Establishment and validation of an aging-related risk signature associated with prognosis and tumor immune microenvironment in breast cancer

Machine learning potential predictor of idiopathic pulmonary fibrosis

Introduction

Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.

Methods

In this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.

Results

These findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.

Discussion

This study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies.

Prediction of novel biomarkers for gastric intestinal metaplasia and gastric adenocarcinoma using bioinformatics analysis

Background & aim

The histologic and molecular changes from intestinal metaplasia (IM) to gastric cancer (GC) have not been fully characterized. The present study sought to identify potential alterations in signaling pathways in IM and GC to predict disease progression; these alterations can be considered therapeutic targets.

Materials & methods

Seven gene expression profiles were selected from the GEO database. Discriminate differentially expressed genes (DEGs) were analyzed by EnrichR. The STRING database, Cytoscape, Gene Expression Profiling Interactive Analysis (GEPIA), cBioPortal, NetworkAnalyst, MirWalk database, OncomiR, and bipartite miRNA‒mRNA correlation network was used for downstream analyses of selected module genes.

Results

Analyses revealed that extracellular matrix-receptor interactions (ITGB1, COL1A1, COL1A2, COL4A1, FN1, COL6A3, and THBS2) in GC and PPAR signaling pathway interactions (FABP1, APOC3, APOA1, HMGCS2, and PPARA and PCK1) in IM may play key roles in both the carcinogenesis and progression of underlying GC from intestinal metaplasia. IM enrichment indicated that this is closely related to digestion and absorption. The TF-hub gene regulatory network revealed that AR, TCF4, SALL4, and ESR1 were more important for hub gene expression. It was revealed that the development and prediction of GC may be affected by hsa-miR-29. It was found that PTGR1, C1orf115, CRYL1, ALDOB, and SULT1B1 were downregulated in GC and upregulated in IM. Therefore, they might have tumor suppressor activity in GC progression.

Conclusion

New potential biomarkers and pathways involved in GC and IM were identified that are important for the transformation of GC from IM to adenocarcinoma and can be therapeutic targets for GC.

The emerging role of fatty acid binding protein 7 (FABP7) in cancers

Fatty acid binding protein 7 (FABP7) is an intracellular protein involved in the uptake, transportation, metabolism, and storage of fatty acids (FAs). FABP7 is upregulated up to 20-fold in multiple cancers, usually correlated with poor prognosis. FABP7 silencing or pharmacological inhibition suggest FABP7 promotes cell growth, migration, invasion, colony and spheroid formation/increased size, lipid uptake, and lipid droplet formation. Xenograft studies show that suppression of FABP7 inhibits tumour formation and tumour growth, and improves host survival. The molecular mechanisms involve promotion of FA uptake, lipid droplets, signalling [focal adhesion kinase (FAK), proto-oncogene tyrosine-protein kinase Src (Src), mitogen-activated protein kinase kinase/p-extracellular signal-regulated kinase (MEK/ERK), and Wnt/β-catenin], hypoxia-inducible factor 1-alpha (Hif1α), vascular endothelial growth factor A/prolyl 4-hydroxylase subunit alpha-1 (VEGFA/P4HA1), snail family zinc finger 1 (Snail1), and twist-related protein 1 (Twist1). The oncogenic capacity of FABP7 makes it a promising pharmacological target for future cancer treatments.

PROS1, a clinical prognostic biomarker and tumor suppressor, is associated with immune cell infiltration in breast cancer: A bioinformatics analysis combined with experimental verification

BACKGROUND

PROS1 is an encoding gene that can generate protein S. This protein is a glycoprotein found in plasma that conducts physiological functions with vitamin K. However, the impact of its expression remains absent in the progression and prognosis of breast cancer (BC).

METHODS

In this study, we comprehensively explored the expression of PROS1 in BC and its relationship with BC patient survival, prognosis, and other clinicopathological features. We investigated how PROS1 influenced the malignant biological behavior of BC cells. A series of enrichment analyses were conducted, and the immune landscape was explored in BC affected by PROS1. We also determined correlations between PROS1 and common drug sensitivities used for BC treatments.

RESULTS

PROS1 had low expression in BC, which tended to result in poor survival of BC patients. Overexpressed PROS1 inhibited the migration and invasion of BC cells as well as the epithelial-mesenchymal transition process by downregulating SNAIL. Functional enrichment analyses revealed that PROS1 was more active in extracellular matrix (ECM) organization and structural constituent, ECM-receptor interaction, and other pathways with its related genes. PROS1 was also found to affect immune activity, including various immune cells infiltrating BC. BC patients with high PROS1 expression tended to have lower IC50 values of three common medications and obtained better efficacy.

CONCLUSIONS

PROS1 can become a promising prognostic factor and a possible therapeutic target in BC patients and suppress BC cell metastatic potential. In addition, PROS1 is a crucial factor in immune infiltration in BC.

ANO6 is a reliable prognostic biomarker and correlates to macrophage polarization in breast cancer

To investigate the value of Anoctamin 6 (ANO6) in breast cancer (BC) by analyzing its expression, prognostic impact, biological function, and its association with immune characteristics. We initially performed the expression and survival analyses, followed by adopting restricted cubic spline to analyze the nonlinear relationship between ANO6 and overall survival (OS). Stratified and interaction analyses were conducted to further evaluate its prognostic value in BC. Next, we performed enrichment analyses to explore the possible pathways regulated by ANO6. Finally, the correlations between ANO6 and immune characteristics were analyzed to reveal its role in immunotherapy. Lower ANO6 expression was observed in BC than that in the normal breast group, but its overexpression independently predicted poor OS among BC patients (P < .05). Restricted cubic spline analysis revealed a linear relationship between ANO6 and OS (P-Nonlinear > 0.05). Interestingly, menopause status was an interactive factor in the correlation between ANO6 and OS (P for interaction = 0.016). Additionally, ANO6 was involved in stroma-associated pathways, and its elevation was significantly linked to high stroma scores and macrophage polarization (P < .05). Moreover, ANO6 was notably correlated with immune checkpoint expression levels, and scores of tumor mutation burden and microsatellite instability (all P < .05). ANO6 was an independent prognostic factor for BC, and might be a potential target for the BC treatment. Besides, ANO6 might affect BC progression via the regulation of stroma-related pathways and macrophage polarization.

Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma

Introduction

Female breast cancer has risen to be the most common malignancy worldwide, causing a huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidative stress remain obscure in breast cancer. In this present study, we attempted to establish a predictive model based on senescence-oxidative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions.

Methods

SOSCRGs were identified via correlation analysis. Transcriptome data and clinical information of patients with breast invasive carcinoma (BRCA) were accessed from The Cancer Genome Atlas (TCGA) and GSE96058. SVM algorithm was employed to process subtype classification of patients with BRCA based on SOSCRGs. LASSO regression analysis was utilized to establish the predictive model based on SOSCRGs. Analyses of the predictive model with regards to efficacy evaluation, subgroup analysis, clinical association, immune infiltration, functional strength, mutation feature, and drug sensitivity were organized. Single-cell analysis was applied to decipher the expression pattern of key SOSCRGs in the tumor microenvironment. Additionally, qPCR was conducted to check the expression levels of key SOSCRGs in five different breast cancer cell lines.

Results

A total of 246 SOSCRGs were identified. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed an active immune landscape. A SOSCRGs-based predictive model was subsequently developed and the risk score was clarified as independent prognostic predictors in breast cancer. A novel nomogram was constructed and exhibited favorable predictive capability. We further ascertained that the infiltration levels of immune cells and expressions of immune checkpoints were significantly influenced by the risk score. The two risk groups were characterized by distinct functional strengths. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. The low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. The analysis further revealed a significantly positive correlation between risk score and TMB. Patients in the low risk group may also sensitively respond to several drug agents. Single-cell analysis dissected that ERRFI1, ETS1, NDRG1, and ZMAT3 were expressed in the tumor microenvironment. Moreover, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified and compared by qPCR respectively.

Conclusion

Multidimensional evaluations verified the clinical utility of the SOSCRGs-based predictive model to predict prognosis, aid clinical decision, and risk stratification for patients with breast cancer.

A novel prognostic signatures based on metastasis- and immune-related gene pairs for colorectal cancer

Background

Metastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention.

Methods

A total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity.

Results

Based on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC.

Conclusion

In conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment.