Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

Zhang, Zhe; Wang, Jian; Han, Wei; Zhao, Li

doi:10.1186/s12876-023-02688-5

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…73. Zhang et al ( 23 ) also used LncRNA to predict the prognosis of ESCC, the AUC values of the model in one year, three years and five years were 0.670, 0.749 and 0.757, respectively.…”

Section: Discussionmentioning

confidence: 99%

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Lu,

Yang,

Luo

et al. 2024

Front. Oncol.

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BackgroundIn contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.ObjectiveTo develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.MethodsThree ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.ResultsRSF’s C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability.ConclusionWe developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

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“…73. Zhang et al ( 23 ) also used LncRNA to predict the prognosis of ESCC, the AUC values of the model in one year, three years and five years were 0.670, 0.749 and 0.757, respectively.…”

Section: Discussionmentioning

confidence: 99%

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Lu,

Yang,

Luo

et al. 2024

Front. Oncol.

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Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer

Uttam,

Kapoor,

Rana

et al. 2024

Cancer Inform

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Objectives: Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients. Methods: To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed. Results: Utilizing a cutoff criterion (log2FC > 1 + log2FC < −1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value = .048), programed cell death ligand 2 (PD-L2; P-value = .002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value = .045) expression levels were significantly higher in low-risk patients compared to high-risk patients. Conclusion: We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future.

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LncRNA SSTR5-AS1 promotes esophageal carcinoma through regulating ITGB6/JAK1/STAT3 signaling

Tang,

Jiang,

Zong

et al. 2023

Preprint

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Background Esophageal carcinoma (ESCA) is the aggressive cancer which threatens people’s health. LncRNA SSTR5-AS1 is upregulated in ESCA. However, the potential regulatory mechanism of SSTR5-AS1 in ESCA is unknown. Methods GEPIA was used to explore the prognosis of SSTR5-AS1 in ESCA patients. EdU staining was used to detect cell proliferation. Transwell assay was applied for assessing cell invasion and migration. Meanwhile, RNA pull-down and RIP were applied to assess the relationship among SSTR5-AS1, EIF4A3 and ITGB6, and FISH was applied for exploring the localization of SSTR5-AS1 in ESCA cells. Results SSTR5-AS1 was upregulated in ESCA. SSTR5-AS1 downregulation repressed the invasion and migration of ESCA cells, and promoted cells apoptosis. Furthermore, SSTR5-AS1 shRNA upregulated the levels of Bax, cleaved caspase 3 and inhibited p-STAT3, p-JAK1 and Bcl-2 levels. SSTR5-AS1 was distributed in cytoplasm, and it could regulate ITGB6 by interacting with EIF4A3. SSTR5-AS1 silencing inhibited ITGB6 expression and inactivated JAK1/STAT3 signaling, while EIF4A3 upregulation reversed this phenomenon. In addition, SSTR5-AS1 silencing attenuated the malignant behavior of ESCA cells through ITGB6-mediated JAK1/STAT3 signaling. Conclusion SSTR5-AS1 promotes ESCA development through interacting with EIF4A3 to regulate ITGB6/JAK1/STAT3 signaling. Hence, this research supplied a basis for discovering strategies against ESCA.

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Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

Cited by 4 publications

References 53 publications

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer

LncRNA SSTR5-AS1 promotes esophageal carcinoma through regulating ITGB6/JAK1/STAT3 signaling

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