Dataset Information

Tumor immune microenvironment in therapy-naive esophageal adenocarcinoma could predict the nodal status.

ABSTRACT:

Background

Currently, preoperative staging of esophageal adenocarcinoma (EAC) has modest reliability and accuracy for pT and pN stages prediction, which heavily affects overall survival. The interplay among immune checkpoints, oncogenes, and intratumoral and peritumoral immune infiltrating cells could be used to predict loco-regional metastatic disease in early EAC.

Methods

We prospectively evaluated immune markers expression and oncogenes status as well as intratumoral and peritumoral immune infiltrating cells populations in esophageal mucosa samples obtained from neoadjuvant therapy-naïve patients who had esophagectomy for EAC.

Results

Vascular invasion and high infiltration of lamina propria mononuclear cells resulted associated with nodal metastasis. Low infiltration of activated CD8⁺ CD28⁺ T cells was observed in both intratumoral and peritumoral mucosa of patients with nodal metastasis. Low levels of CD69, MYD88, and TLR4 transcripts were detected in the intratumoral specimen of patients with lymph node involvement. Receiver operating characteristic curve analysis showed good accuracy for detecting nodal metastasis for all the markers tested. Significant lower infiltration of CD8 T cells and M1 macrophages and a lower expression of CD8A, CD8B, and TBX21 were found also in Esophageal Adenocarcinoma TCGA panCancer Atlas in the normal tissue of patients with nodal metastasis.

Conclusions

Our data suggest that immune surveillance failure is the main driver of nodal metastasis onset. Moreover, nodal metastasis containment also involves the immune microenvironment of the peritumoral healthy tissue.

SUBMITTER: Kotsafti A

PROVIDER: S-EPMC10028023 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Tumor immune microenvironment in therapy-naive esophageal adenocarcinoma could predict the nodal status.

Kotsafti Andromachi A Fassan Matteo M Cavallin Francesco F Angerilli Valentina V Saadeh Luca L Cagol Matteo M Alfieri Rita R Pilati Pierluigi P Castoro Carlo C Castagliuolo Ignazio I Scarpa Melania M Scarpa Marco M

Cancer medicine 20221025 5

<h4>Background</h4>Currently, preoperative staging of esophageal adenocarcinoma (EAC) has modest reliability and accuracy for pT and pN stages prediction, which heavily affects overall survival. The interplay among immune checkpoints, oncogenes, and intratumoral and peritumoral immune infiltrating cells could be used to predict loco-regional metastatic disease in early EAC.<h4>Methods</h4>We prospectively evaluated immune markers expression and oncogenes status as well as intratumoral and peritu ...[more]

PMID: 36281585

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Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

Dataset Information

Tumor immune microenvironment in therapy-naive esophageal adenocarcinoma could predict the nodal status.

Background

Methods

Results

Conclusions

Publications

Tumor immune microenvironment in therapy-naive esophageal adenocarcinoma could predict the nodal status.

Similar Datasets

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