Neurexophilin and PC-esterase domain family member 4 (NXPE4) and prostate androgen-regulated mucin-like protein 1 (PARM1) as prognostic biomarkers for colorectal cancer

Esterase-responsive nanoparticles (ERN): A targeted approach for drug/gene delivery exploits

Nanoparticles are being developed to enhance drug delivery to cancer tumors, leveraging advantages such as the enhanced permeability and retention (EPR) effect. However, traditional nanoparticles often face challenges with low specificity for cancer cells, leading to inefficient delivery and unwanted side effects. Esterase-responsive nanoparticles offer a maximum targeted approach to tumor cells because they release their therapeutic payload at the tumor site under the influence of esterase activity. This review explores the role of esterase-responsive nanoparticles in drug and gene delivery, examines esterase prodrug therapy, and discusses prostate-specific membrane antigen (PSMA) targets esterase-responsive nanoparticles in prostate cancer treatment. Additionally, we reviewed the current research progress and future potential of esterase-responsive nanoparticles in enhancing drug and gene delivery.

Molecular Mechanisms of IL18 in Disease

Interleukin 18 (IL18) was originally identified as an inflammation-induced cytokine that is secreted by immune cells. An increasing number of studies have focused on its non-immunological functions, with demonstrated functions for IL18 in energy homeostasis and neural stability. IL18 is reportedly required for lipid metabolism in the liver and brown adipose tissue. Furthermore, IL18 (Il18) deficiency in mice leads to mitochondrial dysfunction in hippocampal cells, resulting in depressive-like symptoms and cognitive impairment. Microarray analyses of Il18-/- mice have revealed a set of genes with differential expression in liver, brown adipose tissue, and brain; however, the impact of IL18 deficiency in these tissues remains uncertain. In this review article, we discuss these genes, with a focus on their relationships with the phenotypic disease traits of Il18-/- mice.

Neurexophilin 4 is a prognostic biomarker correlated with immune infiltration in bladder cancer

Recent studies have shown that NXPH family member 4 (NXPH4) plays an important role in the progression of cancer. However, the potential role of NXPH4 in bladder cancer (BCa) remains to be explored. The purpose of the present study was to identify whether NXPH4 could be used as a biomarker to predict the prognosis of BCa. We first examined the expression of NXPH4 in pan-cancer, and then focused on BCa. Univariate and multivariate Cox regression analysis were used to investigate whether NXPH4 could be used as an independent prognostic indicator. Gene set enrichment analysis (GSEA) was used for functional analysis of NXPH4-related genes. CIBERSORT algorithm was used to calculate immune cell infiltration levels with different NXPH4 expression. Finally, the expression of NXPH4 was validated in clinical tissue specimens and bladder cancer cell lines by immunohistochemistry and qRT-PCR. The tumor-promoting effects of NXPH4 were further investigated using counting kit-8 (CCK-8), colony formation, EdU assays, and tumor xenograft model. Our results showed that NXPH4 was highly expressed in BCa tissues. Patients in the high NXPH4 expression group had shorter overall survival (OS) and progression-free survival (PFS). We found that immune-related pathways were enriched in NXPH4-related genes. Immune cell infiltrations in BCa were also associated with different NXPH4 expression. NXPH4 was further found to be highly expressed in our validation specimens. The proliferative effect of NXPH4 was confirmed in BCa in vivo and in vitro. Overall, NXPH4 is a biomarker for predicting BCa prognosis and associated with immune infiltration.Abbreviations: NXPH4: Neurexophilin 4; BCa: Bladder cancer; TCGA-BLCA: The Cancer Genome Atlas Urothelial Bladder Carcinoma; shRNA: short hairpin RNA; NC: Negative control; OS: Overall survival; PFS: Progression-free survival; TME: Tumor microenvironment; IPS: immunophenoscore; ICIs: Immune checkpoint inhibitors; DEGs: Differential expression genes.

Identification of Candidate Genes Regulating Carcass Depth and Hind Leg Circumference in Simmental Beef Cattle Using Illumina Bovine Beadchip and Next-Generation Sequencing Analyses

Genome-wide association studies are a robust means of identifying candidate genes that regulate economically important traits in farm animals. The aim of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes potentially related to carcass depth and hind leg circumference in Simmental beef cattle. We performed Illumina Bovine HD Beadchip (~670 k SNPs) and next-generation sequencing (~12 million imputed SNPs) analyses of data from 1252 beef cattle, to which we applied a linear mixed model. Using a statistical threshold (p = 0.05/number of SNPs identified) and adopting a false discovery rate (FDR), we identified many putative SNPs on different bovine chromosomes. We identified 12 candidate genes potentially annotated with the markers identified, including CDKAL1 and E2F3, related to myogenesis and skeletal muscle development. The identification of such genes in Simmental beef cattle will help breeders to understand and improve related traits, such as meat yield.

The bioinformatic approach identifies PARM1 as a new potential prognostic factor in osteosarcoma

Objective

To explore the key factors affecting the prognosis of osteosarcoma patients.

Methods

Based on the GEO dataset and differential expression analysis of normal and osteosarcoma tissues, the gene modules related to the prognosis of osteosarcoma patients were screened by WGCNA, and intersecting genes were taken with differential genes, and the risk prognosis model of osteosarcoma patients was constructed by LASSO regression analysis of intersecting genes, and the prognosis-related factors of osteosarcoma patients were obtained by survival analysis, followed by target for validation, and finally, the expression of prognostic factors and their effects on osteosarcoma cell migration were verified by cellular assays and lentiviral transfection experiments.

Results

The prognosis-related gene module of osteosarcoma patients were intersected with differential genes to obtain a total of 9 common genes. PARM1 was found to be a prognostic factor in osteosarcoma patients by LASSO regression analysis, followed by cellular assays to verify that PARM1 was lowly expressed in osteosarcoma cells and that overexpression of PARM1 in osteosarcoma cells inhibited cell migration. Pan-cancer analysis showed that PARM1 was lowly expressed in most cancers and that low expression of PARM1 predicted poor prognosis for patients.

Conclusion

The data from this study suggest that PARM1 is closely associated with the prognosis of osteosarcoma patients, and PARM1 may serve as a novel potential prognostic target for osteosarcoma, providing a heartfelt direction for the prevention and treatment of osteosarcoma.

Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.

Identification of transcriptome alterations in the prefrontal cortex, hippocampus, amygdala and hippocampus of suicide victims

Suicide is one of the leading causes of death globally for all ages, and as such presents a very serious problem for clinicians worldwide. However, the underlying neurobiological pathology remains to a large extent unknown. In order to address this gap, we have carried out a genome-wide investigation of the gene expression in the amygdala, hippocampus, prefrontal cortex and thalamus in post-mortem brain samples obtained from 20 suicide completers and 7 control subjects. By KEGG enrichment analysis indicated we identified novel clusters of downregulated pathways involved in antigen neutralization and autoimmune thyroid disease (amygdala, thalamus), decreased axonal plasticity in the hippocampus. Two upregulated pathways were involved in neuronal death in the hippocampus and olfactory transduction in the thalamus and the prefrontal cortex. Autoimmune thyroid disease pathway was downregulated only in females. Metabolic pathways involved in Notch signaling amino acid metabolism and unsaturated lipid synthesis were thalamus-specific. Suicide-associated changes in the expression of several genes and pseudogenes that point to various functional mechanisms possibly implicated in the pathology of suicide. Two genes (SNORA13 and RNU4-2) involved in RNA processing were common to all brain regions analyzed. Most of the identified gene expression changes were related to region-specific dysregulated manifestation of genetic and epigenetic mechanisms underlying neurodevelopmental disorders (SNORD114-10, SUSd1), motivation, addiction and motor disorders (CHRNA6), long-term depression (RAB3B), stress response, major depression and schizophrenia (GFAP), signal transduction at the neurovascular unit (NEXN) and inhibitory neurotransmission in spatial learning, neural plasticity (CALB2; CLIC6, ENPP1). Some of the differentially expressed genes were brain specific non-coding RNAs involved in the regulation of translation (SNORA13). One, (PARM1) is a potential oncogene and prognostic biomarker for colorectal cancer with no known function in the brain. Disturbed gene expression involved in antigen neutralization, autoimmunity, neural plasticity, stress response, signal transduction at the neurovascular unit, dysregulated nuclear RNA processing and translation and epigenetic imprinting signatures is associated with suicide and point to regulatory non-coding RNAs as potential targets of new drugs development.

Prognostic significance of esterase gene expression in multiple myeloma

BACKGROUND

Esterase enzymes differ in substrate specificity and biological function and may display dysregulated expression in cancer. This study evaluated the biological significance of esterase expression in multiple myeloma (MM).

METHODS

For gene expression profiling and evaluation of genomic variants in the Institute for Molecular Medicine Finland (FIMM) cohort, bone marrow aspirates were obtained from patients with newly diagnosed MM (NDMM) or relapsed/refractory MM (RRMM). CD138+ plasma cells were enriched and used for RNA sequencing and analysis, and to evaluate genomic variation. The Multiple Myeloma Research Foundation (MMRF) Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile (CoMMpass) dataset was used for validation of the findings from FIMM.

RESULTS

MM patients (NDMM, n = 56; RRMM, n = 78) provided 171 bone marrow aspirates (NDMM, n = 56; RRMM, n = 115). Specific esterases exhibited relatively high or low expression in MM, and expression of specific esterases (UCHL5, SIAE, ESD, PAFAH1B3, PNPLA4 and PON1) was significantly altered on progression from NDMM to RRMM. High expression of OVCA2, PAFAH1B3, SIAE and USP4, and low expression of PCED1B, were identified as poor prognostic markers (P < 0.05). The MMRF CoMMpass dataset provided validation that higher expression of PAFAH1B3 and SIAE, and lower expression of PCED1B, were associated with poor prognosis.

CONCLUSIONS

Esterase gene expression levels change as patients progress from NDMM to RRMM. High expression of OVCA2, PAFAH1B3, USP4 and SIAE, and low expression of PCED1B, are poor prognostic markers in MM, suggesting a role for these esterases in myeloma biology.

CCDC167 as a potential therapeutic target and regulator of cell cycle-related networks in breast cancer

According to cancer statistics reported in 2020, breast cancer constitutes 30% of new cancer cases diagnosed in American women. Histological markers of breast cancer are expressions of the estrogen receptor (ER), the progesterone receptor (PR), and human epidermal growth factor receptor (HER)-2. Up to 80% of breast cancers are grouped as ER-positive, which implies a crucial role for estrogen in breast cancer development. Therefore, identifying potential therapeutic targets and investigating their downstream pathways and networks are extremely important for drug development in these patients. Through high-throughput technology and bioinformatics screening, we revealed that coiled-coil domain-containing protein 167 (CCDC167) was upregulated in different types of tumors; however, the role of CCDC167 in the development of breast cancer still remains unclear. Integrating many kinds of databases including ONCOMINE, MetaCore, IPA, and Kaplan-Meier Plotter, we found that high expression levels of CCDC167 predicted poor prognoses of breast cancer patients. Knockdown of CCDC167 attenuated aggressive breast cancer growth and proliferation. We also demonstrated that treatment with fluorouracil, carboplatin, paclitaxel, and doxorubicin resulted in decreased expression of CCDC167 and suppressed growth of MCF-7 cells. Collectively, these findings suggest that CCDC167 has high potential as a therapeutic target for breast cancer.

Genetic Regulation of Immunoglobulin G Glycosylation

Defining the genetic components that control glycosylation of the human immunoglobulin G (IgG) is an ongoing effort, which has so far been addressed by means of heritability, linkage and genome-wide association studies (GWAS). Unlike the synthesis of proteins, N-glycosylation biosynthesis is not a template-driven process, but rather a complex process regulated by both genetic and environmental factors. Current heritability studies have shown that while up to 75% of the variation in levels of some IgG glycan traits can be explained by genetics, some glycan traits are completely defined by environmental influences. Advances in both high-throughput genotyping and glycan quantification methods have enabled genome-wide association studies that are increasingly used to estimate associations of millions of single-nucleotide polymorphisms and glycosylation traits. Using this method, 18 genomic regions have so far been robustly associated with IgG N-glycosylation, discovering associations with genes encoding glycosyltransferases, but also transcription factors, co-factors, membrane transporters and other genes with no apparent role in IgG glycosylation. Further computational analyses have shown that IgG glycosylation is likely to be regulated through the expression of glycosyltransferases, but have also for the first time suggested which transcription factors are involved in the process. Moreover, it was also shown that IgG glycosylation and inflammatory diseases share common underlying causal genetic variants, suggesting that studying genetic regulation of IgG glycosylation helps not only to better understand this complex process but can also contribute to understanding why glycans are changed in disease. However, further studies are needed to unravel whether changes in IgG glycosylation are causing these diseases or the changes in the glycome are caused by the disease.

Identification of candidate biomarkers and therapeutic drugs of colorectal cancer by integrated bioinformatics analysis

Most colorectal cancer (CRC) patients are diagnosed with advanced stages and low prognosis. We aimed to identify potential diagnostic and prognostic biomarkers, as well as active small molecules of CRC. Microarray data (GSE9348, GSE35279, and GSE106582) were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by the GEO2R platform. Common DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Cytoscape software was used to construct protein-protein interaction networks and identify hub genes. Hub genes were evaluated by Kaplan-Meier survival analysis in the GEPIA database and validated in two independent microarray data (GSE74602 and GSE83889). Common DEGs were used to select active small molecules by the connectivity map database. A total of 166 DEGs were identified as common DEGs. GO analysis demonstrated that common DEGs were significantly enriched in the apoptotic process, cell proliferation, and cell adhesion. KEGG analysis indicated that the most enriched pathways were the PI3K-Akt signaling pathway and extracellular matrix-receptor interaction. COL1A2, THBS2, TIMP1, and CXCL8 significantly upregulated in colorectal tumor. High expressions of COL1A2, THBS2, and TIMP1 were associated with poor survival, while high expressions of CXCL8 were associated with better survival. We selected 11 small molecules for CRC therapy. In conclusion, we found key dysregulated genes associated with CRC and potential small molecules to reverse them. COL1A2, THBS2, TIMP1, and CXCL8 may act as diagnostic and prognostic biomarkers of CRC.

Comprehensive analysis of the transcriptome-wide m6A methylome in colorectal cancer by MeRIP sequencing

Accumulating evidence has demonstrated that N6-methyladenosine (m6A) plays important roles in various cancers, making it essential to profile m6A modifications at a transcriptome-wide scale in colorectal cancer (CRC). In the present study, we performed high-throughput sequencing to determine the m6A methylome in CRC. We obtained six pairs of CRC samples and tumour-adjacent normal tissues from Peking University People's Hospital. We used MeRIP-seq to determine that compared to the tumour-adjacent normal tissues, the CRC samples had 1343 dysregulated m6A peaks, and 625 m6A peaks were significantly upregulated and 718 m6A peaks were significantly downregulated. Genes with altered m6A peaks play critical roles in regulating glucose metabolism, RNA metabolism, and cancer stem cells. Furthermore, we identified 297 hypermethylated m6A peaks and 328 hypomethylated m6A peaks in mRNAs through conjoint analyses of MeRIP-seq and RNA-seq data. After analysing these genes with differentially methylated m6A peaks and synchronously differential expression, we identified four genes (WDR72, SPTBN2, MORC2, and PARM1) that were associated with prognosis of colorectal cancer patients by searching The Cancer Genome Atlas (TCGA). Our study suggests that m6A modifications play important roles in tumour progression and survival of CRC patients. The results also indicate that modulating m6A modifications may represent an alternative strategy to predict the survival of cancer patients and interfere with tumour progression in the future.

Four targeted genes for predicting the prognosis of colorectal cancer: A bioinformatics analysis case

The molecular mechanisms underlying the development and progression of colorectal cancer (CRC) have not been clarified. The purpose of the present study was to identify key genes that may serve as novel therapeutic targets or prognostic predictors in patients with CRC using bioinformatics analysis. Four gene expression datasets were downloaded from the Gene Expression Omnibus database, which revealed 19 upregulated and 34 downregulated differentially expressed genes (DEGs). The downregulated DEGs were significantly enriched in eight pathways according to Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction network was constructed with 52 DEGs and 458 edges. Ten key genes were identified according to the degree value, betweenness centrality and closeness centrality. Survival analysis revealed that low expression of four of the ten genes, carcinoembryonic antigen related cell adhesion molecule 7 (CEACAM7), solute carrier family 4 member 4 (SLC4A4), glucagon (GCG) and chloride channel accessory 1 (CLCA1) genes, were associated with unfavorable prognosis in CRC. Furthermore, gene set enrichment analysis revealed that two pathways were significantly enriched in the CEACAM7 low-expression group. Thus, CEACAM7, SLC4A4, GCG and CLCA1 may be prognostic markers or therapeutic targets of CRC. Low CEACAM7 expression may be associated with the activation of glycosaminoglycan biosynthesis-chondroitin sulfate and extracellular matrix receptor interaction pathways and may affect the prognosis of CRC.