy, precision, and sensitivity. Despite computational demands, it shows promise for medical image classification. Future work should leverage larger datasets, cloud platforms, and hyperparameter optimisation to improve performance, especially for distinguishing normal and pneumonia cases.