Accumulating studies have demonstrated the indispensable roles of exosomes and long non-coding RNAs (lncRNAs) in cancer progression and the tumor microenvironment (TME). However, the clinical relevance of exosome-related lncRNAs (ER-lncRNAs) in esophageal squamous cell carcinoma (ESCC) remains unclear. Three subtypes were identified by consensus clustering of 3459 valid ER-lncRNA pairs, of which subtype A is preferentially related to favorable prognosis, lower stromal and immune scores, and higher tumor purity scores. Higher immune cell infiltration, higher mRNA levels of immune checkpoints, higher stromal and immune scores, and lower tumor purity were found in subtype C, which presented a poor prognosis. We developed a prognostic risk score model based on 8 ER-lncRNA pairs in the GEO cohort using univariate Cox regression analysis and LASSO Cox regression analysis. Patients were divided into a high risk-score group and low risk-score group by the cut-off values of the 1-year ROC curves in the training set (GEO cohort) and the validation set (TCGA cohort). Receiver operating characteristic (ROC) curves, Decision curve analysis (DCA), clinical correlation analysis, and univariate and multivariate Cox regression all confirmed that the prognostic model has good predictive power and that the risk score can be used as an independent prognostic factor in different cohorts. By further analyzing the TME based on the risk model, higher immune cell infiltration and more active TME were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher mRNA levels of immune checkpoints and lower IC50 values, indicating that these patients may be more prone to profit from chemotherapy and immunotherapy. The top five most abundant microbial phyla in ESCC was also identified. The best ER-lncRNAs (AC082651.3, AP000487.1, PLA2G4E-AS1, C8orf49 and AL356056.2) were identified based on machine learning algorithms. Subsequently, the expression levels of the above ER-lncRNAs were analyzed by combining the GTEx and TCGA databases. In addition, qRT-PCR analysis based on clinical samples from our hospital showed a high degree of consistency. This study fills the gap of ER-lncRNA model in predicting the prognosis of patients with ESCC and the risk score-based risk stratification could facilitate the determination of therapeutic option to improve prognoses.
Lactic acid, formerly thought of as a byproduct of glycolysis or a metabolic waste produced, has now been identified as a key regulator of cancer growth, maintenance, and progression. However, the results of investigations on lactic acid metabolism-related long non-coding RNAs (LRLs) in esophageal squamous cell carcinoma (ESCC) remain inconclusive. In this study, univariate Cox regression analysis was carried out in the TCGA cohort, and 9 lncRNAs were shown to be significantly associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were then used in the GEO cohort. 6 LRLs were identified as independent prognostic factors for ESCC patients used to construct a prognostic risk-related signature subsequently. Two groups were formed based on the middle value of risk scores: a low-risk group and a high-risk group. Following that, we conducted Kaplan-Meier survival analysis, which revealed that the high-risk group had a lower survival probability than the low-risk group in both GEO and TCGA cohorts. On multivariate Cox regression analysis, the prognostic signature was shown to be independent prognostic factor, and it was found to be a better predictor of the prognosis of ESCC patients than the currently widely used grading and staging approaches. The established nomogram can be conveniently applied in the clinic to predict the 1-, 3-, and 5- year survival rates of patients. There was a significant link found between the 6 LRLs-based prognostic signature and immune-cell infiltration, tumor microenvironment (TME), tumor somatic mutational status, and chemotherapeutic treatment sensitivity in the study population. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 6 LRLs. In conclusion, we constructed a prognostic signature which could predict the prognosis and immunotherapy response of ESCC patients.
N7-Methylguanosine (m7G) and long non-coding RNAs (lncRNAs) have been widely reported to play an important role in cancer. However, there is little known about the relationship between m7G-related lncRNAs and esophageal squamous cell carcinoma (ESCC). In this study, we aimed to find new potential biomarkers and construct an m7G-related lncRNA prognostic signature for ESCC. Three molecular clusters were identified by consensus clustering of 963 m7G-related lncRNAs, of which cluster B is preferentially related to poorer prognosis, higher immune and stromal scores, higher mRNA levels of immune checkpoints, and higher immune infiltrate level. We constructed a robust and effective m7G-related lncRNA prognostic signature (m7G-LPS, including 7 m7G-related prognostic lncRNAs) and demonstrated its prognostic value and predictive ability in the GEO and TCGA cohorts. The risk score was able to serve as an independent risk factor for patients with ESCC and showed better prediction than the traditional clinical risk factors. The immune score, stromal score, the infiltration level of immune cells and expression of immune checkpoints were significantly higher in the high-risk subgroup compared to the low-risk subgroup. The establishment of nomogram further improved the performance of m7G-LPS and facilitated its clinical application. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 7 m7G-related lncRNAs. To a certain degree, m7G-lncRNAs can be used as prognostic markers and therapeutic targets for ESCC patients.
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