Phospholipase Cγ1 (PLCG1) overexpression is associated with tumor growth and poor survival in IDH wild-type lower-grade gliomas in adult patients

Preliminary analysis of pathways and their implications during salinity stress in abalone

Transcriptome sequencing has offered immense opportunities to study non-model organisms. Abalone is an important marine mollusk that encounters harsh environmental conditions in its natural habitat and under aquaculture conditions; hence, research that increases molecular information to understand abalone physiology and stress response is noteworthy. Accordingly, the study used transcriptome sequencing of the gill tissues of abalone exposed to low salinity stress. The aim is to explore some enriched pathways during salinity stress and the crosstalk and functions of the genes involved in the candidate biological processes for future further analysis of their expression patterns. The data suggest that abalone genes such as YAP/TAZ, Myc, Nkd, and Axin (involved in the Hippo signaling pathway) and PI3K/Akt, SHC, and RTK (involved in the Ras signaling pathways) might mediate growth and development. Thus, deregulation of the Hippo and Ras pathways by salinity stress could be a possible mechanism by which unfavorable salinities influence growth in abalone. Furthermore, PEPCK, GYS, and PLC genes (mediating the Glucagon signaling pathway) might be necessary for glucose homeostasis, reproduction, and abalone meat sensory qualities; hence, a need to investigate how they might be influenced by environmental stress. Genes such as MYD88, IRAK1/4, JNK, AP-1, and TRAF6 (mediating the MAPK signaling pathway) could be useful in understanding abalone's innate immune response to environmental stresses. Finally, the aminoacyl-tRNA biosynthesis pathway hints at the mechanism by which new raw materials for protein biosynthesis are mobilized for physiological processes and how abalone might respond to this process during salinity stress. Low salinity clearly regulated genes in these pathways in a time-dependent manner, as hinted by the heat maps. In the future, qRT-PCR verification and in-depth study of the various genes and proteins discussed would provide enormous molecular information resources for the abalone biology.

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

A novel micellular fluorogenic substrate for quantitating the activity of 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma (PLCγ) enzymes

The activities of the phospholipase C gamma (PLCγ) 1 and 2 enzymes are essential for numerous cellular processes. Unsurprisingly, dysregulation of PLCγ1 or PLCγ2 activity is associated with multiple maladies including immune disorders, cancers, and neurodegenerative diseases. Therefore, the modulation of either of these two enzymes has been suggested as a therapeutic strategy to combat these diseases. To aid in the discovery of PLCγ family enzyme modulators that could be developed into therapeutic agents, we have synthesized a high-throughput screening-amenable micellular fluorogenic substrate called C16CF3-coumarin. Herein, the ability of PLCγ1 and PLCγ2 to enzymatically process C16CF3-coumarin was confirmed, the micellular assay conditions were optimized, and the kinetics of the reaction were determined. A proof-of-principle pilot screen of the Library of Pharmacologically Active Compounds 1280 (LOPAC1280) was performed. This new substrate allows for an additional screening methodology to identify modulators of the PLCγ family of enzymes.

Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration

The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene-gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk. Our method achieved better accuracy compared with existing methods. Furthermore, key factors nonlinearly associated with the tumor pathogenesis are prioritized by employing two interpretation algorithms (i.e. GNN-Explainer and IGscore) for neural networks, at gene and pathway level, respectively. The top genes and pathways exhibit strong associations with disease in survival analyses, many of which such as SEC61G and CYP27B1 are previously reported in the literature.

PRIM2: A Marker of MYC-driven Hyper-proliferation, Disease Progression, Tumor Aggressiveness and Poor Survival in Glioma Patients

BACKGROUND/AIM

Gliomas are the most prevalent brain tumors with metabolic alterations playing a pivotal role in disease progression. However, the precise coordination of metabolic alterations with tumor-promoting cellular mechanisms, leading to tumor initiation, progression, and aggressiveness, resulting in poor outcomes, remains poorly understood in gliomas.

MATERIALS AND METHODS

We conducted a metabolism-targeted differential gene expression analysis using glioma patients' expression profiling data from The Cancer Genome Atlas (TCGA) database. In addition, pathway enrichment analysis, gene set enrichment analysis (GSEA), transcription factor prediction, network construction, and correlation analyses were performed. Survival analyses were performed in R. All results were validated using independent GEO expression datasets.

RESULTS

Metabolism-targeted analysis identified 5 hits involved in diverse metabolic processes linking them to disease aggressiveness in gliomas. Subsequently, we established that cell cycle progression and hyper-proliferation are key drivers of tumor progression and aggressiveness in gliomas. One of the identified metabolic hits, DNA primase 2 (PRIM2), a gene involved in DNA replication was found directly associated with cell cycle progression in gliomas. Furthermore, our analysis indicated that PRIM2, along with other cell cycle-related genes, is under the control of and regulated by the oncogenic MYC transcription factor in gliomas. In addition, PRIM2 expression alone is enough to predict MYC-driven cell cycle progression and is associated with tumor progression, aggressive disease state, and poor survival in glioma patients.

CONCLUSION

Our findings highlight PRIM2 as a marker of MYC-driven cell cycle progression and hyper-proliferation, disease onset and progression, tumor aggressiveness, and poor survival in glioma patients.

Protein S-palmitoylation modification: implications in tumor and tumor immune microenvironment

Protein S-palmitoylation is a reversible post-translational lipid modification that involves the addition of a 16-carbon palmitoyl group to a protein cysteine residue via a thioester linkage. This modification plays a crucial role in the regulation protein localization, accumulation, secretion, stability, and function. Dysregulation of protein S-palmitoylation can disrupt cellular pathways and contribute to the development of various diseases, particularly cancers. Aberrant S-palmitoylation has been extensively studied and proven to be involved in tumor initiation and growth, metastasis, and apoptosis. In addition, emerging evidence suggests that protein S-palmitoylation may also have a potential role in immune modulation. Therefore, a comprehensive understanding of the regulatory mechanisms of S-palmitoylation in tumor cells and the tumor immune microenvironment is essential to improve our understanding of this process. In this review, we summarize the recent progress of S-palmitoylation in tumors and the tumor immune microenvironment, focusing on the S-palmitoylation modification of various proteins. Furthermore, we propose new ideas for immunotherapeutic strategies through S-palmitoylation intervention.

Microneedle Patch Delivery of PLCG1-siRNA Efficient Enhanced Temozolomide Therapy for Glioblastoma

The blood-brain barrier (BBB) and drug resistance present challenges for chemotherapy of glioblastoma (GBM). A microneedle (MN) patch with excellent biocompatibility and biodegradability was designed to bypass the BBB and release temozolomide (TMZ) and PLCG1-siRNA directly into the tumor site for synergistic treatment of GBM. The codelivery of TMZ and PLCG1-siRNA enhanced DNA damage and apoptosis. The potential mechanism behind this enhancement is to knockdown of PLCG1 expression, which positively regulates the expression of signal transducer and activator of transcription 3 genes, thereby preventing DNA repair and enhancing the sensitivity of GBM to TMZ. The MN patch enables long-term sustainable drug release through in situ implantation and increases local drug concentrations in diseased areas, significantly extending mouse survival time compared to other drug treatment groups. MN drug delivery provides a platform for the combination treatment of GBM and other central nervous system diseases.

Patients with Systemic Juvenile Idiopathic Arthritis (SJIA) Show Differences in Autoantibody Signatures Based on Disease Activity

Systemic juvenile idiopathic arthritis (SJIA) is a severe rheumatic disease in children. It is a subgroup of juvenile idiopathic arthritis (JIA; MIM #604302), which is the most common rheumatic disease in children. The diagnosis of SJIA often comes with a significant delay, and the classification between autoinflammatory and autoimmune disease is still discussed. In this study, we analyzed the immunological responses of patients with SJIA, using human proteome arrays presenting immobilized recombinantly expressed human proteins, to analyze the involvement of autoantibodies in SJIA. Results from group comparisons show several differentially reactive antigens involved in inflammatory processes. Intriguingly, many of the identified antigens had a high reactivity against proteins involved in the NF-κB pathway, and it is also notable that many of the detected DIRAGs are described as dysregulated in rheumatoid arthritis. Our data highlight novel proteins and pathways potentially dysregulated in SJIA and offer a unique approach to unraveling the underlying disease pathogenesis in this chronic arthropathy.

Phosphatidylcholine-Specific Phospholipase C as a Promising Drug Target

Phosphatidylcholine-specific phospholipase C (PC-PLC) is an enzyme that catalyzes the formation of the important secondary messengers phosphocholine and diacylglycerol (DAG) from phosphatidylcholine. Although PC-PLC has been linked to the progression of many pathological conditions, including cancer, atherosclerosis, inflammation and neuronal cell death, studies of PC-PLC on the protein level have been somewhat neglected with relatively scarce data. To date, the human gene expressing PC-PLC has not yet been found, and the only protein structure of PC-PLC that has been solved was from Bacillus cereus (PC-PLCBc). Nonetheless, there is evidence for PC-PLC activity as a human functional equivalent of its prokaryotic counterpart. Additionally, inhibitors of PC-PLCBc have been developed as potential therapeutic agents. The most notable classes include 2-aminohydroxamic acids, xanthates, N,N'-hydroxyureas, phospholipid analogues, 1,4-oxazepines, pyrido[3,4-b]indoles, morpholinobenzoic acids and univalent ions. However, many medicinal chemistry studies lack evidence for their cellular and in vivo effects, which hampers the progression of the inhibitors towards the clinic. This review outlines the pathological implications of PC-PLC and highlights current progress and future challenges in the development of PC-PLC inhibitors from the literature.

Phospholipases in Gliomas: Current Knowledge and Future Perspectives from Bench to Bedside

Phospholipases are essential intermediaries that work as hydrolyzing enzymes of phospholipids (PLs), which represent the most abundant species contributing to the biological membranes of nervous cells of the healthy human brain. They generate different lipid mediators, such as diacylglycerol, phosphatidic acid, lysophosphatidic acid, and arachidonic acid, representing key elements of intra- and inter-cellular signaling and being involved in the regulation of several cellular mechanisms that can promote tumor progression and aggressiveness. In this review, it is summarized the current knowledge about the role of phospholipases in brain tumor progression, focusing on low- and high-grade gliomas, representing promising prognostic or therapeutic targets in cancer therapies due to their influential roles in cell proliferation, migration, growth, and survival. A deeper understanding of the phospholipases-related signaling pathways could be necessary to pave the way for new targeted therapeutic strategies.

A novel risk score model based on pyroptosis-related genes for predicting survival and immunogenic landscape in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer worldwide, with high incidence and mortality. Pyroptosis, a form of inflammatory-regulated cell death, is closely associated with oncogenesis.

METHODS

Expression profiles of HCC were downloaded from the TCGA database and validated using the ICGC and GEO databases. Consensus clustering analysis was used to determine distinct clusters. The pyroptosis-related genes (PRGs) included in the pyroptosis-related signature were selected by univariate Cox regression and LASSO regression analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to estimate the prognostic potential of the model. The characteristics of infiltration of immune cells between different groups of HCC were explored.

RESULTS

Two independent clusters were identified according to PRG expression. Cluster 2 showed upregulated expression, poor prognosis, increased immune cell infiltration and worse immunotherapy response than cluster 1. A prognostic risk signature consisting of five genes (GSDME, NOD1, PLCG1, NLRP6 and NLRC4) was identified. In the high-risk score group, HCC patients showed decreased survival rates. In particular, multiple clinicopathological characteristics and immune cell infiltration were significantly associated with the risk score. Notably, the 5 PRGs in the risk score have been implicated in carcinogenesis, immunological pathways and drug sensitivity.

CONCLUSIONS

A prognostic signature comprising five PRGs can be used as a potential prognostic factor for HCC. The PRG-related signature provides an in-depth understanding of the association between pyroptosis and chemotherapy or immunotherapy for HCC patients.

Systematic Analysis of a Pyroptosis-Related Signature to Predict the Prognosis and Immune Microenvironment of Lower-Grade Glioma

Current treatments for lower-grade glioma (LGG) do not effectively improve life expectancy rates, and this is a major global health concern. Improving our knowledge of this disease will ultimately help to improve prevention, accurate prognosis, and treatment strategies. Pyroptosis is an inflammatory form of regulated cell death, which plays an important role in tumor progression and occurrence. There is still a lack of effective markers to evaluate the prognosis of LGG patients. We collected paraffin-embedded tissue samples and prognostic information from 85 patients with low-grade gliomas and fabricated them into a tissue microarray. Combining data from public databases, we explored the relationship between pyroptosis-related genes (PRGs) and the prognoses of patients with LGG and investigated their correlations with the tumor microenvironment (TME) by means of machine learning, single-cell, immunohistochemical, nomogram, GSEA, and Cox regression analyses. We developed a six-gene PRG-based prognostic model, and the results have identified CASP4 as an effective marker for LGG prognosis predictions. Furthermore, the effects on immune cell infiltration may also provide guidance for future immunotherapy strategies.

Cathepsin a upregulation in glioma: A potential therapeutic target associated with immune infiltration

BACKGROUND

Glioma is the result of malignant transformation of glial cells in the white matter of the brain or spinal cord and accounts for approximately 80% of all intracranial malignancies. Cathepsin A (CTSA) is highly expressed in a variety of tumor tissues, but its role in glioma is poorly studied. This study analyses the relationship between CTSA, and glioma based on The Cancer Genome Atlas (TCGA).

METHODS

Data for glioma patients were collected from TCGA. The expression level of CTSA was compared between paired glioma tissues and normal tissues with Wilcoxon rank-sum test. In addition, the Wilcoxon ranksum test was also applied to analyze the relationship between clinicopathologic features and CTSA expression. Kaplan-Meier Plotter was applied to analyze OS, DSS and PFI. Immuno-infiltration analysis of BLCA was performed by single sample gene set enrichment analysis (ssGSEA) in the "GSVA" R package.

RESULTS

The CTSA was overexpressed in glioma tissues compared to normal tissues (P<0.001). The high expression of CTSA was significantly related to 1p/19q codeletion, IDH, WHO grade and histological type. Kaplan-Meier survival analysis showed that patients with glioma characterized with high expressed CTSA had a poorer OS (HR=2.16 P<0.001), DSS (HR=2.17 P<0.001) and PFI (HR=1.48 P<0.001) than patients with low CTSA expression. Moreover, High expressed CTSA was associated with immune cell infiltration.

CONCLUSIONS

CTSA may serve as a candidate prognostic biomarker for determining prognosis associated with immune infiltration in glioma.

The cell-line-derived subcutaneous tumor model in preclinical cancer research

Tumor-bearing experimental animals are essential for preclinical cancer drug development. A broad range of tumor models is available, with the simplest and most widely used involving a tumor of mouse or human origin growing beneath the skin of a mouse: the subcutaneous tumor model. Here, we outline the different types of in vivo tumor model, including some of their advantages and disadvantages and how they fit into the drug-development process. We then describe in more detail the subcutaneous tumor model and key steps needed to establish it in the laboratory, namely: choosing the mouse strain and tumor cells; cell culture, preparation and injection of tumor cells; determining tumor volume; mouse welfare; and an appropriate experimental end point. The protocol leads to subcutaneous tumor growth usually within 1-3 weeks of cell injection and is suitable for those with experience in tissue culture and mouse experimentation.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( $p<0.05$ ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

The Prognostic Value of the Circulating Tumor Cell-Based Four mRNA Scoring System: A New Non-Invasive Setting for the Management of Bladder Cancer

Bladder cancer (BC) is one of the most expensive lifetime cancers to treat because of the high recurrence rate, repeated surgeries, and long-term cystoscopy monitoring and treatment. The lack of an accurate classification system predicting the risk of recurrence or progression leads to the search for new biomarkers and strategies. Our pilot study aimed to identify a prognostic gene signature in circulating tumor cells (CTCs) isolated by ScreenCell devices from muscle invasive and non-muscle invasive BC patients. Through the PubMed database and Cancer Genome Atlas dataset, a panel of 15 genes modulated in BC with respect to normal tissues was selected. Their expression was evaluated in CTCs and thanks to the univariate and multivariate Cox regression analysis, EGFR, TRPM4, TWIST1, and ZEB1 were recognized as prognostic biomarkers. Thereafter, by using the risk score model, we demonstrated that this 4-gene signature significantly grouped patients into high- and low-risk in terms of recurrence free survival (HR = 2.704, 95% CI = 1.010−7.313, Log-rank p < 0.050). Overall, we identified a new prognostic signature that directly impacted the prediction of recurrence, improving the choice of the best treatment for BC patients.

Development of a Prognostic Model Based on Pyroptosis-Related Genes in Pancreatic Adenocarcinoma

Background

The importance of pyroptosis in tumorigenesis and cancer progression is becoming increasingly apparent. However, the efficacy of using pyroptosis-related genes (PRGs) in predicting the prognosis of pancreatic adenocarcinoma (PAAD) patients is unknown.

Methods

This investigation used two databases to obtain expression data for PAAD patients. Differentially expressed PRGs (DEPRGs) were identified between PAAD and control samples. Several bioinformatic approaches were used to analyze the biological functions of DEPRGs and to identify prognostic DERPGs. A miRNA-prognostic DEPRG-transcription factor (TF) regulatory network was created via the miRNet online tool. A risk score model was created after each patient's risk score was calculated. The microenvironments of the low- and high-risk groups were assessed using xCell, the expression of immune checkpoints was determined, and gene set variation analysis (GSVA) was performed. Finally, the efficacy of certain potential drugs was predicted using the pRRophetic algorithm, and the results in the high- and low-risk groups were compared.

Results

A total of 13 DEPRGs were identified between PAAD and control samples. Functional enrichment analysis showed that the DEPRGs had a close relationship with inflammation. In univariate and multivariate Cox regression analyses, GSDMC, IRF1, and PLCG1 were identified as prognostic biomarkers in PAAD. The results of the miRNA-prognostic DEPRG-TF regulatory network showed that GSDMC, IRF1, and PLCG1 were regulated by both specific and common miRNAs and TFs. Based on the risk score and other independent prognostic indicators, a nomogram with a good ability to predict the survival of PAAD patients was developed. By evaluating the tumor microenvironment, we observed that the immune and metabolic microenvironments of the two groups were substantially different. In addition, individuals in the low-risk group were more susceptible to axitinib and camptothecin, whereas lapatinib might be preferred for patients in the high-risk group.

Conclusion

Our study revealed the prognostic value of PRGs in PAAD and created a reliable model for predicting the prognosis of PAAD patients. Our findings will benefit the prognostication and treatment of PAAD patients.