Esophageal Cancer Associated Immune Genes as Biomarkers for Predicting Outcome in Upper Gastrointestinal Tumors

Chemokines and Their Receptors: Predictors of Therapeutic Potential in Tumor Microenvironment on Esophageal Cancer

Esophageal carcinoma (ESCA) is an aggressive solid tumor. The 5-year survival rate for patients with ESCA is estimated to be less than 20%, mainly due to tumor invasion and metastasis. Therefore, it is urgent to improve early diagnostic tools and effective treatments for ESCA patients. Tumor microenvironment (TME) enhances the ability of tumor cells to proliferate, migrate, and escape from the immune system, thus promoting the occurrence and development of tumor. TME contains chemokines. Chemokines consist of four major families, which are mainly composed of CC and CXC families. The main purpose of this review is to understand the CC and CXC chemokines and their receptors in ESCA, to improve the understanding of tumorigenesis of ESCA and determine new biomarkers for the diagnosis and prognosis of ESCA. We reviewed the literature on CC and CXC chemokines and their receptors in ESCA identified by PubMed database. This article introduces the general structures and functions of CC, CXC chemokines and their receptors in TME, as well as their roles in the progress of ESCA. Chemokines are involved in the development of ESCA, such as cancer cell invasion, metastasis, angiogenesis, and radioresistance, and are key determinants of disease progression, which have a great impact on patient prognosis and treatment response. In addition, a full understanding of their mechanism of action is essential to further verify that these chemokines and their receptors may serve as biomarkers or therapeutic targets of ESCA.

Osteosarcoma-Associated Immune Genes as Potential Immunotherapy and Prognosis Biomarkers

Immune-modulating therapies exhibit abundant promise compared to traditional treatment for osteosarcoma. We aim to establish an immune-related prognostic signatures in osteosarcoma. We identified the differentially expressed genes in osteosarcoma compared with normal controls using the GEO dataset. The intersection with immune-related genes was considered as differentially expressed immune genes. Potential prognosis-related differentially expressed genes were first analyzed with the multifactor Cox regression and then the step function performed the iteration. The best model was finally chosen as the immunological risk score signature model. And finally, we evaluated the correlation of genes in the prognostic model with immune cells, common immune checkpoints, and immune checkpoint blockade responses. We identified 1527 significantly upregulated and 2407 significantly downregulated genes in osteosarcoma compared to normal samples. In the 258 differentially expressed immune genes, 20 genes with independent prognostic significance were included in the step function. Finally, we constructed a prognostic signature for overall survival based on five immune genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) in osteosarcoma. These five genes are closely related to immune infiltration. Osteosarcoma with high JAG2 expression or low CMKLR1 expression may be associated with better immune checkpoint blockade response. JAG2 overexpression or CMKLR1 inhibition induced sensitivity to PD-1 antibody in osteosarcoma cells. We constructed a prognosis prediction signature containing five immune-related genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) with a significant prognostic value in osteosarcoma. Significant differences in immune infiltration and immunotherapy responses were identified between groups with different levels of these genes.

Predicting the prognosis of esophageal cancer based on extensive analysis of new inflammatory response‐related signature

The prognosis of esophageal cancer (ESCA) is very poor, with a 5-year survival rate of less than 20%. On the other hand, inflammation is the characteristic hallmark of ESCA; however, the prognostic relationship between inflammatory response-related genes and ESCA has not been clarified yet. Therefore, in the present manuscript, we intend to investigate the correlation and specific signature of inflammation for the prediction of the prognosis of ESCA. A total of 173 samples were obtained from The Cancer Genome Atlas (TCGA) database, including 162 tumors and 11 normal specimens. The prognostic signature was established by least absolute shrinkage and selection operator Cox regression analysis. The transcription factor regulatory network with genes of the prognostic signature was analyzed from the transcriptional regulatory relationships unravelled by sentence-based text-mining database. Chemotherapy sensitivity and immunotherapy analysis were also performed. Multivariate Cox analysis showed that the signature was an independent prognostic risk factor. The low-risk group had poorer outcomes than the high-risk group. In the high-risk group, the infiltration of most immune cells was high and strongly correlated with the riskScore. In chemotherapeutic drug sensitivity analysis, OSM, AHR, and BTG2 were significantly correlated with the current chemotherapeutic drugs of ESCA. We have demonstrated a valid prognostic signature of inflammatory response-related genes and found strong associations with immune cells, targeted genes, and chemotherapeutic agents.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

Long Noncoding RNA RP11-732M18.3 Promotes Glioma Angiogenesis by Upregulating VEGFA

Gliomas are the most aggressive and common type of malignant brain tumor, with limited treatment options and a dismal prognosis. Angiogenesis, a hallmarks of cancer, is one of two critical events in the progression of gliomas. Accumulating evidence has demonstrated that in glioma dysregulated molecules like long noncoding RNAs (lncRNAs), are closely linked to tumorigenesis and prognosis. However, the effects of and mechanisms of action of lncRNAs during tumor angiogenesis are poorly understood. The effect of lncRNA RP11-732M18.3 on angiogenesis was elucidated through an intracranial orthotopic glioma model, immunohistochemistry, and an in vitro angiogenesis assay. Co-culture experiments and cell migration assays were performed to investigate the function of lncRNA RP11-732M18.3 in vitro. lncRNA RP11-732M18.3 increased CD31⁺ microvessel density, and overexpression of lncRNA RP11-732M18.3 resulted in poor mouse survival. lncRNA RP11-732M18.3 promoted endothelial cell migration and tube formation. Nomogram and Kaplan-Meier survival analyses indicated that higher VEGFA is correlated with a poor prognosis. Mechanistically, lncRNA RP11-732M18.3 promotes angiogenesis by increasing the nuclear level of EP300 and facilitating the transcription and secretion of VEGFA. Our study contributes to the latest understanding of glioma angiogenesis and prognosis. lncRNA RP11-732M18.3 may be a potential treatment target in glioma.

HSPA6 and its role in cancers and other diseases

Heat Shock Protein Family A (Hsp70) Member 6 (HSPA6) (Online Mendelian Inheritance in Man: 140555) belongs to the HSP70 family and is a partially conserved inducible protein in mammals. The HSPA6 gene locates on the human chromosome 1q23.3 and encodes a protein containing two important structural domains: The N-terminal nucleotide-binding domain and the C-terminal substrate-binding domain. Currently, studies have found that HSPA6 not only plays a role in the tumorigenesis and tumor progresses but also causes non-tumor-related diseases. Furthermore, HSPA6 exhibits to inhibit tumorigenesis and tumor progression in some types of cancers but promotes in others. Even though HSPA6 research has increased, its exact roles and mechanisms are still unclear. This article reviews the structure, expression, function, research progress, possible mechanism, and perspective of HSPA6 in cancers and other diseases, highlighting its potential role as a targeted therapeutic and prognostic marker.

A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas

Background. Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS. Methods. Differential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified. Results. A total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS ( $\text{AUC}=0.930$ , 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability ( $\text{C}-\text{index}=0.828$ ). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts. Conclusion. IRSS may serve as a novel survival prediction tool in the classification of LGG patients.

Development of a model to predict the prognosis of esophageal carcinoma based on autophagy-related genes

Aim: To identify a potential prognostic signature of esophageal carcinoma based on autophagy-related genes (ARGs). Methods: RNA sequencing and clinical data were downloaded from the Cancer Genome Atlas. Significantly different ARGs were identified by Wilcoxon signed-rank test. A prognostic model was established employing Cox regression analysis. The model was evaluated by receiver operating characteristic and Kaplan-Meier curve. Results: A total of 28 significantly different ARGs were identified. Seven ARGs were screened to construct the prognostic model. The efficacy of the model was verified. A nomogram also validated the role of risk score in predicting prognosis. Enrichment analyses showed the possible underlying mechanisms. Conclusion: The seven-ARGs prognostic model was validated to be promising for predicting the prognosis of patients with esophageal carcinoma.

Comprehensive analysis of a new immune-related prognostic signature for esophageal cancer and its correlation with infiltrating immune cells and target genes

Background

The incidence of esophageal cancer (ESCA) is increasing rapidly, and the 5-year survival rate is less than 20%. This study provides new ideas for clinical treatment by establishing a prognostic signature composed of immune-related genes (IRGs), and fully analyzing its relationship with target genes and the tumor microenvironment (TME).

Methods

We downloaded the ESCA expression matrix and clinical information from The Cancer Genome Atlas (TCGA) database. Differential expression genes (DEGs) were identified with the edgeR package and crossed with the IRGs we obtained from the ImmPort database to obtain differential IRGs (DEIRGs). The prognostic signature was then obtained through univariate Cox, LASSO-Cox, and multivariate Cox analyses. The receiver operating characteristic (ROC) curve was used to evaluate the prediction effect of the model. The immune cell infiltration abundance obtained by ssGSEA and therapeutic target genes was used to perform sufficient correlation analysis with the obtained prognostic signature and related genes.

Results

A total of 173 samples were obtained from TCGA database, including 162 tumor and 11 normal samples. The 3,033 differential genes were used to obtain 254 DEIRGs by intersections with 2,483 IRGs (IRGs) obtained from the ImmPort Database. Finally, multivariate Cox regression analysis identified eight prognostic DEIRGs and established a new prognostic signature (HR: 2.49, 95% CI: 1.68–3.67; P<0.001). Based on the expression of the eight genes, the cohort was then divided into high and low risk groups and Kaplan-Meier (K-M) curves were plotted with the log-rank test P<0.0001 and 1-, 3-year area under the curve (AUC) >0.7. The K-M curves grouped according to high and low risks performed well in the two subgroup validation cohorts, with log-rank test P<0.05. There were differences in the degree of infiltration of 16 kinds of immune cells in tumor and normal samples, and the infiltration abundance of 12 kinds of immune cells was different in the high and low-risk groups.

Conclusions

An effective and validated prognostic signature composed of IRGs was established and had a strong correlation with immune cells and target genes of drug therapy.