Identification of PYGL as a key prognostic gene of glioma by integrated bioinformatics analysis

A glycosylation-related gene signature predicts prognosis, immune microenvironment infiltration, and drug sensitivity in glioma

Glioma represents the most common primary cancer of the central nervous system in adults. Glycosylation is a prevalent post-translational modification that occurs in eukaryotic cells, leading to a wide array of modifications on proteins. We obtained the clinical information, bulk RNA-seq data, and single-cell RNA sequencing (scRNA-seq) from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO), and Repository of Molecular Brain Neoplasia Data (Rembrandt) databases. RNA sequencing data for normal brain tissues were accessed from the Genotype-Tissue Expression (GTEx) database. Then, the glycosylation genes that were differentially expressed were identified and further subjected to variable selection using a least absolute shrinkage and selection operator (LASSO)-regularized Cox model. We further conducted enrichment analysis, qPCR, nomogram, and single-cell transcriptome to detect the glycosylation signature. Drug sensitivity analysis was also conducted. A five-gene glycosylation signature (CHPF2, PYGL, GALNT13, EXT2, and COLGALT2) classified patients into low- or high-risk groups. Survival analysis, qPCR, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group. Furthermore, GSEA and immune infiltration analysis indicated that the glycosylation signature has the potential to predict the immune response in glioma. In addition, four drugs (crizotinib, lapatinib, nilotinib, and topotecan) showed different responses between the two risk groups. Glioma cells had been classified into seven lines based on single-cell expression profiles. The five-gene glycosylation signature can accurately predict the prognosis of glioma and may offer additional guidance for immunotherapy.

Identification of glycogen phosphorylase L as a potential target for lung cancer

Traditional Chinese medicine (TCM) has been widely used for cancer treatment. Identification of anti-cancer targets of TCM is the first and principal step in discovering molecular mechanisms of TCM as well as obtaining novel targets for cancer therapy. In this study, glycogen phosphorylase L (PYGL) was identified as one of the targeted proteins for several TCMs and was upregulated in various cancer types. The expression level of PYGL was positively correlated with the stage of lung cancer and the poor prognosis of patients. Meanwhile, knockdown of PYGL significantly inhibited proliferation and migration in lung cancer cells. In addition, PYGL was associated with spindle, kinetochore, and microtubule, the cellular components that are closely related to mitosis, in lung cancer. Moreover, PYGL was more susceptible to be upregulated by 144 mutated genes. Taken together, PYGL is a potential target for lung cancer treatment and its molecular mechanism probably influences the mitotic function of cells by regulating energy metabolism.

Systematic Review of Molecular Targeted Therapies for Adult-Type Diffuse Glioma: An Analysis of Clinical and Laboratory Studies

Gliomas are the most common brain tumor in adults, and molecularly targeted therapies to treat gliomas are becoming a frequent topic of investigation. The current state of molecular targeted therapy research for adult-type diffuse gliomas has yet to be characterized, particularly following the 2021 WHO guideline changes for classifying gliomas using molecular subtypes. This systematic review sought to characterize the current state of molecular target therapy research for adult-type diffuse glioma to better inform scientific progress and guide next steps in this field of study. A systematic review was conducted in accordance with PRISMA guidelines. Studies meeting inclusion criteria were queried for study design, subject (patients, human cell lines, mice, etc.), type of tumor studied, molecular target, respective molecular pathway, and details pertaining to the molecular targeted therapy-namely the modality, dose, and duration of treatment. A total of 350 studies met the inclusion criteria. A total of 52 of these were clinical studies, 190 were laboratory studies investigating existing molecular therapies, and 108 were laboratory studies investigating new molecular targets. Further, a total of 119 ongoing clinical trials are also underway, per a detailed query on clinicaltrials.gov. GBM was the predominant tumor studied in both ongoing and published clinical studies as well as in laboratory analyses. A few studies mentioned IDH-mutant astrocytomas or oligodendrogliomas. The most common molecular targets in published clinical studies and clinical trials were protein kinase pathways, followed by microenvironmental targets, immunotherapy, and cell cycle/apoptosis pathways. The most common molecular targets in laboratory studies were also protein kinase pathways; however, cell cycle/apoptosis pathways were the next most frequent target, followed by microenvironmental targets, then immunotherapy pathways, with the wnt/β-catenin pathway arising in the cohort of novel targets. In this systematic review, we examined the current evidence on molecular targeted therapy for adult-type diffuse glioma and discussed its implications for clinical practice and future research. Ultimately, published research falls broadly into three categories-clinical studies, laboratory testing of existing therapies, and laboratory identification of novel targets-and heavily centers on GBM rather than IDH-mutant astrocytoma or oligodendroglioma. Ongoing clinical trials are numerous in this area of research as well and follow a similar pattern in tumor type and targeted pathways as published clinical studies. The most common molecular targets in all study types were protein kinase pathways. Microenvironmental targets were more numerous in clinical studies, whereas cell cycle/apoptosis were more numerous in laboratory studies. Immunotherapy pathways are on the rise in all study types, and the wnt/β-catenin pathway is increasingly identified as a novel target.

Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma

BACKGROUND AND AIM

Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence.

METHODS

The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms.

RESULTS

A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682).

CONCLUSIONS

Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.