E2F1/2/4 mRNA is associated with immune infiltration and are potential biomarkers for the prognosis of human gastric carcinoma

Comprehensive review regarding the association of E2Fs with the prognosis and immune infiltrates in human head and neck squamous cell carcinoma

E2F transcription factors (E2Fs) are a group of genes that encode a family of transcription factors. They have been identified as being involved in the tumor progression of various cancer types. However, little is known about the expression level, genetic variation, molecular mechanism, and prognostic value and immune infiltration of different E2Fs in HNSCC.In this study, we utilized multiple databases to investigate the mRNA expression level, genetic alteration, and biological function of E2Fs in HNSCC patients. Then, the relationship between E2Fs expression and its association with the occurrence, progress, prognosis, and immune cell infiltration in patients with HNSCC was evaluated. We found that all eight E2Fs were higher expressed in HNSCC tissues than in normal tissues, and the expression levels of E2F1/2/3/4/5/6/8 were also associated with the stage and grade of HNSCC. The abnormal expression of E2F1/2/4/8 in HNSCC patients is related to the clinical outcome. The expression of E2Fs was statistically correlated with the immune cell infiltration in HNSCC and the infiltration of B cells and CD8+ T cells were positively associated with better OS in HNSCC patients. Furthermore, we verified the E2F2 at the tissue level in the validation experiment. Our study may provide novel insights into the choice of immunotherapy targets and potential prognostic biomarkers in HNSCC patients.

Development of a Signature Based on Eight Metastatic-Related Genes for Prognosis of GC Patients

Gastric cancer (GC) has been a common tumor type with high mortality. Distal metastasis is one of the main causes of death in GC patients, which is also related to poor prognosis. The mRNA profiles and clinical information of GC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Univariate Cox and LASSO Cox analyses were used to screen the optimal metastasis-related genes (MRGs) to establish a prognostic Risk Score model for GC patients. The nomogram was used to visualize the Risk Score and predict the 1-, 3-, 5-year survival rate. The immune cell infiltration was analyzed by CIBERSORT and the ratio of immune-stromal component was calculated by the ESTIMATE algorithm. A total of 142 differentially expressed genes were identified between metastatic and non-metastatic GC samples. The optimal 8 genes, comprising GAMT (guanidinoacetate N-methyltransferase), ABCB5 (ATP-binding cassette subfamily B member 5), ITIH3 (inter-alpha-trypsin inhibitor heavy chain 3), GDF3 (growth differentiation factor 3), VSTM2L (V-set and transmembrane domain-containing 2 like), CIDEA (cell death inducing DFFA like effector a), NPTX1 (neuronal pentraxin-1), and UMOD (uromodulin), were further screened to establish a prognostic Risk Score, which proved to be an independent prognostic factor. Patients in high-risk group had a poor prognosis. There were significant differences in the proportion of 11 tumor-infiltrating immune cells between high-risk and low-risk subgroups. In addition, the StromalScore, ImmuneScore, and ESTIMATEScore in high-risk group were higher than those in low-risk group, indicating that the tumor microenvironment of the high-risk group was more complex. A Risk Score model based on eight metastasis-related genes could clearly distinguish the prognosis of GC patients. The poor prognosis of patients with high-Risk Score might be associated with the complex tumor microenvironments.

GNG4, as a potential predictor of prognosis, is correlated with immune infiltrates in colon adenocarcinoma

The tumour microenvironment (TME) and immunosuppression play an important role in colon cancer (CC) metastasis, which seriously affects the prognosis of CC. G protein subunit gamma 4 (GNG4) has been shown to participate in tumour progression and the tumour mutation burden (TMB) in colorectal cancer. However, the effect of GNG4 on the CC TME and immunology remains elusive. Weighted gene coexpression network analysis (WGCNA) was employed for screening aberrantly expressed genes associated with the immune score, and GNG4 was then selected through prognostic and immune correlation analysis. Based on RNA sequencing data obtained from the TCGA and GEO databases, the expression pattern and immune characteristics of GNG4 were comprehensively examined using a pan-cancer analysis. Upregulation of GNG4 was linked to an adverse prognosis and immune inhibitory phenotype in CC. Pan-cancer analysis demonstrated higher GNG4 expression in tumours than in paired normal tissue in human cancers. GNG4 expression was closely related to prognosis, TMB, immune checkpoints (ICPs), microsatellite instability (MSI) and neoantigens. GNG4 promoted CC cell proliferation, migration and invasion and participated in immune regulation in the TME. Significantly, GNG4 expression was found to negatively correlate with tumour-infiltrating immune cells, ICP, TMB and MSI in CC. GNG4 expression predicted the immunotherapy response in the IMvigor210 cohort, suggesting that GNG4 could be used as a potential biomarker in CC for prognostication and immunology. Moreover, the expression of GNG4 predicted the immunotherapy response of ICB in CC.

Construction of ovarian metastasis-related immune signature predicting prognosis of gastric cancer patients

BACKGROUND

Ovarian metastasis (OM) results in poor survival of gastric cancer (GC) patients. While immunotherapy has emerged as a promising approach for late-stage GC, validated immune-related prognostic signatures still remain in need. In this study, we constructed an ovarian metastasis- and immune-related prognostic signature (OMIRPS), characterized the molecular and immune features of OMIRPS-categorized subgroups and predicted their potential response to immunotherapy.

METHODS

Three individual cohorts were used to construct and evaluate OMIRPS: RNA-seq of matched primary GC and OM from Fudan University Shanghai Cancer Center (FUSCC) (discovery cohort, n = 4), The Cancer Genome Atlas (TCGA) (training cohort, n = 544) and GSE84437 (validation cohort, n = 433). Differentially expressed genes (DEGs) identified between primary GC and OM and immune-related genes (IRGs) from the ImmPort and InnateDB databases were used to identify immune-related prognostic hub genes, which were further used to construct OMIRPS by using LASSO regression analysis. Prognosis, molecular characteristics, immune features, and differential immunotherapy efficacy between different OMIRPS subgroups were analyzed.

RESULTS

Functional analyses of DEGs revealed the significance of immune-related signatures and pathways in the OM. Immune-related prognostic hub genes including TNFRSF18, CARD11, BCL11B, NRP1, BNIP3L, and ATF3 were utilized to construct OMIRPS, which was identified as an independent prognostic factor. Comprehensive analyses unveiled the distinctive molecular and immune characteristics of OMIRPS-high and -low subgroup in regard to enriched pathways, mutation rate, tumor mutation burden, microsatellite instability status, infiltrated immune cell, immune exclusion score, and the prediction of immunotherapy efficacy. Additionally, OMIRPS was associated with Immune Subtypes with borderline significance.

CONCLUSIONS

RNA-seq of paired primary and ovarian metastatic tumors unveiled the significance of immune-related pathways and tumor immune microenvironment in OM. OMIRPS served as a promising biomarker to predict the prognosis of GC patients and distinguish the molecular features, immune characteristics, and efficacy of immunotherapy between different subgroups.