The significance of MAGED4 expression in non-small cell lung cancer as analyzed by real-time fluorescence quantitative PCR

Transcriptomic Profiling for Prognostic Biomarkers in Early-Stage Squamous Cell Lung Cancer (SqCLC)

Squamous cell lung carcinoma (SqCLC) is associated with high mortality and limited treatment options. Identification of therapeutic targets and prognostic biomarkers is still lacking. This research aims to analyze the transcriptomic profile of SqCLC samples and identify the key genes associated with tumorigenesis, overall survival (OS), and a profile of the tumor-infiltrating immune cells. Differential gene expression analysis, pathway enrichment analysis, and Gene Ontology analysis on RNA-seq data obtained from FFPE tumor samples (N = 23) and healthy tissues (N = 3) were performed (experimental cohort). Validation of the results was conducted on publicly available gene expression data using TCGA LUSC (N = 225) and GTEx healthy donors' cohorts (N = 288). We identified 1133 upregulated and 644 downregulated genes, common for both cohorts. The most prominent upregulated genes were involved in cell cycle and proliferation regulation pathways (MAGEA9B, MAGED4, KRT, MMT11/13), while downregulated genes predominately belonged to immune-related pathways (DEFA1B, DEFA1, DEFA3). Results of the survival analysis, conducted on the validation cohort and commonly deregulated genes, indicated that overexpression of HOXC4 (p < 0.001), LLGL1 (p = 0.0015), and SLC4A3 (p = 0.0034) is associated with worse OS in early-stage SqCLC patients. In contrast, overexpression of GSTZ1 (p = 0.0029) and LILRA5 (p = 0.0086) was protective, i.e., associated with better OS. By applying a single-sample gene-set enrichment analysis (ssGSEA), we identified four distinct immune subtypes. Immune cell distribution suggests that the memory T cells (central and effector) and follicular helper T cells could serve as important stratification parameters.

Multimodal AI for prediction of distant metastasis in carcinoma patients

Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

Combined treatment with epigenetic agents enhances anti-tumor activity of MAGE-D4 peptide-specific T cells by upregulating the MAGE-D4 expression in glioma

Objective

The study evaluated the efficacy of combined epigenetic drugs of decitabine (DAC), valproic acid (VPA), and trichostatin A (TSA) on immunotherapy against glioma.

Methods

The expression and prognosis of MAGE-D4 in glioma were analyzed online, and the expression of MAGE-D4 and HLA-A2 in glioma induced by epigenetic drugs was detected by qRT-PCR, Western blot, and flow cytometry. The methylation status of the MAGE-D4 promoter was determined by pyrosequencing. An HLA-A2 restricted MAGE-D4 peptide was predicted and synthesized. An affinity assay and a peptide/HLA complex stability assay were performed to determine the affinity between peptide and HLA. CCK8 assay, CFSE assay, ELISA and ELISPOT were performed to detect the function of MAGE-D4 peptide-specific T cells. Flow cytometry, ELISA, and cytotoxicity assays were used to detect the cytotoxicity effect of MAGE-D4 peptide-specific T cells combined with epigenetic drugs against glioma in vitro. Finally, the glioma-loaded mouse model was applied to test the inhibitory effect of specific T cells on gliomas in vivo.

Results

MAGE-D4 was highly expressed in glioma and correlated with poor prognosis. Glioma cells could be induced to express MAGE-D4 and HLA-A2 by epigenetic drugs. MAGE-D4-associated peptides were found that induce DCs to stimulate the highest T-cell activities of proliferation, IL-2 excretion, and IFN-γ secretion. MAGE-D4 peptide-specific T cells treated with TSA only or combining TSA and DAC had the most cytotoxicity effect, and its cytotoxicity effect on glioma cells decreased significantly after HLA blocking. In vivo experiments also confirmed that MAGE-D4-specific T cells inhibit TSA-treated glioma.

Conclusion

MAGE-D4 is highly expressed in glioma and correlated with the prognosis of glioma. The novel MAGE-D4 peptide identified was capable of inducing MAGE-D4-specific T cells that can effectively inhibit glioma growth, and the epigenetic drug application can enhance this inhibition.

Leptin Signaling Affects Survival and Chemoresistance of Estrogen Receptor Negative Breast Cancer

Estrogen-receptor-negative breast cancer (BCER-) is mainly treated with chemotherapeutics. Leptin signaling can influence BCER- progression, but its effects on patient survival and chemoresistance are not well understood. We hypothesize that leptin signaling decreases the survival of BCER- patients by, in part, inducing the expression of chemoresistance-related genes. The correlation of expression of leptin receptor (OBR), leptin-targeted genes (CDK8, NANOG, and RBP-Jk), and breast cancer (BC) patient survival was determined from The Cancer Genome Atlas (TCGA) mRNA data. Leptin-induced expression of proliferation and chemoresistance-related molecules was investigated in triple-negative BC (TNBC) cells that respond differently to chemotherapeutics. Leptin-induced gene expression in TNBC was analyzed by RNA-Seq. The specificity of leptin effects was assessed using OBR inhibitors (shRNA and peptides). The results show that OBR and leptin-targeted gene expression are associated with lower survival of BCER- patients. Importantly, the co-expression of these genes was also associated with chemotherapy failure. Leptin signaling increased the expression of tumorigenesis and chemoresistance-related genes (ABCB1, WNT4, ADHFE1, TBC1D3, LL22NC03, RDH5, and ITGB3) and impaired chemotherapeutic effects in TNBC cells. OBR inhibition re-sensitized TNBC to chemotherapeutics. In conclusion, the co-expression of OBR and leptin-targeted genes may be used as a predictor of survival and drug resistance of BCER- patients. Targeting OBR signaling could improve chemotherapeutic efficacy.

Prognostic roles of MAGE family members in breast cancer based on KM-Plotter Data

Breast cancer is the second leading cause of cancer-associated mortality among women worldwide, and the prevalence and mortality rates associated with this disease are high in Western countries. The melanoma-associated antigen (MAGE) family proteins are well-known tumor-specific antigens; this family includes >60 proteins that serve an important part in cell cycle withdrawal, neuronal differentiation and apoptosis. The aim of the present study was to identify a biomarker within the MAGE family that is specific for breast cancer. In the present study, the prognostic role of MAGE mRNA expression was investigated in patients with breast cancer using the Kaplan-Meier plotter database. The prognostic value of MAGE members in the different intrinsic subtypes of breast cancer was further investigated, as well as the clinicopathological features of the disease. The results of the present study indicated that patients with breast cancer that had high mRNA expression levels of MAGEA5, MAGEA8, MAGEB4 and MAGEB6 had an improved relapse-free survival, whereas those with high mRNA expression levels of MAGEB18 and MAGED4 did not. These results suggested that MAGEA5, MAGEA8, MAGEB4 and MAGEB6 may have roles as tumor suppressors in the occurrence and development of breast cancer, whereas MAGEB18 and MAGED4 may possess carcinogenic potential. MAGED2, MAGED3 and MAGEF1 had different effects depending on the type of breast cancer. In particular, high MAGEC3 mRNA expression was associated with worse RFS in lymph node-positive breast cancer, but with improved RFS in lymph node-negative breast cancer. In patients with wild-type TP53 and patients with different pathological grades of breast cancer, MAGEE2, MAGEH1 and MAGEL2 were more worthy of attention as potential prognostic factors. The results of the present study may help to elucidate the role of MAGE family members in the development of breast cancer, and may promote further research that identifies MAGE-targeting reagents for the treatment of breast cancer.

Prognostic and clinicopathological value of melanoma-associated antigen D4 in patients with glioma

The aim of the present study was to evaluate the clinical importance of melanoma-associated antigen D4 (MAGE-D4) expression in glioma, and to identify it as a valuable prognostic biomarker and therapeutic target. To achieve this, the expression of MAGE-D4 protein in 124 tumor tissues from patients with glioma was measured using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC), and the associations between MAGE-D4expression and clinicopathological factors were evaluated. The survival analysis demonstrated the significant prognostic value of MAGE-D4 in glioma using follow-up data. RT-qPCR and IHC analysis confirmed that MAGE-D4 mRNA and protein expression levels were significantly increased in glioma tissues compared with those in normal brain tissues. The present study demonstrated that the percentage of glioma tissues with high expression of MAGE-D4 mRNA was 67.74%, and the percentage positive for MAGE-D4 protein expression was 78.23%. All patients with high MAGE-D4 expression in cancerous tissues experienced significantly reduced median overall survival (OS; 18.00 vs. 33.29 months; P<0.001) and recurrence-free survival (RFS; 12.7 vs. 28.3 months; P<0.001) times compared with those with low MAGE-D4 expression. In the patients with lower grade glioma [World Health Organization (WHO), I–II], similar results were obtained for the OS (26.11 vs. 57.85 months; P=0.013) and RFS (22.7 vs. 55.3 months; P=0.010) times; however, in patients with high-grade glioma (WHO, III–IV), there were no significant differences between high and low MAGE-D4 expression levels with regard to OS and RFS times (P>0.05). Multivariate analysis indicated that high MAGE-D4 protein expression was an important independent prognostic factor for patients with glioma (hazard ratio, 2.384; P=0.005), and was significantly associated with higher grade glioma (P<0.001). These results indicated that MAGE-D4 may be a potential biomarker for glioma and an important prognostic factor for patients with new or recurring glioma.

High expression and frequently humoral immune response of melanoma-associated antigen D4 in glioma

MAGE-D4 is a novel member of MAGE super-family. It has preliminarily been demonstrated that MAGE-D4 mRNA is not expressed in majority of normal tissues except for brain and ovary in which only trace amount of MAGE-D4 mRNA can be detected, but predominantly expressed in glioma. MAGE-D4 protein expression and its immunogenicity in glioma have not been elucidated well. This study was designed to analyze MAGE-D4 expression both at mRNA and protein level, characteristic of humoral immune response, and their relationships with glioma patients' clinicopathological parameters. Recombinant MAGE-D4 protein and antiserum were generated. Quantitative RT-PCR analysis revealed that MAGE-D4 mRNA expression was overall up-regulated in 41 glioma specimens compared with that in 14 normal brain tissues. Immunohistochemistry analysis showed that 78% (21/27) glioma tissues expressed MAGE-D4 protein, which was predominantly located in the cytoplasm of tumor cells, but absent in any neuroglia cell of normal brain tissues. ELISA analysis demonstrated that humoral response against MAGE-D4 was detected in 17% (7/41) of glioma patients' sera but not in 77 healthy donors. No apparent correlation was observed between the expression and immunogenicity of MAGE-D4 with clinicopathological parameters of glioma. In summary, these results indicate that MAGE-D4 is highly expressed in glioma and can develop specifically humoral response in glioma patients, which supports that it may be a promising biomarker for glioma diagnosis and immunotherapy.