Establishment and validation of an immune-associated signature in lung adenocarcinoma

Identification of age- and immune-related gene signatures for clinical outcome prediction in lung adenocarcinoma

BACKGROUND

The understanding of the factors causing decreased overall survival (OS) in older patients compared to younger patients in lung adenocarcinoma (LUAD) remains.

METHODS

Gene expression profiles of LUAD were obtained from publicly available databases by Kaplan-Meier analysis was performed to determine whether age was associated with patient OS. The immune cell composition in the tumor microenvironment (TME) was evaluated using CIBERSORT. The fraction of stromal and immune cells in tumor samples were also using assessed using multiple tools including ESTIMATE, EPIC, and TIMER. Differentially expressed genes (DEGs) from the RNA-Seq data that were associated with age and immune cell composition were identified using the R package DEGseq. A 22-gene signature composed of DEGs associated with age and immune cell composition that predicted OS were constructed using Least Absolute Shrinkage and Selection Operator (LASSO).

RESULTS

In The Cancer Genome Atlas (TCGA)-LUAD dataset, we found that younger patients (≤70) had a significant better OS compared to older patients (>70). In addition, older patients had significantly higher expression of immune checkpoint proteins including inhibitory T cell receptors and their ligands. Moreover, analyses using multiple bioinformatics tools showed increased immune infiltration, including CD4+ T cells, in older patients compared to younger patients. We identified a panel of genes differentially expressed between patients >70 years compared to those ≤70 years, as well as between patients with high or low immune scores and selected 84 common genes to construct a prognostic gene signature. A risk score calculated based on 22 genes selected by LASSO predicted 1, 3, and 5-year OS, with an area under the curve (AUC) of 0.72, 0.72, 0.69, receptively, in TCGA-LUAD dataset and an independent validation dataset available from the European Genome-phenome Archive (EGA).

CONCLUSION

Our results demonstrate that age contributes to OS of LUAD patients atleast in part through its association with immune infiltration in the TME.

Identification of Angiogenesis-Related Prognostic Biomarkers Associated With Immune Cell Infiltration in Breast Cancer

Background: This study aimed to explore the prognostic value of angiogenesis-related genes (ARGs) and their association with immune cell infiltration (ICI) in breast cancer (BC).

Methods: Transcriptome data of BC were obtained from the TCGA and GEO databases. Differentially expressed ARGs were identified by the limma package. The identification of key genes and construction of the risk score model were performed by univariate and multivariate Cox regression algorithms. The prognostic value of the risk score was assessed by ROC curves and nomogram. GO, KEGG pathway, and GSEA were used to investigate the biological functions of differentially expressed genes (DEGs), and CIBERSORT, ssGSEA, and xCell algorithms were performed to estimate the ICI in high-risk and low-risk groups. The correlations between prognostic biomarkers and differentially distributed immune cells were assessed. Moreover, a ceRNA regulatory network based on prognostic biomarkers was constructed and visualized by Cytoscape software.

Results: A total of 18 differentially expressed ARGs were identified between tumor and adjacent normal tissue samples. TNFSF12, SCG2, COL4A3, and TNNI3 were identified as key prognostic genes by univariate and multivariate Cox regression analyses. The risk score model was further constructed based on the four-gene signature and validated in GSE7390 and GSE88770 datasets. ROC curves and nomogram indicated that the risk score had good accuracy for determining BC patient survival. Biological function analysis showed that DEGs in high- and low-risk groups had a high enrichment in immune-related biological processes and signaling pathways. Moreover, significantly different ICIs were found between high- and low-risk groups, such as memory B cells, CD8⁺ T cells, resting memory CD4⁺ T cells, follicular helper T cells, regulatory T cells, monocytes, M2 macrophages, and neutrophils, and each prognostic biomarker was significantly correlated with one or more immune cell types.

Conclusion: The current study identified novel prognostic ARGs and developed a prognostic model for predicting survival in patients with BC. Furthermore, this study indicated that ICI may act as a bond between angiogenesis and BC. These findings enhance our understanding of angiogenesis in BC and provide novel guidance on developing therapeutic targets for BC patients.

SCG2 is a Prognostic Biomarker Associated With Immune Infiltration and Macrophage Polarization in Colorectal Cancer

Colorectal cancer (CRC) is the second most lethal malignancy around the world. Limited efficacy of immunotherapy creates an urgent need for development of novel treatment targets. Secretogranin II (SCG2) is a member of the chromogranin family of acidic secretory proteins, has a role in tumor microenvironment (TME) of lung adenocarcinoma and bladder cancer. Besides, SCG2 is a stroma-related gene in CRC, its potential function in regulating tumor immune infiltration of CRC needs to be fully elucidated. In this study, we used western blot, real-time PCR, immunofluorescence and public databases to evaluate SCG2 expression levels and distribution. Survival analysis and functional enrichment analysis were performed. We examined TME and tumor infiltrating immune cells using ESTIMATE and CIBERSORT algorithm. The results showed that SCG2 expression was significantly decreased in CRC tumor tissues, and differentially distributed between tumor and adjacent normal tissues. SCG2 was an independent prognostic predictor in CRC. High expression of SCG2 correlated with poor survival and advanced clinical stage in CRC patients. SCG2 might regulate multiple tumor- and immune-related pathways in CRC, influence tumor immunity by regulating infiltration of immune cells and macrophage polarization in CRC.

The S100 Protein Family as Players and Therapeutic Targets in Pulmonary Diseases

The S100 protein family consists of over 20 members in humans that are involved in many intracellular and extracellular processes, including proliferation, differentiation, apoptosis, Ca₂⁺ homeostasis, energy metabolism, inflammation, tissue repair, and migration/invasion. Although there are structural similarities between each member, they are not functionally interchangeable. The S100 proteins function both as intracellular Ca²⁺ sensors and as extracellular factors. Dysregulated responses of multiple members of the S100 family are observed in several diseases, including the lungs (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cystic fibrosis, pulmonary hypertension, and lung cancer). To this degree, extensive research was undertaken to identify their roles in pulmonary disease pathogenesis and the identification of inhibitors for several S100 family members that have progressed to clinical trials in patients for nonpulmonary conditions. This review outlines the potential role of each S100 protein in pulmonary diseases, details the possible mechanisms observed in diseases, and outlines potential therapeutic strategies for treatment.

Potential role of m6A RNA methylation regulators in osteosarcoma and its clinical prognostic value

BACKGROUND

Osteosarcoma is a disease with high mortality in children and adolescents, and metastasis is one of the important clinical features of osteosarcoma. N6-Methyladenosine (m6A) is the most abundant methylation modification in mRNA, which is regulated by m6A regulators. It is reported that it is related to the occurrence and development of tumors. However, the mechanism of its action in osteosarcoma is rarely known. The purpose of this study was to identify the potential role of m6A regulatory factor in osteosarcoma and its clinical prognostic value.

METHODS

Here, we used The Cancer Genome Atlas (TCGA) to comprehensively analyze the relationship between m6A regulatory factors and osteosarcoma (metastasis group and non-metastasis group). We analyzed their survival relationship and analyzed all the m6A regulatory factors in TCGA tumor data set by using the univariate Cox proportional hazard regression model. Finally, we selected two survival-related methylation regulators (FTO and IGF2BP2) as risk gene signature.

RESULTS

According to the median risk, patients were divided into low-risk group and high-risk group. Multivariate Cox regression analysis showed that these two risk genes were considered to be the key factors independently predicting the prognosis of patients with osteosarcoma. In addition, we verified their characteristics with gene expression omnibus (GEO) DataSets and confirmed that they are related to tumor and immune-related signaling pathways through gene set enrichment analysis (GESA) and immune infiltration analysis.

CONCLUSIONS

In conclusion, m6A regulators might play an important role in the metastasis of osteosarcoma and have potential important value for the prognosis and treatment strategy of osteosarcoma patients.

The predictive prognostic values of CBFA2T3, STX3, DENR, EGLN1, FUT4, and PCDH7 in lung cancer

BACKGROUND

Lung cancer is one of the most malignant tumors. However, neither the pathogenesis of lung cancer nor the prognosis markers are completely clear. The purpose of this study is to screen the diagnostic or prognostic markers of lung cancer.

METHODS

TCGA and GEO datasets were used to analyze the relationship between lung cancer-related genes and lung cancer samples. Common differential genes were screened, and a univariate Cox regression analysis was used to screen survival related genes. A univariable Cox proportional hazards regression analysis was used to verify the genes and construct risk model. The key factors affecting the prognosis of lung cancer were determined by univariate and multivariate regression analyses. The ROC curve, AUC and the survival of each risk gene was analyzed. Finally, the biological functions of high- and low-risk patients were explored by GSEA and an immune-infiltration analysis.

RESULTS

Based on the common differential genes, 13 genes significantly related to lung cancer survival were identified. Eight risk genes (CBFA2T3, DENR, EGLN1, FUT2, FUT4, PCDH7, PHF14, and STX3) were screened out. The results showed that risk status may be an independent prognostic factor, and the risk score predicted the prognosis of lung cancer. CBFA2T3 and STX3 are protective genes, while DENR, EGLN1, FUT4 and PCDH7 are dangerous genes. These 6 genes can be used as independent lung cancer prognosis markers. The corresponding biological functions of genes expressed in high-risk patients were mostly related to tumor proliferation and inflammatory infiltration. Neutrophil, CD8+T, Macrophage M0, Macrophage M1- and mDC-activated cells were high in high-risk status samples.

CONCLUSIONS

CBFA2T3, STX3, DENR, EGLN1, FUT4, and PCDH7 are important participants in the occurrence and development of lung cancer. High-risk patients display serious inflammatory infiltration. This study not only provides insight into the mechanism of occurrence and development of lung cancer, but also provides potential targets for targeted therapy of lung cancer.