Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma

A novel signature based on microvascular invasion predicts the recurrence of HCC

Background and objectives

In hepatocellular carcinoma (HCC) patients, microvascular invasion (MVI) is associated with worse outcomes regardless of treatment. No single reliable preoperative factor exists to predict MVI. The aim of the work described here was to develop a new MVI− based mRNA biomarker to differentiate between high and low risk patients.

Methods

Using The Cancer Genome Atlas (TCGA) database, we collected data from 315 HCC patients, including mRNA expression and complete clinical data. We generated a seven-mRNA signature to predict patient outcomes. The mRNA signature was validated using the GSE36376 cohort. Finally, we tested the formula in our own 53 HCC patients using qPCR for the seven mRNAs and analyzing the computed tomography (CT) features.

Results

This seven‐mRNA signature significantly correlated with length of recurrence-free survival (RFS) and overall survival (OS) for both the training and validation groups. RFS and OS were briefer in high risk versus low risk patients. A Kaplan–Meier analysis also indicated that survival time was significantly shortened in the high risk group versus the low risk group. Time-dependent receiver operating characteristic analysis demonstrated good predictive performance for the seven-mRNA signature. The mRNA signature also acts as an independent factor according to a Multivariate analysis. Our results are consistent with the seven-mRNA formula risk score.

Conclusion

Our research showed a novel seven-mRNA biomarker based on MVI predicting RFS and OS in HCC patients. This mRNA signature can stratify patients into subgroups based on their risk of recurrence to help guide individualized treatment and precision management in HCC.

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.

ASCL1 promotes tumor progression through cell-autonomous signaling and immune modulation in a subset of lung adenocarcinoma

The master regulator of neuroendocrine differentiation, achaete-scute complex homolog 1 (ASCL1) defines a subgroup of lung adenocarcinoma. However, the mechanistic role of ASCL1 in lung tumorigenesis and its relation to the immune microenvironment is principally unknown. Here, the immune landscape of ASCL1-positive lung adenocarcinomas was characterized by immunohistochemistry. Furthermore, ASCL1 was transduced in mouse lung adenocarcinoma cell lines and comparative RNA-sequencing and secretome analyses were performed. The effects of ASCL1 on tumorigenesis were explored in an orthotopic syngeneic transplantation model. ASCL1-positive lung adenocarcinomas revealed lower infiltration of CD8⁺, CD4⁺, CD20⁺, and FOXP3⁺ lymphocytes and CD163⁺ macrophages indicating an immune desert phenotype. Ectopic ASCL1 upregulated cyclin transcript levels, stimulated cell proliferation, and enhanced tumor growth in mice. ASCL1 suppressed secretion of chemokines, including CCL20, CXCL2, CXCL10, and CXCL16, indicating effects on immune cell trafficking. In accordance with lower lymphocytes infiltration, ASCL1-positive lung adenocarcinomas demonstrated lower abundance of CXCR3-and CCR6-expressing cells. In conclusion, ASCL1 mediates its tumor-promoting effect not only through cell-autonomous signaling but also by modulating chemokine production and immune responses. These findings suggest that ASCL1-positive tumors represent a clinically relevant lung cancer entity.