CD3D Is an Independent Prognostic Factor and Correlates With Immune Infiltration in Gastric Cancer

CD3D silencing alleviates diabetic nephropathy via inhibition of JAK/STAT pathway

Diabetic nephropathy (DN) is a severe microvascular complication of diabetes that poses a significant burden to global health. This investigation aims to illustrate the functional role of CD3D and its relevant mechanisms in DN progression. The pivotal genes between the GSE47183 and GSE30528 datasets were identified using bioinformatics methods. The effects of CD3D silencing on renal damage, inflammatory response, and lipid metabolism were validated in DN mice. Furthermore, the impacts of CD3D knockdown on cell viability, apoptotic rate, inflammation, and lipid levels were investigated in HK-2 cells under high glucose (HG) conditions. Additionally, RO8191 was employed to investigate the role of CD3D in the JAK/STAT pathway in HG-treated cells. A total of 5 focal genes were identified through bioinformatics and were found to be upregulated in renal tissues from DN mice. CD3D silencing mitigated pathological damage to kidneys, reduced inflammatory response, and decreased lipid accumulation in DN mice. HG stimulation restrained viability, increased apoptosis, promoted the release of inflammatory cytokines, and affected expressions of hallmarks related to lipid metabolism in HG-treated cells; these changes were partially abolished by CD3D knockdown. Mechanistically, CD3D downregulation ameliorated HG-induced injury in HK-2 cells by blocking the JAK/STAT pathway. This study underscores that CD3D silencing has significant potential as a promising candidate in the treatment of DN.

Immune Subtypes and Characteristics of Endometrial Cancer Based on Immunogenes

Purpose

The aim of this study was to explore the immune subtypes of endometrial cancer (EC) and its characteristics by immunogenes from the perspective of multidimensional genomics (multi-omics).

Patients and Methods

Immune subtypes were carried out using an unsupervised non-negative matrix factorization clustering (NMF) method and their characteristics were analysed. Key genes were identified using random forest analysis. A predictive model for immune subtypes and their clinical prognosis were constructed. The relationship between immune subtypes and molecular subtypes was investigated.

Results

Two immune subtypes C1 and C2 were available. C2 patients were younger, less graded, had significantly higher immune cell infiltration, immune checkpoint expression, tumor neoantigens, tumor mutation load than C1 (P<005). S100A9, CD3D, CD3E, HLA-DRB1 and IL2RB were the key genes with significant survival outcomes. S100A9 expression was lower in C2 than C1, and IL2RB, HLA-DRB1, CD3E and CD3D expression was higher than C1 (P<0.05). The predictive accuracy of five key genes for immune subtypes was good, with a Receiver operating characteristic of 0.941. The incidence of TP53abn type in C2 was significantly lower than that of C1, and the incidence of POLE type was significantly higher than that of C1 (P<0.0001).

Conclusion

EC can be divided into two immune subtypes based on immunogenes. Low expression of S100A9 and high expression of IL2RB, HLA-DRB1, CD3E, and CD3D suggest sensitivity to immunotherapy and a good prognosis.

Construction of immune-related gene pairs signature to predict the overall survival of multiple myeloma patients based on whole bone marrow gene expression profiling

Multiple myeloma (MM) is a plasma cell dyscrasia that is characterized by the uncontrolled proliferation of malignant PCs in the bone marrow. Due to immunotherapy, attention has returned to the immune system in MM, and it appears necessary to identify biomarkers in this area. In this study, we created a prognostic model for MM using immune-related gene pairs (IRGPs), with the advantage that it is not affected by technical bias. After retrieving microarray data of MM patients, bioinformatics analyses like COX regression and least absolute shrinkage and selection operator (LASSO) were used to construct the signature. Then its prognostic value is assessed via time-dependent receiver operating characteristic (ROC) and the Kaplan-Meier (KM) analysis. We also used XCELL to examine the status of immune cell infiltration among MM patients. 6-IRGP signatures were developed and proved to predict MM prognosis with a P-value of 0.001 in the KM analysis. Moreover, the risk score was significantly associated with clinicopathological characteristics and was an independent prognostic factor. Of note, the combination of age and β2-microglobulin with risk score could improve the accuracy of determining patients' prognosis with the values of the area under the curve (AUC) of 0.73 in 5 years ROC curves. Our model was also associated with the distribution of immune cells. This novel signature, either alone or in combination with age and β2-microglobulin, showed a good prognostic predictive value and might be used to guide the management of MM patients in clinical practice.

Hypomethylation of CD3D promoter induces immune cell infiltration and supports malignant phenotypes in uveal melanoma

Alterations in DNA methylation in malignant diseases have been heralded as promising targets for diagnostic, prognostic, and predictive values. This study was based on epigenetic alterations and immune cell infiltration analysis to investigate the mechanism of CD3D methylation in uveal melanoma (UM). Bioinformatics analysis was performed on transcriptome data, 450 K methylation data, and clinical information of UM patients from the TCGA database. Stromal and immune cell infiltration was evaluated by calculating the StromalScore and ImmuneScore of UM samples. UM samples were divided into high and low StromalScore and ImmuneScore groups, followed by differential and enrichment analyses. PPI network construction and correlation analysis was used to identify the core prognosis-related genes. The bioinformatics analysis results were confirmed in UM cell experiments. StromalScore and ImmuneScore were significantly associated with the prognosis of UM patients. CD3D, IRF1, CCL3, and FN1 were identified as core genes driven by methylation that affected the prognosis of UM patients. CD3D expression showed the highest correlation with its methylation and was closely related to the four key immune cells in UM development. CD3D was hypomethylated and abundantly expressed in UM cells, while silencing of CD3D inhibited the proliferation, migration, and invasion of UM cells in vitro. In summary, this study identifies hypomethylation of CD3D promoter in UM, which was associated with immune cell infiltration of UM.

Construction of the novel immune risk scoring system related to CD8+ T cells in uterine corpus endometrial carcinoma

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy.

METHODS

CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8⁺ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan-Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels.

RESULTS

Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8⁺ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8⁺ T cells.

CONCLUSIONS

In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.

ERCC1 is a potential biomarker for predicting prognosis, immunotherapy, chemotherapy efficacy, and expression validation in HER2 over-expressing breast cancer

Objective

To investigate the relationship between Excision repair cross-complementation 1 (ERCC1) expression, clinicopathological features, and breast cancer prognosis in patients treated with trastuzumab. Further, we aim to explore the immune status of ERCC1 in breast cancer.

Methods

The data were retrieved from publicly available databases like the Cancer Genome Atlas, Therapeutically Applicable Research to Generate Effective Treatments, and the Genotype-Tissue Expression. The data was used to perform differential expression analyses between tumor and normal tissues in pan-cancers, immune-related analysis, homologous recombination deficiency (HRD), tumor mutation burden, and microsatellite instability. A total of 210 patients with HER2 over-expressing breast cancer from the Fourth Hospital of Hebei Medical University between January 2013 to December 2015 were enrolled in the study. Ten adjacent normal tissues were used to study the expression pattern of ERCC1 in normal tissues. Immunohistochemistry was performed to study ERCC1 expression and immune cell infiltration in different status of ERCC1 expression. Further, the correlation between ERCC1 expression, immune cell infiltration clinicopathological features, and the prognosis of patients with breast cancer was analyzed.

Results

The immune analysis revealed a significant correlation between CD8+ T cell, CD4+ T cell, T helper cell, macrophages, mast cells, and ERCC1 expression. Spearman analysis show that ERCC1 expression is related to macrophages and T cells. A close correlation was observed between increased ERCC1 expression and high tumor immune dysfunction and exclusion (TIDE) score as well as HRD. The results revealed a significant correlation among ERCC1, chemotherapy and estrogen receptor (ER; P < 0.05) expression. Univariate survival analysis revealed a significant correlation (P < 0.05) between that ERCC1 and ER expression, blood vessel invasion, and disease-free survival (DFS). ERCC1 and ER expression, tumor size, blood vessel invasion, pathological type, and lymph node metastases significantly correlated (P < 0.05) with overall survival in patients. Multivariate regression analysis revealed that ERCC1 expression and chemotherapy were independent factors that influence DFS. ERCC1 expression and vascular tumor thrombus were independent influencing factors that influence OS.

Conclusion

A correlation was observed between high ERCC1 expression and poor patient prognosis. High ERCC1 expression also influences the efficacy of immunotherapy and chemotherapy.

A novel prognostic biomarker CD3G that correlates with the tumor microenvironment in cervical cancer

Cervical cancer (CESC) is the fourth most common and death-causing gynecological cancer, mostly induced by infection of human papillomavirus (HPV). Multiple components of the tumor microenvironment (TME), such as tumor infiltrating immune cells, could be targets of immunotherapy for HPV-related CESC. However, little is known about the TME of CESC until now. Here, we aimed to uncover the pathogenesis as well as to identify novel biomarkers to predict prognosis and immunotherapy efficacy for CESC. Combining the transcriptomic data and clinical characteristics, we identified differentially expressed genes in CESC samples from TCGA database by comparing the two groups with different ImmuneScore and StromalScore. Next, we detected ten key genes based on the PPI network and survival analyses with the univariate Cox regression model. Thereafter, we focused on CD3G, the only gene exhibiting increased RNA and protein expression in tumors by multiple analyses. Higher CD3G expression was associated with better survival; and it was also significantly associated with immune-related pathways through GSEA analysis. Furthermore, we found that CD3G expression was correlated with 16 types of TICs. Single cell RNA-sequencing data of CD3G in lymphocytes subgroup indicated its possible role in HPV defense. Hence, CD3G might be a novel biomarker in prognosis and immunotherapy for CESC patients.