Angiogenic Factor-Based Signature Predicts Prognosis and Immunotherapy Response in Non-Small-Cell Lung Cancer

Prognostic Value of Pentraxin3 Protein Expression in Human Malignancies: A Systematic Review and Meta-Analysis

BACKGROUND/OBJECTIVES

Pentraxin 3 (PTX3), a member of the pentraxin superfamily, plays diverse roles in immunity and inflammation. Its dual role in tumorigenesis, exhibiting both protumoral and antitumoral effects, has been the subject of conflicting reports. High PTX3 expression levels in serum and tumor tissues have been associated with poor prognosis in various malignancies, suggesting its potential as a prognostic biomarker. Through this meta-analysis, we aim to comprehensively assess the prognostic significance of PTX3 protein expression in human malignancies and evaluate its potential as a pan-cancer prognostic marker.

METHODS

A systematic literature search was conducted across the PubMed, Embase, Web of Science, MEDLINE, and Cochrane Library databases. Studies were included if they assessed the association between PTX3 protein expression and overall survival (OS) in cancer patients. Hazard ratios (HRs) were pooled using a random-effects model. Subgroup analyses were performed based on the method of PTX3 assessment, and publication bias was evaluated using Egger's and Begg's tests.

RESULTS

Nine studies encompassing 1215 patients were included in the analysis. High PTX3 expression was significantly associated with poorer OS (HR = 1.89, 95% CI = 1.55-2.32, p < 0.01) with no significant heterogeneity (I2 = 0%). Subgroup analysis revealed consistent results across different assessment methods (immunohistochemistry: HR = 1.93, p < 0.01; immunoassay: HR = 1.86, p < 0.01). However, publication bias was detected (Egger's test, p = 0.03).

CONCLUSIONS

High PTX3 protein expression is associated with a poor prognosis in various malignancies, supporting its potential as a prognostic biomarker.

Microsatellite instability-related prognostic risk score (MSI-pRS) defines a subset of lung squamous cell carcinoma (LUSC) patients with genomic instability and poor clinical outcome

Background: Lung squamous cell carcinoma (LUSC) shares less typical onco-drivers and target resistance, but a high overall mutation rate and marked genomic complexity. Mismatch repair (MMR) deficiency leads to microsatellite instability (MSI) and genomic instability. MSI is not an ideal option for prognosis of LUSC, whereas its function deserves exploration. Method: MSI status was classified by MMR proteins using unsupervised clustering in the TCGA-LUSC dataset. The MSI score of each sample was determined by gene set variation analysis. Intersections of the differential expression genes and differential methylation probes were classified into functional modules by weighted gene co-expression network analysis. Least absolute shrinkage and selection operator regression and stepwise gene selection were performed for model downscaling. Results: Compared with the MSI-low (MSI-L) phenotype, MSI-high (MSI-H) displayed higher genomic instability. The MSI score was decreased from MSI-H to normal samples (MSI-H > MSI-L > normal). A total of 843 genes activated by hypomethylation and 430 genes silenced by hypermethylation in MSI-H tumors were classified into six functional modules. CCDC68, LYSMD1, RPS7, and CDK20 were used to construct MSI-related prognostic risk score (MSI-pRS). Low MSI-pRS was a protective prognostic factor in all cohorts (HR = 0.46, 0.47, 0.37; p-value = 7.57e-06, 0.009, 0.021). The model contains tumor stage, age, and MSI-pRS that showed good discrimination and calibration. Decision curve analyses indicated that microsatellite instability-related prognostic risk score added extra value to the prognosis. A low MSI-pRS was negatively correlated with genomic instability. LUSC with low MSI-pRS was associated with increased genomic instability and cold immunophenotype. Conclusion: MSI-pRS is a promising prognostic biomarker in LUSC as the substitute of MSI. Moreover, we first declared that LYSMD1 contributed to genomic instability of LUSC. Our findings provided new insights in the biomarker finder of LUSC.

Insights into the Relationship between Pentraxin-3 and Cancer

Although cancer can be cured if detected early and treated effectively, it is still a leading cause of death worldwide. Tumor development can be limited by an appropiate immune response, but it can be promoted by chronic extensive inflammation through metabolic dysregulation and angiogenesis. In the past decade, numerous efforts have been made in order to identify novel candidates with predictive values in cancer diagnostics. In line with this, researchers have investigated the involvement of pentraxin-3 (PTX-3) in cellular proliferation and immune escape in various types of cancers, although it has not been clearly elucidated. PTX-3 is a member of the long pentraxin subfamily which plays an important role in regulating inflammation, innate immunity response, angiogenesis, and tissue remodeling. Increased synthesis of inflammatory biomarkers and activation of different cellular mechanisms can induce PTX-3 expression in various types of cells (neutrophils, monocytes, lymphocytes, myeloid dendritic cells, fibroblasts, and epithelial cells). PTX-3 has both pro- and anti-tumor functions, thus dual functions in oncogenesis. This review elucidates the potential usefulness of PTX-3 as a serum biomarker in cancer. While future investigations are needed, PTX-3 is emerging as a promising tool for cancer's diagnosis and prognosis, and also treatment monitoring.

Roles of follistatin-like protein 3 in human non-tumor pathophysiologies and cancers

Follistatin-like protein 3 (FSTL3) is a type of FSTLs. By interacting with a disintegrin and metalloproteinase 12 (ADAM12), transforming growth factor-β ligands (activin, myostatin and growth differentiation factor (GDF) 11), FSTL3 can either activate or inhibit these molecules in human non-tumor pathophysiologies and cancers. The FSTL3 gene was initially discovered in patients with in B-cell chronic lymphocytic leukemia, and subsequent studies have shown that the FSTL3 protein is associated with reproductive development, insulin resistance, and hematopoiesis. FSTL3 reportedly contributes to the development and progression of many cancers by promoting tumor metastasis, facilitating angiogenesis, and inducing stem cell differentiation. This review summarizes the current pathophysiological roles of FSTL3, which may be a putative prognostic biomarker for various diseases and serve as a potential therapeutic target.

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Purpose

To investigate the significance of macrophage infiltration to the prognosis of lung adenocarcinoma.

Methods

R language bioinformatics analysis technology, was used to obtain macrophage infiltration-related module genes through WGCNA (Weighted Gene Co-Expression Network Analysis). Marker genes of macrophage subtypes were identified using single-cell sequencing of lung adenocarcinoma tissue. Risk score models were constructed and validated using external data cohorts and clinical samples.

Results

Analysis of cohorts TCGA-LUAD, GSE11969, GSE31210, GSE50081, GSE72094 and GSE8894, revealed a negative correlation between macrophage infiltration and survival. Immunohistochemical analyses of clinical samples were consistent with these data. Based on cell-cluster-markers and TAMs-related-genes, TOP8 genes were obtained (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, and TK1) with a significant association to prognosis. Risk score models including 9 factors (C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65) for prognosis were constructed. The efficacy, stability and generalizability of the risk score models were validated using multiple data cohorts (GSE19188, GSE26939, GSE31210, GSE50081, GSE42127, and GSE72094).

Conclusions

Macrophage infiltration negatively correlates with prognosis in patients with lung adenocarcinoma. Based on cell-cluster-markers and TAMs-related-genes, both TOP8 genes (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, TK1) and risk score models using C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65 could predict disease prognosis.