Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

BACKGROUND

Cutaneous melanoma (CM) has an overall poor prognosis due to a high rate of metastasis. This study aimed to explore the role of hypoxia-related genes (HRGs) in CM.

METHODS

We first used on-negative matrix factorization consensus clustering (NMF) to cluster CM samples and preliminarily analyzed the relationship of HRGs to CM prognosis and immune cell infiltration. Subsequently, we identified prognostic-related hub genes by univariate COX regression analysis and the least absolute shrinkage and selection operator (LASSO) and constructed a prognostic model. Finally, we calculated a risk score for patients with CM and investigated the relationship between the risk score and potential surrogate markers of response to immune checkpoint inhibitors (ICIs), such as TMB, IPS values, and TIDE scores.

RESULTS

Through NMF clustering, we identified high expression of HRGs as a risk factor for the prognosis of CM patients, and at the same time, increased expression of HRGs also indicated a poorer immune microenvironment. Subsequently, we identified eight gene signatures (FBP1, NDRG1, GPI, IER3, B4GALNT2, BGN, PKP1, and EDN2) by LASSO regression analysis and constructed a prognostic model.

CONCLUSION

Our study identifies the prognostic significance of hypoxia-related genes in melanoma and shows a novel eight-gene signature to predict the potential efficacy of ICIs.

Characterization of glucose metabolism in breast cancer to guide clinical therapy

Background

Breast cancer (BRCA) ranks as a leading cause of cancer death in women worldwide. Glucose metabolism is a noticeable characteristic of the occurrence of malignant tumors. In this study, we aimed to construct a novel glycometabolism-related gene (GRG) signature to predict overall survival (OS), immune infiltration and therapeutic response in BRCA patients.

Materials and methods

The mRNA sequencing and corresponding clinical data of BRCA patients were obtained from public cohorts. Lasso regression was applied to establish a GRG signature. The immune infiltration was evaluated with the ESTIMATE and CIBERSORT algorithms. The drug sensitivity was estimated using the value of IC50, and further forecasted the therapeutic response of each patient. The candidate target was selected in Cytoscape. A nomogram was constructed via the R package of “rms”.

Results

We constructed a six-GRG signature based on CACNA1H, CHPF, IRS2, NT5E, SDC1 and ATP6AP1, and the high-risk patients were correlated with poorer OS (P = 2.515 × 10⁻⁷). M2 macrophage infiltration was considerably superior in high-risk patients, and CD8⁺ T cell infiltration was significantly higher in low-risk patients. Additionally, the high-risk group was more sensitive to Lapatinib. Fortunately, SDC1 was recognized as candidate target and patients had a better OS in the low-SDC1 group. A nomogram integrating the GRG signature was developed, and calibration curves were consistent between the actual and predicted OS.

Conclusions

We identified a novel GRG signature complementing the present understanding of the targeted therapy and immune biomarker in breast cancer. The GRGs may provide fresh insights for individualized management of BRCA patients.

Exogenous Tetranectin Alleviates Pre-formed-fibrils-induced Synucleinopathies in SH-SY5Y Cells by Activating the Plasminogen Activation System

Parkinson's disease (PD) is a common neurodegenerative disease. Previously we identified tetranectin (TN) as a differentially expressed protein in the cerebrospinal fluid of PD patients, and we were surprised to find that TN knockout mice developed PD features. However, the specific role of TN in PD has not been clarified. In this study, we aimed to determine the effect of exogenous TN on cellular PD models and elucidate the underlying mechanisms. We found that exogenous TN could alleviate pre-formed-fibrils (PFFs)-induced synucleinopathies in SH-SY5Y cells and reduce the cell-to-cell transmission of α-synuclein (SYN). We also found that TN can promote the degradation of SYN by plasmin, which may account for its effect on cellular PD models. Moreover, administration of SYN/PFFs decreased the expression of TN and increased the expression of plasminogen activator inhibitor-1 (PAI-1) in SH-SY5Y cells, thereby reducing plasmin activity. Our findings depict a possible SYN-TN-plasmin interaction in which elevated levels of extracellular SYN monomers and aggregates in PD diminish the production of TN and PAI-1. Such changes lead to a reduced plasmin activity, which in turn reduces the degradation of extracellular SYN, thus forming a vicious cycle.

A Novel lncRNA Panel for Risk Stratification and Immune Landscape in Breast Cancer Patients

Purpose

In recent years, breast cancer (BC) has been a primary cause of mortality in women. However, the underlying mechanisms remain to be elucidated. Accumulating evidence has supported the hypothesis that long noncoding RNAs (lncRNAs) play central roles in the progression of cancer. We aimed to construct an immune-related lncRNA panel to predict the prognosis of patients with BC and evaluate the immune features.

Methods

The expression profiles of patients with BC were obtained from The Cancer Genome Atlas (TCGA) database to screen the differentially expressed lncRNAs (DELs). Pearson’s correlation analysis was employed to filter the DELs related to the immune-associated genes. Univariate Cox regression, the LASSO algorithm, and multivariate Cox regression analyses were conducted to establish the model. Functional enrichment analyses and biological experiments were performed to explore the immune activity of the lncRNA panel.

Results

A four-immune-related lncRNA panel (IRLP) composed of AC022196.1, ARHGAP26-AS1, DPYD-AS1 and PURPL was established in TCGA training cohort. The prognostic accuracy of the predictive model was confirmed in TCGA internal validation cohort, TCGA entire cohort and Qilu external validation cohort. Bioinformatics analyses indicated that the IRLP had a close relationship with tumour infiltrating immune cells and immunomodulatory biomarkers. The biological functions of the four immune-related lncRNAs in BC were first investigated in vitro and in vivo. PURPL was indicated to play a central role in the regulation of macrophage recruitment and polarization via CCL2.

Conclusion

Our study identified IRLP as a reliable prognostic indicator with great potential for clinical application in personalized immunotherapy.

Tumor microenvironment-related multigene prognostic prediction model for breast cancer

Background: Breast cancer is an invasive disease with complex molecular mechanisms. Prognosis-related biomarkers are still urgently needed to predict outcomes of breast cancer patients.

Methods: Original data were download from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The analyses were performed using perl-5.32 and R-x64-4.1.1.

Results: In this study, 1086 differentially expressed genes (DEGs) were identified in the TCGA cohort; 523 shared DEGs were identified in the TCGA and GSE10886 cohorts. Eight subtypes were estimated using non-negative matrix factorization clustering with significant differences seen in overall survival (OS) and progression-free survival (PFS) (P < 0.01). Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs; this score separated breast cancer into low- and high-risk groups with significant differences in survival (P < 0.01) and showed powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.729, 3-year AUC = 0.778, 5-year AUC = 0.781). A nomogram prediction model was constructed using non-negative matrix factorization clustering, the risk score, and clinical characteristics. Our model was confirmed to be related with tumor microenvironment. Furthermore, DEGs in high-risk breast cancer were enriched in histidine metabolism (normalized enrichment score [NES] = 1.49, P < 0.05), protein export (NES = 1.58, P < 0.05), and steroid hormone biosynthesis signaling pathways (NES = 1.56, P < 0.05).

Conclusions: We established a comprehensive model that can predict prognosis and guide treatment.