Esophageal Cancer Staging

Marom, G.

doi:10.1016/j.thorsurg.2022.06.006

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Although the prognosis of ESCC has improved in recent years with development of multimodality approaches, there is still lack of effective treatment approach for ESCC ( Kakeji et al, 2021 ; Wang et al, 2022 ). Conventional clinical parameter such as TNM stage is often used to predict prognosis of ESCC ( Marom, 2022 ). However, due to the significant tumor heterogeneity, the predictive accuracy of it is limited ( Mahar et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

Liang

Tan

et al. 2023

Front. Pharmacol.

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Background: The tricarboxylic acid cycle (TCA cycle) is an important metabolic pathway and closely related to tumor development. However, its role in the development of esophageal squamous cell carcinoma (ESCC) has not been fully investigated.Methods: The RNA expression profiles of ESCC samples were retrieved from the TCGA database, and the GSE53624 dataset was additionally downloaded from the GEO database as the validation cohort. Furthermore, the single cell sequencing dataset GSE160269 was downloaded. TCA cycle-related genes were obtained from the MSigDB database. A risk score model for ESCC based on the key genes of the TCA cycle was built, and its predictive performance was evaluated. The association of the model with immune infiltration and chemoresistance were analyzed using the TIMER database, the R package “oncoPredict” score, TIDE score and so on. Finally, the role of the key gene CTTN was validated through gene knockdown and functional assays.Results: A total of 38 clusters of 8 cell types were identified using the single-cell sequencing data. The cells were divided into two groups according to the TCA cycle score, and 617 genes were identified that were most likely to influence the TCA cycle. By intersecting 976 key genes of the TCA cycle with the results of WGCNA, 57 genes significantly associated with the TCA cycle were further identified, of which 8 were screened through Cox regression and Lasso regression to construct the risk score model. The risk score was a good predictor of prognosis across subgroups of age, N, M classification and TNM stage. Furthermore, BI-2536, camptothecin and NU7441 were identified as possible drug candidates in the high-risk group. The high-risk score was associated with decreased immune infiltration in ESCC, and the low-risk group had better immunogenicity. In addition, we also evaluated the relationship between risk scores and immunotherapy response rates. Functional assays showed that CTTN may affect the proliferation and invasion of ESCC cells through the EMT pathway.Conclusion: We constructed a predictive model for ESCC based on TCA cycle-associated genes, which achieved good prognostic stratification. The model are likely associated with the regulation of tumor immunity in ESCC.

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Section: Discussionmentioning

confidence: 99%

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

Liang

Tan

et al. 2023

Front. Pharmacol.

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“…Traditionally, CT and endoscopic ultrasound (EUS) were the standard modalities employed for the staging of esophageal cancer [11]. However, improved diagnostic accuracy from the routine incorporation of PET-CT imaging is supported by several series and has become a key tool in the staging of locally advanced esophageal cancer [12].…”

Section: Staging For Localized Diseasementioning

confidence: 99%

Clinical Utility of 18F-2-Fluoro-deoxy-d-glucose PET Imaging in Locally Advanced Esophageal/Gastroesophageal Junction Adenocarcinoma

Cowzer

Keane

2023

Diagnostics

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Esophageal adenocarcinoma, including adenocarcinoma of the gastroesophageal junction, is uncommon in the United States, but is associated with a rising incidence in young adults, and has a traditionally poor prognosis. Despite the incremental benefits that have been made with multimodality approaches to locally advanced disease, most patients will go on to develop metastatic disease, and long-term outcomes remain suboptimal. Over the last decade, PET-CT has emerged as a key tool in the management of this disease, with several prospective and retrospective studies evaluating its role in this disease. Herein, we review the key data pertaining to the use of PET-CT in the management of locally advanced esophageal and GEJ adenocarcinoma, with a focus on staging, prognostication, PET-CT adapted therapy in the neoadjuvant setting, and surveillance.

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“…The TNM classi cation system, as de ned by the Union for International Cancer Control (UICC), is currently the gold standard for clinical staging of cancer worldwide 1 . Both TNM and Japanese systems categorize T1N1 and T2N1 subsets of gastric cancer as stages IB and IIA 2,3 , respectively; whereas T1-2N1 esophageal cancer is staged correspondingly as IIB and II 4,5 . Thus, inconsistencies are apparent in the pathologic staging of cancer for various organs.…”

Section: Introductionmentioning

confidence: 99%

Long-term prognosis of patients with pT1-2 colorectal cancer unaffected by lymph node metastasis

Song,

Wang,

Chen

et al. 2024

Preprint

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Background/Aim: Our goal was to investigate patients with pT1-2 colorectal cancer (CRC) in terms of lymph node metastasis (LNM) and its clinical implications, perhaps questioning the staging of such tumors. Patients and Methods: This retrospective cohort study took place at a high-volume cancer center in Japan. We stratified patients with pT1-2 CRC (n=1288) by presence (LNM+) or absence (LNM-) of LNM, assessing overall (OS), cancer-specific (CSS), and relapse-free (RFS) survival rates in both groups before and after propensity score matching (PSM). COX multivariate analysis served for screening of prognostic risk factors. Results: Lymph node metastasis was ultimately confirmed in 256 study subjects (19.9%). Before matching, tumors of the LNM+ (vs LNM-) group were more inclined to be fairly large (≥2 cm: 76.6% vs 61.2%; p<0.001), with greater propensity for infiltrating or ulcerative features (55.1% vs 36.2%; p<0.001) and histotypes of lesser differentiation (Mod/Poor/Sig/Muc: 65.6% vs 45.8%; p<0.001). Likewise, they showed greater tendency for aggressive growth (91.1% vs 81.1%; p<001), lymphatic (44.5% vs 19.4%; p<0.001) or vascular (59% vs 35.1%; p<0.001) invasion, and prolific lymph node harvesting (23.6±12.2 vs 21.7±12.3; p=0.025). Although similar in terms of OS (LNM-, 94.2%; LNM+, 91.8%; p=0.339), the LNM- (vs LNM+) group displayed significantly better CSS (99.5% vs 96.9%; p<0.001) and RFS (97.2% vs 89.5%; p<0.001). After matching, RFS still proved significantly better in the LNM- (vs LNM+) group (95.9% vs 89.8%; p=0.016), with multivariate analysis identifying LNM+ as an independent risk factor for RFS before and after PSM. A higher recurrence rate was also evident in the LNM+ (vs LNM-) group (before matching: 10.5% vs 2.8%, [p<0.001]; after matching: 10.2% vs 4.1% [p=0.008]), involving liver and lymph nodes primarily. Neither OS nor CSS differed significantly by group. Conclusion: In patients with pT1-2N+ CRC, we found greater risk of hepatic or nodal recurrence, compared with node-negative counterparts. However, long-term survival was unaffected. Appropriate downstaging of pT1-2N+ CRC from stage IIIA is therefore a reasonable prospect.

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Esophageal Cancer Staging

Cited by 7 publications

References 24 publications

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

Clinical Utility of 18F-2-Fluoro-deoxy-d-glucose PET Imaging in Locally Advanced Esophageal/Gastroesophageal Junction Adenocarcinoma

Long-term prognosis of patients with pT1-2 colorectal cancer unaffected by lymph node metastasis

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