GPAA1 promotes gastric cancer progression via upregulation of GPI-anchored protein and enhancement of ERBB signalling pathway

Glycosylphosphatidylinositol anchor biosynthesis pathway-based biomarker identification with machine learning for prognosis and T cell exhaustion status prediction in breast cancer

As the primary component of anti-tumor immunity, T cells are prone to exhaustion and dysfunction in the tumor microenvironment (TME). A thorough understanding of T cell exhaustion (TEX) in the TME is crucial for effectively addressing TEX in clinical settings and promoting the efficacy of immune checkpoint blockade therapies. In eukaryotes, numerous cell surface proteins are tethered to the plasma membrane via Glycosylphosphatidylinositol (GPI) anchors, which play a crucial role in facilitating the proper translocation of membrane proteins. However, the available evidence is insufficient to support any additional functional involvement of GPI anchors. Here, we investigate the signature of GPI-anchor biosynthesis in the TME of breast cancer (BC)patients, particularly its correlation with TEX. GPI-anchor biosynthesis should be considered as a prognostic risk factor for BC. Patients with high GPI-anchor biosynthesis showed more severe TEX. And the levels of GPI-anchor biosynthesis in exhausted CD8 T cells was higher than normal CD8 T cells, which was not observed between malignant epithelial cells and normal mammary epithelial cells. In addition, we also found that GPI -anchor biosynthesis related genes can be used to diagnose TEX status and predict prognosis in BC patients, both the TEX diagnostic model and the prognostic model showed good AUC values. Finally, we confirmed our findings in cells and clinical samples. Knockdown of PIGU gene expression significantly reduced the proliferation rate of MDA-MB-231 and MCF-7 cell lines. Immunofluorescence results from clinical samples showed reduced aggregation of CD8 T cells in tissues with high expression of GPAA1 and PIGU.

Therapeutic and Diagnostic Potential of Folic Acid Receptors and Glycosylphosphatidylinositol (GPI) Transamidase in Prostate Cancer

Due to the proliferation-induced high demand of cancer cells for folic acid (FA), significant overexpression of folate receptors 1 (FR1) is detected in most cancers. To our knowledge, a detailed characterization of FR1 expression and regulation regarding therapeutic and diagnostic feasibilities in prostate cancer (PCa) has not been described. In the present study, cell cultures, as well as tissue sections, were analyzed using Western blot, qRT-PCR and immunofluorescence. In addition, we utilized FA-functionalized lipoplexes to characterize the potential of FR1-targeted delivery into PCa cells. Interestingly, we detected a high level of FR1-mRNA in healthy prostate epithelial cells and healthy prostate tissue. However, we were able to show that PCa cells in vitro and PCa tissue showed a massively enhanced FR1 membrane localization where the receptor can finally gain its function. We were able to link these changes to the overexpression of GPI-transamidase (GPI-T) by image analysis. PCa cells in vitro and PCa tissue show the strongest overexpression of GPI-T and thereby induce FR1 membrane localization. Finally, we utilized FA-functionalized lipoplexes to selectively transfer pDNA into PCa cells and demonstrate the therapeutic potential of FR1. Thus, FR1 represents a very promising candidate for targeted therapeutic transfer pathways in PCa and in combination with GPI-T, may provide predictive imaging in addition to established diagnostics.

Protein lipidation in health and disease: molecular basis, physiological function and pathological implication

Posttranslational modifications increase the complexity and functional diversity of proteins in response to complex external stimuli and internal changes. Among these, protein lipidations which refer to lipid attachment to proteins are prominent, which primarily encompassing five types including S-palmitoylation, N-myristoylation, S-prenylation, glycosylphosphatidylinositol (GPI) anchor and cholesterylation. Lipid attachment to proteins plays an essential role in the regulation of protein trafficking, localisation, stability, conformation, interactions and signal transduction by enhancing hydrophobicity. Accumulating evidence from genetic, structural, and biomedical studies has consistently shown that protein lipidation is pivotal in the regulation of broad physiological functions and is inextricably linked to a variety of diseases. Decades of dedicated research have driven the development of a wide range of drugs targeting protein lipidation, and several agents have been developed and tested in preclinical and clinical studies, some of which, such as asciminib and lonafarnib are FDA-approved for therapeutic use, indicating that targeting protein lipidations represents a promising therapeutic strategy. Here, we comprehensively review the known regulatory enzymes and catalytic mechanisms of various protein lipidation types, outline the impact of protein lipidations on physiology and disease, and highlight potential therapeutic targets and clinical research progress, aiming to provide a comprehensive reference for future protein lipidation research.

Applying Untargeted Lipidomics to Evaluate the Efficacy of Combined Neoadjuvant Chemotherapy and Immunotherapy for Esophageal Squamous Carcinoma Treatment

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignant tumor with a poor prognosis due to insidious symptoms that make early diagnosis difficult. Despite the combination of multiple treatment modalities, the recurrence and mortality rates of ESCC remain high. Neoadjuvant chemotherapy combined with immunotherapy is an emerging treatment modality that improves the prognosis of patients with ESCC. However, owing to the presence of hyperprogression and pseudoprogression, the currently used methods cannot accurately evaluate the efficacy of this therapy in patients, thus creating an evaluation bias and depriving these patients of the opportunity to benefit. We used untargeted lipidomics to identify the differences in lipid composition between cancer specimens and normal tissue specimens in the neoadjuvant chemotherapy combined with the immunotherapy group and the surgery-alone group of esophageal cancer patients and constructed a prediction model based on sphingomyelin 12:1;2O/30:0 and triglyceride (TG) 60:3 | TG 18:0_24:1_18 using a machine learning approach, which helps to better evaluate the neoadjuvant efficacy of combination therapy and better guide the treatment of ESCC.

Structure and Function of the Glycosylphosphatidylinositol Transamidase, a Transmembrane Complex Catalyzing GPI Anchoring of Proteins

Glycosylphosphatidylinositol (GPI) anchoring of proteins is a ubiquitous posttranslational modification in eukaryotic cells. GPI-anchored proteins (GPI-APs) play critical roles in enzymatic, signaling, regulatory, and adhesion processes. Over 20 enzymes are involved in GPI synthesis, attachment to client proteins, and remodeling after attachment. The GPI transamidase (GPI-T), a large complex located in the endoplasmic reticulum membrane, catalyzes the attachment step by replacing a C-terminal signal peptide of proproteins with GPI. In the last three decades, extensive research has been conducted on the mechanism of the transamidation reaction, the components of the GPI-T complex, the role of each subunit, and the substrate specificity. Two recent studies have reported the three-dimensional architecture of GPI-T, which represent the first structures of the pathway. The structures provide detailed mechanisms for assembly that rationalizes previous biochemical results and subunit-dependent stability data. While the structural data confirm the catalytic role of PIGK, which likely uses a caspase-like mechanism to cleave the proproteins, they suggest that unlike previously proposed, GPAA1 is not a catalytic subunit. The structures also reveal a shared cavity for GPI binding. Somewhat unexpectedly, PIGT, a single-pass membrane protein, plays a crucial role in GPI recognition. Consistent with the assembly mechanisms and the active site architecture, most of the disease mutations occur near the active site or the subunit interfaces. Finally, the catalytic dyad is located ~22 Å away from the membrane interface of the GPI-binding site, and this architecture may confer substrate specificity through topological matching between the substrates and the elongated active site. The research conducted thus far sheds light on the intricate processes involved in GPI anchoring and paves the way for further mechanistic studies of GPI-T.

[Glycosylphosphatidylinositol biosynthesis deficiency 15 caused by GPAA1 gene mutation: a rare disease study]

A boy, aged 6 years, attended the hospital due to global developmental delay for 6 years and recurrent fever and convulsions for 5 years. The boy was found to have delayed mental and motor development at the age of 3 months and experienced recurrent fever and convulsions since the age of 1 year, with intermittent canker sores and purulent tonsillitis. During the fever period, blood tests showed elevated white blood cell count, C-reactive protein, and erythrocyte sedimentation rate, which returned to normal after the fever subsides. Electroencephalography showed epilepsy, and genetic testing showed compound heterozygous mutations in the GPAA1 gene. The boy was finally diagnosed with glycosylphosphatidylinositol biosynthesis deficiency 15 (GPIBD15) and periodic fever. The patient did not respond well to antiepileptic treatment, but showed successful fever control with glucocorticoid therapy. This article reports the first case of GPIBD15 caused by GPAA1 gene mutation in China and summarizes the genetic features, clinical features, diagnosis, and treatment of this disease, which provides a reference for the early diagnosis and treatment of GPIBD15.

A Novel Mechanism Underlying the Inhibitory Effects of Trastuzumab on the Growth of HER2-Positive Breast Cancer Cells

To improve the efficacy of trastuzumab, it is essential to understand its mechanism of action. One of the significant issues that makes it difficult to determine the precise mechanism of trastuzumab action is the formation of various HER receptor dimers in HER2-positive breast cancer cells. So far, studies have focused on the role of HER2-HER3 heterodimers, and little is known regarding EGFR-HER2 heterodimers. Here, we study the role of trastuzumab on the cell signaling and cell proliferation mediated by EGFR-HER2 heterodimers in BT474 and SRBR3 cells. EGF stimulates the formation of both EGFR homodimer and EGFR-HER2 heterodimer. Trastuzumab only binds to HER2, not EGFR. Therefore, any effects of trastuzumab on EGF-induced activation of EGFR, HER2, and downstream signaling proteins, as well as cell proliferation, are through its effects on EGFR-HER2 heterodimers. We show that trastuzumab inhibits EGF-induced cell proliferation and cell cycle progression in BT474 and SKBR3 cells. Interestingly trastuzumab strongly inhibits EGF-induced Akt phosphorylation and slightly inhibits EGF-induced Erk activation, in both BT474 and SKBR3 cells. These data suggest the presence of a novel mechanism that allows trastuzumab to inhibit EGR-induced Akt activation and cell proliferation, without blocking EGF-induced EGFR-HER2 heterodimerization and activation. We show that trastuzumab inhibits EGF-induced lipid raft localization of the EGFR-HER2 heterodimer. Disruption of the lipid raft with MβCD blocks HER2-mediated AKT activation in a similar way to trastuzumab. MβCD and trastuzumab synergically inhibit AKT activation. We conclude that trastuzumab inhibits EGF-induced lipid raft localization of EGFR-HER2 heterodimer, which leads to the inhibition of Akt phosphorylation and cell proliferation, without blocking the formation and phosphorylation of the EGFR-HER2 heterodimer.

FMR1 promotes the progression of colorectal cancer cell by stabilizing EGFR mRNA in an m6A-dependent manner

FMR1, a new m⁶A reader, is known to be involved in the regulation of cancer progression. However, its role, regulatory mechanism, and clinical significance in colorectal cancer (CRC) are elusive. Here, we showed that FMR1 was upregulated in CRC, and it promoted proliferation and metastasis of CRC cells in vitro and in vivo. Mechanically, FMR1 recognized the m⁶A-modification site in EGFR mRNA, a key molecule in cancer occurrence and targeted therapy, sustained its stability and maintained its expression in an m⁶A-dependent manner, thereby promoting the tumorigenesis and metastasis of CRC. And the effect of FMR1 knockdown in CRC cells could be abolished by METTL3. Furthermore, FMR1 shRNA plasmid carried by attenuated Salmonella has an effective anti-tumor effect in vivo. Collectively, we identified the METTL3/FMR1/EGFR axis in the progression of CRC. This novel mechanism indicated that the METTL3/FMR1/EGFR axis is a potential target for early therapeutic intervention in CRC progression.

A novel gene signature derived from the CXC subfamily of chemokine receptors predicts the prognosis and immune infiltration of patients with lung adenocarcinoma

The highly malignant nature of lung adenocarcinoma (LUAD) makes its early diagnosis and prognostic assessment particularly important. However, whether the CXC subfamily of chemokine receptors (CXCR) is involved in the development and prognosis of LUAD remains unclear. Here, differentially expressed genes (DEGs) associated with overall survival (OS) were selected from the cancer genome atlas (TCGA) dataset using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Then, a prognostic gene signature was constructed, which was evaluated using Kaplan-Meier curves, receiver operating characteristics curves, nomogram curves, and an external gene expression omnibus (GEO) dataset. Finally, we verified the functions of the genes comprising the signature using the gene expression profiling interactive analysis (GEPIA) and the immune system interaction database (TISIDB) web portals. We constructed a 7-gene signature (SHC1, PRKCD, VEGFC, RPS6KA1, CAT, CDC25C, and GPI) that stratified patients into high- and low-risk categories. Notably, the risk score of the signature was a separate and effective predictor for OS (P < .001). Patients in the low-risk category had a better prognosis than those in the high-risk category. The receiver operating characteristics and nomogram curves verified the predictive power of the signature. Moreover, in both categories, biological processes and pathways associated with cell migration were enriched. Immune infiltration statuses differed between the 2 risk categories. Critically, the results from the GEPIA and TISIDB web portals indicated that the expression of the 7-gene signature was associated with survival, clinical stage, and immune subtypes of LUAD patients. We identified a CXCR-related gene signature that could assess prognosis and provide a reference for the diagnosis and treatment of LUAD.

Prognostic Signature and Therapeutic Value Based on Membrane Lipid Biosynthesis-Related Genes in Breast Cancer

There is a need to improve diagnostic and therapeutic approaches to enhance the prognosis of breast cancer, the most common malignancy worldwide. Membrane lipid biosynthesis is a hot biological pathway in current cancer research. It is unclear whether membrane lipid biosynthesis is involved in the prognosis of BRCA. With LASSO regression, a 14-gene prediction model was constructed using data from the TCGA-BRCA cohort. The prediction model includes GPAA1, PIGF, ST3GAL1, ST6GALNAC4, PLPP2, ELOVL1, HACD1, SGPP1, PRKD2, VAPB, CERS2, SGMS2, ALDH3B2, and HACD3. BRCA patients from the TCGA-BRCA cohort were divided into two risk subgroups based on the model. Kaplan–Meier survival curves showed that patients with lower risk scores had significantly improved overall survival (P=2.49e − 09). In addition, risk score, age, stage, and TNM classification were used to predict mortality in BRCA patients. In addition, the 14 genes in the risk model were analyzed for gene variation, methylation level, drug sensitivity, and immune cell infiltration, and the miRNA-mRNA network was constructed. Afterward, the THPA website then analyzed the protein expression of 14 of these risk model genes in normal and pathological BRCA tissues. In conclusion, the membrane lipid biosynthesis-related risk model and nomogram can be used to predict BRCA clinical prognosis.

Integrated multi-omics analysis identifies ENY2 as a predictor of recurrence and a regulator of telomere maintenance in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has a high recurrence rate. Accurate prediction of recurrence risk is urgently required for tailoring personalized treatment programs for individual HCC patients in advance. In this study, we analyzed a gene expression dataset from an HCC cohort with 247 samples and identified five genes including ENY2, GPAA1, NDUFA4L2, NEDD9, and NRP1 as the variables for the prediction of HCC recurrence, especially the early recurrence. The Cox model and risks score were validated in two public HCC cohorts (GSE76427 and The Cancer Genome Atlas (TCGA)) and one cohort from Huashan Hospital, which included a total of 641 samples. Moreover, the multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor in the prediction of HCC recurrence. In addition, we found that ENY2, GPAA1, and NDUFA4L2 were significantly upregulated in HCC of the two validation cohorts, and ENY2 had significantly higher expression levels than another four genes in malignant cells, suggesting that ENY2 might play key roles in malignant cells. The cell line analysis revealed that ENY2 could promote cell cycle progression, cell proliferation, migration, and invasion. The functional analysis of the genes correlated with ENY2 revealed that ENY2 might be involved in telomere maintenance, one of the fundamental hallmarks of cancer. In conclusion, our data indicate that ENY2 may regulate the malignant phenotypes of HCC via activating telomere maintenance.

Proteomic-Based Machine Learning Analysis Reveals PYGB as a Novel Immunohistochemical Biomarker to Distinguish Inverted Urothelial Papilloma From Low-Grade Papillary Urothelial Carcinoma With Inverted Growth

Background

The molecular biology of inverted urothelial papilloma (IUP) as a precursor disease of urothelial carcinoma is poorly understood. Furthermore, the overlapping histology between IUP and papillary urothelial carcinoma (PUC) with inverted growth is a diagnostic pitfall leading to frequent misdiagnoses.

Methods

To identify the oncologic significance of IUP and discover a novel biomarker for its diagnosis, we employed mass spectrometry-based proteomic analysis of IUP, PUC, and normal urothelium (NU). Machine learning analysis shortlisted candidate proteins, while subsequent immunohistochemical validation was performed in an independent sample cohort.

Results

From the overall proteomic landscape, we found divergent ‘NU-like’ (low-risk) and ‘PUC-like’ (high-risk) signatures in IUP. The latter were characterized by altered metabolism, biosynthesis, and cell–cell interaction functions, indicating oncologic significance. Further machine learning-based analysis revealed SERPINH1, PKP2, and PYGB as potential diagnostic biomarkers discriminating IUP from PUC. The immunohistochemical validation confirmed PYGB as a specific biomarker to distinguish between IUP and PUC with inverted growth.

Conclusion

In conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Glucose metabolic reprogramming and immune imbalance play important roles in the progression of cancers. The purpose of this study is to develop a glycolysis-related prognostic signature for endometrial cancer (EC) and analyze its relationship with immune function. The mRNA expression profiling of the glycolysis-related genes and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA). We identified a glycolysis-related gene prognostic signature for predicting the prognosis of EC by using The Least Absolute Shrinkage and Selection Operator (LASSO) regression and found the patients in the high-risk group had worse survival prognosis. Multivariate Cox regression analysis showed that the gene signature was an independent prognostic factor for EC. The ROC curve confirmed the accuracy of the prognostic signature (AUC = 0.730). Then, we constructed a nomogram to predict the 1–5 years survival rate of EC patients. The association between the gene signature and immune function was analyzed based on the “ESTIMATE” and “CIBERSORT” algorithm, which showed the immune and ESTIMATE scores of patients in the high-risk group were lower, while the low immune and ESTIMATE scores were associated with a worse prognosis of patients. The imbalance of immune cells was also found in the high-risk group. Further, the protein of CDK1, a gene in the signature, was found to be closely related to prognosis of EC and inhibition of CDK1 could inhibit migration and promote apoptosis of EC cells. This study reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC.

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.

Polymorphisms in Neuronal Growth Regulator 1 and Otoancorin Alternate the Susceptibility to Lung Cancer in Chinese Nonsmoking Females

Cell adhesion molecules (CAMs) play crucial roles in the genesis and progress of tumor. We investigated the effects of single nucleotide polymorphisms (SNPs) of CAMs, neuronal growth regulator 1 (NEGR1), and Otoancorin (OTOA) on lung cancer susceptibility in Chinese nonsmoking females. Logistic regression and Cox regression analyses were conducted to investigate the effects of SNPs and environmental factors. For rs3102911, genotype TT carriers decreased the risk of lung cancer with an odds ratio (OR) of 0.635. AA genotypes of rs741718 increased the risk of lung cancer with an OR of 3.527. In stratified analysis, genotype AA carriers of rs741718 had a high susceptibility to lung adenocarcinoma compared with GG and AG genotypes. Analyses of association between SNPs and clinical characteristics revealed that rs3102911 as a protective factor and rs741718 as a risk factor influenced the lung cancer occurrence and progression in nonsmoking females.

Identification and Validation of Prognostically Relevant Gene Signature in Melanoma

Background

Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis.

Method

The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis.

Results

We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients.

Conclusions

We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.