Clinical Prognostic Value of the PLOD Gene Family in Lung Adenocarcinoma

Designing lysyl hydroxylase inhibitors for oral submucous fibrosis - Insights from molecular dynamics

Alpha-ketoglutarate (αKG) dependent Lysyl hydroxylase (LH) is a critical enzyme in the post-translational conversion of lysine into hydroxylysine in collagen triple helix and telopeptide regions. Overexpression of LH increases collagen hydroxylation and covalent cross-linkage, causing fibrosis. Currently, no drugs are available to inhibit LH potentially. Virtual screening of the Zinc database was employed to identify new leads. They were docked using Glide. Lead1 complex exhibits a notably superior docking score compared to other leads. This complex hinders iron stabilization by engaging with the HXD..Xn..H motif and competitively inhibiting 2OG binding at the catalytic site via interactions with Cys691 and Arg729 by forming a salt bridge. Molecular dynamics simulations over a 500 ns time scale and molecular mechanics Poisson-Boltzmann surface area calculations illustrate the stable binding of Leads. DCCA analysis finds the coordinated residue motions and the influence of the second coordinating sphere in long-range interactions. In-silico results were validated by quantifying the amount of collagen in zebrafish through histology and hydroxyproline assay. These findings demonstrated a reduction in collagen deposition in the treated samples compared to the positive control. This computational study unveiled insights into how leads may impede collagen lysine hydroxylation and potentially impact collagen-related processes.

Identification and validation of a glycosyltransferase gene signature as a novel prognostic model for lung adenocarcinoma

Background

The role of glycosyltransferase (GT) genes in lung adenocarcinoma (LUAD) needs further elucidation. Thus, our study aims to identify the prognostic gene signature of LUAD and explore its molecular functions.

Methods

We initially extracted GT gene sets from the database, and obtained mRNA expression levels and clinical data from The Cancer Genome Atlas (TCGA) database. For constructing a prognostic model for GT genes, we utilized univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Using the model, patients were categorized into high- and low-risk groups. Additionally, we evaluated differences in tumor immune infiltration between these groups and identified potential therapeutic drugs. Finally, we experimentally validated the expression levels of these crucial prognostic genes.

Results

We developed a risk score comprising nine GT genes (C1GALT1, FUT1, GALNT2, PLOD2, POMK, PYGB, ST3GAL6, UGT2B11, UGT3A1). Patients were then categorized into low- and high-risk groups based on this score. The low-risk group showed superior overall survival (OS) compared to the high-risk group. There were significantly distinct tumor immune microenvironment statuses observed between the two groups. We identified potential therapeutic drugs, including the MEK inhibitor (PD-184352). Finally, we verified the expression of these nine GT genes through immunohistochemistry (IHC) staining and quantitative real-time PCR (qPCR).

Conclusion

We identified a distinct LUAD GT gene signature, and these differentially expressed mRNAs could serve as valuable prognostic biomarkers and therapeutic targets. Furthermore, we experimentally validated their expression levels and identified potential therapeutic agents.

Identifying the EMT-related signature to stratify prognosis and evaluate the tumor microenvironment in lung adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is a critical process in tumor invasion and metastasis. EMT has been shown to significantly influence the invasion, metastasis, and poor prognosis in lung adenocarcinoma (LUAD). This study aimed to develop a novel EMT-related prognostic model capable of predicting overall survival (OS) in patients with LUAD.

Methods: A total of 283 LUAD patients from TCGA RNA-seq dataset were assigned to a training cohort for model building, and 310 LUAD patients from GEO RNA-seq dataset were assigned to a validation cohort. EMT genes were acquired from MsigDB database and then prognosis-related EMT genes were identified by univariate Cox regression. Lasso regression was then performed to determine the genes and the corresponding variables to construct a prognosis risk model from the training cohort. Furthermore, characteristics of the tumor microenvironment (TME), mutation status and chemotherapy responses were analyzed to assess the differences between the two risk groups based on the prognostic model. In addition, RT-qPCR was employed to validate the expression patterns of the 6 genes derived from the risk model.

Results: A six-gene EMT signature (PMEPA1, LOXL2, PLOD2, MMP14, SPOCK1 and DCN) was successfully constructed and validated. The signature assigned the LUAD patients into high-risk and low-risk groups. In comparison with the low-risk group, patients in the high-risk group had a significantly lower survival rate. ROC curves and calibration curves for the risk model demonstrated reliable stratification and predictive ability. The risk model was robustly correlated with multiple TME characteristics. Besides, the data showed that patients in the low-risk group had more immune activities, higher stemness scores and cytolytic activity scores and higher TMB. In addition, RT-qPCR results revealed that PMEPA1, LOXL2, PLOD2, MMP14, and SPOCK1 were notably upregulated in LUAD tissues, while DCN was downregulated.

Conclusion: Our study successfully developed a novel EMT-related signature to predict prognosis of LUAD patients and guide treatment strategies. The six genes derived from the prediction signature might play a potential role in antitumor immunity and serve as promising therapeutic targets in LUAD.

PLOD Family: A Novel Biomarker for Prognosis and Personalized Treatment in Soft Tissue Sarcoma

Despite various treatment attempts, the heterogenous group of soft tissue sarcomata (STS) with more than 100 subtypes still shows poor outcomes. Therefore, effective biomarkers for prognosis prediction and personalized treatment are of high importance. The Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase (PLOD) gene family, which is related to multiple cancer entities, consists of three members which encode important enzymes for the formation of connective tissue. The relation to STS, however, has not yet been explored. In this study, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to analyze the role of PLOD1–3 in STS. It was found that an overexpression of PLOD family members correlates with poor prognosis, which might be due to an increased infiltration of immune-related cells in the tumor microenvironment. In STS, the expression of PLOD genes could be a novel biomarker for prognosis and a personalized, more aggressive treatment in these patients.