GIMAP7 as a Potential Predictive Marker for Pan-Cancer Prognosis and Immunotherapy Efficacy

Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes

BACKGROUND

Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction.

METHODS

The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes.

RESULTS

Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer.

CONCLUSION

The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Identification and validation of GIMAP family genes as immune-related prognostic biomarkers in lung adenocarcinoma

Background

The GIMAP family genes play a key role in immune function. Increasing evidence suggests that GIMAP genes were implicated in the tumorigenesis of lung adenocarcinoma (LUAD). This study aimed to investigate the clinical significance of GIMAP family genes in LUAD.

Methods

In this study, we explored the expression, mutation, prognostic value of GIMAP family genes and the correlation with immune microenvironment in LUAD. We further investigated the relationship between GIMAP family genes expression and immunotherapy response in GEO LUAD and melanoma cohorts.

Results

Among the GIMAP family genes, the expression levels of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly lower in LUAD tumor tissues than normal tissues. Most GIMAP genes were closely related to age, tumor grade and T stage, but not significantly related to sex, N stage and M stage. In the overall population, patients with high expression of GIMAP family genes had a significant longer overall survival (OS). GO and KEGG enrichment analysis showed that GIMAP family genes were highly enriched in immune-related biological process. The expression of GIMAP family genes was positively correlated with immune cell infiltration and immune checkpoint molecules. Furthermore, high expression of GIMAP family genes were correlated with therapeutic response to immunotherapy in LUAD and melanoma patients.

Conclusion

In this study, we identified that GIMAP family genes were significantly associated with immune cell infiltration and immune checkpoint molecules. They potentially play a critical role in anti-tumor immunity and serve as immunotherapy biomarkers.

U-shaped relationship found between fibrinogen-to-albumin ratio and systemic inflammation response index in osteoporotic fracture patients

The relationship between the Systemic Inflammatory Response Index (SIRI) and the Fibrinogen-to-albumin ratio (FAR) has not been extensively investigated. The objective of this study was to determine the independent relationship between FAR and SIRI in people with osteoporotic fractures (OPF). A cross-sectional study was conducted using retrospective data from 3431 hospitalized OPF patients. The exposure variable in this study was the baseline FAR, while the outcome variable was the SIRI. Covariates, including age, gender, BMI, and other clinical and laboratory factors, were adjusted. Cross-correlation analysis and linear regression models were applied. The generalized additive model (GAM) investigated non-linear relationships. Adjusted analysis revealed an independent negative association between FAR and SIRI in OPF patients (β = - 0.114, p = 0.00064, 95% CI - 0.180, - 0.049). A substantial U-shaped association between FAR and SIRI was shown using GAM analysis (p < 0.001). FAR and SIRI indicated a negative association for FAR below 6.344% and a positive correlation for FAR over 6.344%. The results of our study revealed a U-shaped relationship between SIRI and FAR. The lowest conceivable FAR for a bone-loose inflammatory disease might be 6.344%, suggesting that this has particular significance for the medical diagnosis and therapy of persons with OPF. Consequently, the term "inflammatory trough" is proposed. These results offer fresh perspectives on controlling inflammation in individuals with OPF and preventing inflammatory osteoporosis.

Prediction and verification of the prognostic biomarker SLC2A2 and its association with immune infiltration in gastric cancer

Gastric cancer (GC) is the fifth most common cause of cancer-associated deaths; however, its treatment options are limited. Despite clinical improvements, chemotherapy resistance and metastasis are major challenges in improving the prognosis and quality of life of patients with GC. Therefore, effective prognostic biomarkers and targets associated with immunological interventions need to be identified. Solute carrier family 2 member 2 (SLC2A2) may serve a role in tumor development and invasion. The present study aimed to evaluate SLC2A2 as a prospective prognostic marker and chemotherapeutic target for GC. SLC2A2 expression in several types of cancer and GC was analyzed using online databases, and the effects of SLC2A2 expression on survival prognosis in GC were investigated. Clinicopathological parameters were examined to explore the association between SLC2A2 expression and overall survival (OS). Associations between SLC2A2 expression and immune infiltration, immune checkpoints and IC50 were estimated using quantification of the tumor immune contexture from human RNA-seq data, the Tumor Immune Estimation Resource 2.0 database and the Genomics of Drug Sensitivity in Cancer database. Differential SLC2A2 expression and the predictive value were validated using the Human Protein Atlas, Gene Expression Omnibus, immunohistochemistry and reverse transcription-quantitative PCR. SLC2A2 expression was downregulated in most types of tumor but upregulated in GC. Functional enrichment analysis revealed an association between SLC2A2 expression and lipid metabolism and the tumor immune microenvironment. According to Gene Ontology term functional enrichment analysis, SLC2A2-related differentially expressed genes were enriched predominantly in 'chylomicron assembly', 'plasma lipoprotein particle assembly', 'high-density lipoprotein particle', 'chylomicron', 'triglyceride-rich plasma lipoprotein particle', 'very-low-density lipoprotein particle'. 'intermembrane lipid transfer activity', 'lipoprotein particle receptor binding', 'cholesterol transporter activity' and 'intermembrane cholesterol transfer activity'. In addition, 'cholesterol metabolism', and 'fat digestion and absorption' were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Patients with GC with high SLC2A2 expression had higher levels of neutrophil and M2 macrophage infiltration and a significant inverse correlation was observed between SLC2A2 expression and MYC targets, tumor mutation burden, microsatellite instability and immune checkpoints. Furthermore, patients with high SLC2A2 expression had worse prognosis, including OS, disease-specific survival and progression-free interval. Multivariate regression analysis demonstrated that SLC2A2 could independently prognosticate GC and the nomogram model showed favorable performance for survival prediction. SLC2A2 may be a prospective prognostic marker for GC. The prediction model may improve the prognosis of patients with GC in clinical practice, and SLC2A2 may serve as a novel therapeutic target to provide immunotherapy plans for GC.

GIMAP7 inhibits epithelial-mesenchymal transition and glycolysis in lung adenocarcinoma cells via regulating the Smo/AMPK signaling pathway

BACKGROUND

GTPase immunity-associated protein 7 (GIMAP7) has been previously recognized as a prognostic marker in pan-cancer. Our objective was to explore the function of GIMAP7 in the progression of lung adenocarcinoma (LUAD).

METHODS

GIMAP7 was overexpressed by transfection with GIMAP7 plasmid, and knocked down using siRNAs. The biological functions of GIMAP7 were examined by employing CCK-8, EdU, colony formation, flow cytometry, wound healing, and transwell assays. The effects of GIMAP7 on the extracellular acidification rate (ECAR), oxygen consumption rate (OCR), lactate production, and glucose uptake were evaluated. In addition, the related mRNA and protein expression was detected employing immunohistochemical, western blot, and qRT-PCR. A xenograft tumor model was established in nude mice to evaluate the effects of GIMAP7 on tumor growth.

RESULTS

GIMAP7 was lowly expressed in LUAD tissues and cells. GIMAP7 inhibited the proliferation, mobility, EMT, glycolysis, but promoted apoptosis in LUAD cells. Moreover, we also confirmed that GIMAP7 suppressed Smo/AMPK signaling in LUAD cells. By adding the Smo agonist SAG and AMPK agonist GSK621, the results of rescue experiments further verified that GIMAP7 played a role in LUAD inhibition through inhibition of the Smo/AMPK signaling pathway. Furthermore, the role of GIMAP7 in inhibiting tumor growth was verified in vivo.

CONCLUSIONS

These results demonstrate that GIMAP7 could inhibit cell proliferation, mobility and glycolysis, but accelerate apoptosis via repressing the Smo/AMPK signaling pathway in LUAD.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

GIMAP7 induces oxidative stress and apoptosis of ovarian granulosa cells in polycystic ovary syndrome by inhibiting sonic hedgehog signalling pathway

Polycystic ovary syndrome (PCOS) is a gynaecological endocrine disease. The objective of the present study was to investigate the role of GTPase immunity-associated protein (GIMAP) 7 in PCOS. A PCOS rat model was established using dehydroepiandrosterone injection. The data showed that GIMAP7 was mainly located in granulosa cells and was abundantly expressed in the ovarian granulosa cells of PCOS rats. GIMAP7 silencing decreased blood glucose levels, HOMA-IR scores, and number of cystic follicles. In addition, GIMAP7 silencing corrected erratic oestrous cycles, inhibited apoptosis and reduced c-caspase-3 protein expression in the ovarian tissues of PCOS rats. GIMAP7 silencing reduced malondialdehyde (MDA) but increased glutathione (GSH) and superoxide dismutase (SOD) levels in the serum and ovarian tissues of PCOS rats. The effects of GIMAP7 were further investigated in human ovarian granulosa KGN cells. GIMAP7 silencing increased the viability, promoted proliferation, and increased the percentage of S-phase KGN cells. The apoptosis rate was significantly decreased by GIMAP7 silencing. GIMAP7 also inhibited oxidative stress in KGN cells, resulting in decreased levels of reactive oxygen species (ROS) and MDA and increased levels of GSH and SOD. Notably, GIMAP7 inhibited the sonic hedgehog (SHH) signalling pathway, and GIMAP7 silencing increased the expression of the SHH signalling pathway downstream genes SHH, SMO, and Gli1. Inhibition of the SHH signalling pathway using cyclopamine reduced the effect of GIMAP7 silencing on KGN cells. This study proved that GIMAP7 promotes oxidative stress and apoptosis in ovarian granulosa cells in PCOS by inhibiting the SHH signalling pathway.

scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data

Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.

Role of SERCA3 in the Prognosis and Immune Function in Pan-Cancer

The sarcoendoplasmic reticulum calcium adenosine triphosphatase (ATPase) 3 (SERCA3), a member of the SERCA protein family, is located at the endoplasmic reticulum. Its main function is to pump Ca2+ into the endoplasmic reticulum and is involved in maintaining intracellular calcium homeostasis and signal transduction, which are very important factors impacting cancer development and progression. However, the specific role of SERCA3 in cancer remains unclear. Our study, for the first time, comprehensively analyzed the SERCA3 expression profile in multiple cancers and its prognostic value in different cancers using bioinformatics. Furthermore, TCGA database was applied to evaluate the certain correlation of SERCA3 expression with immune modulator genes, immune checkpoints, immune cell infiltration, TMB, and MSI. The results revealed that in many cancers, SERCA3 expression was markedly decreased, which was related to poor prognosis. Additionally, we noticed that SERCA3 expression was correlated with TNM classification and WHO cancer stages in some cancer types. The Pearson correlation analysis showed that SERCA3 expression was closely associated with chemokines, chemokine receptors, MHC, immune activation genes, and immunosuppressive genes. In most cancer types, SERCA3 expression was also associated with immune checkpoints, including PDCD1 and CTLA-4. Further analysis suggested that SERCA3 was significantly correlated with CD8+ T cells, and regulatory T cells. Additionally, pan-cancer analysis confirmed that SERCA3 expression was related to TMB and MSI. In conclusion, these results offer a new insight into the functions and effects of SERCA3 in pan-cancer, and further provide some basis for considering SERCA3 as a potential cancer treatment target and biomarker.

Pan-cancer analyses confirmed the cuproptosis-related gene FDX1 as an immunotherapy predictor and prognostic biomarker

Background: The latest research identified cuproptosis as an entirely new mechanism of cell death. However, as a key regulator in copper-induced cell death, the prognostic and immunotherapeutic value of FDX1 in pan-cancer remains unclear.

Methods: Data from the UCSC Xena, GEPIA, and CPTAC were analyzed to conduct an inquiry into the overall differential expression of FDX1 across multiple cancer types. The expression of FDX1 in GBM, LUAD and HCC cell lines as well as their control cell lines was verified by RT-QPCR. The survival prognosis, clinical features, and genetic changes of FDX1 were also evaluated. Finally, the relationship between FDX1 and immunotherapy response was further explored through Gene Set Enrichment Analysis enrichment analysis, tumor microenvironment, immune cell infiltration, immune gene co-expression and drug sensitivity analysis.

Results: The transcription and protein expression of FDX1 were significantly reduced in most cancer types and had prognostic value for the survival of certain cancer patients such as ACC, KIRC, HNSC, THCA and LGG. In some cancer types, FDX1 expression was also markedly correlated with the clinical characteristics, TMB, MSI, and antitumor drug susceptibility or resistance of different tumors. Gene set enrichment analysis showed that FDX1 was significantly associated with immune-related pathways. Moreover, the expression level of FDX1 was confirmed to be strongly correlated with immune cell infiltration, immune checkpoint genes, and immune regulatory genes to a certain extent.

Conclusion: This study comprehensively explored the potential value of FDX1 as a prognostic and immunotherapeutic marker for pan-cancer, providing new direction and evidence for cancer therapy.

Clinical Data and Biocalculation Methods of GABRD Determine the Clinical Characteristics and Immune Relevance of Colorectal Cancer

Background

The aim of this study was to clarify the expression of gamma-aminobutyric acid type A receptor delta subunit (GABRD) gene in pan-cancer and its correlation with patient prognosis, and to investigate the function and possible mechanism of GABRD in colorectal cancer (CRC).

Methods

The Cancer Genome Atlas (TCGA) data were used to analyze the expression differences of GABRD in pan-cancer, and the correlation between GABRD and clinical prognosis of various tumors was analyzed by Cox regression method. According to the expression level of GABRD, Gene Function Annotation (GO) and Kyoto Encyclopedia of Genomes (KEGG) functional enrichment analysis were performed on the differentially expressed genes. Expression of GABRD gene and 44 marker genes of three types of RNA modification (m1A (10), m5C (13), m6A (21)) genes in different tumors was observed. Pearson correlation of GABRD gene and marker genes of five immune pathways was measured.

Results

: TCGA data analysis showed that GABRD was significantly upregulated in various tumor tissues, especially COAD and READCOAD. Survival analysis showed that GABRD was a prognostic protective factor in CRC (p < 0.001). The results of survival nomogram showed that GABRD, age, and tumor (T) lymph node (N) distant metastasis (M) stage were independent prognostic factors, and the survival model C-index was 0.724 (0.644-1). Gene enrichment and functional analysis showed that GABRD may be related to protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response. The expression of GABRD was positively correlated in m1A-, m5C-, and m6A-related genes. The GABRD gene was found in B cell, T cell CD4, T cell CD8, neutrophil, macrophage in TCGA-COAD (N = 282), and TCGA-COADREAD (N = 373). The infiltration level and DC was significantly positively correlated (p < 0.05). Also, the Pearson correlation coefficient is the largest.

Conclusion

The involvement of GABRD in the occurrence and development of CRC may be related to protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response. GABRD can be used as a molecular marker for the prognosis of CRC.