Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer

Replenishment of TCA cycle intermediates and long-noncoding RNAs regulation in breast cancer

The tricarboxylic acid (TCA) cycle is an essential interface that coordinates cellular metabolism and is as a primary route determining the fate of a variety of fuel sources, including glucose, fatty acid and glutamate. The crosstalk of nutrients replenished TCA cycle regulates breast cancer (BC) progression by changing substrate levels-induced epigenetic alterations, especially the methylation, acetylation, succinylation and lactylation. Long non-coding RNAs (lncRNA) have dual roles in inhibiting or promoting energy reprogramming, and so altering the metabolic flux of fuel sources to the TCA cycle, which may regulate epigenetic modifications at the cellular level of BC. This narrative review discussed the central role of the TCA cycle in interconnecting numerous fuels and the induced epigenetic modifications, and the underlying regulatory mechanisms of lncRNAs in BC.

Fatty acid metabolism-related lncRNAs are potential biomarkers for survival prediction in clear cell renal cell carcinoma

Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC.

Identification and experimental validation of Stearoyl-CoA desaturase is a new drug therapeutic target for osteosarcoma

Osteosarcoma (OS) is the most common malignant bone tumor. Fatty acid reprogramming plays an essential role in OS progression. However, new fatty acid related therapeutic targets of OS have not been completely elucidated. Therefore, we firstly identified 113 differentially expressed fatty acid metabolism genes using bioinformatic analysis, 19 of which were found to be associated with OS prognosis. Then, 7 hub genes were screened out and yielded a strong prediction accuracy (AUC value = 0.88, at 3 years) for predicting the survival status of OS patients. Furthermore, we confirmed that SCD was highly expressed in OS cells and patients. And Knock-down of SCD impaired proliferation and migration of OS cells. Moreover, SCD was transcriptionally activated by c-Myc to promote proliferation and migration of OS cells. Finally, SCD inhibitor could significantly induce OS ferroptosis in vitro and in vivo. In conclusion, we identified that SCD was a reliable risk factor for OS patients. And SCD was activated by c-Myc. The inhibitor of SCD could significantly impaired OS growth and induce OS ferroptosis, which indicated that SCD was a potential drug target for OS treatment.

LncRNAs-circRNAs as Rising Epigenetic Binary Superstars in Regulating Lipid Metabolic Reprogramming of Cancers

As one of novel hallmarks of cancer, lipid metabolic reprogramming has recently been becoming fascinating and widely studied. Lipid metabolic reprogramming in cancer is shown to support carcinogenesis, progression, distal metastasis, and chemotherapy resistance by generating ATP, biosynthesizing macromolecules, and maintaining appropriate redox status. Notably, increasing evidence confirms that lipid metabolic reprogramming is under the control of dysregulated non-coding RNAs in cancer, especially lncRNAs and circRNAs. This review highlights the present research findings on the aberrantly expressed lncRNAs and circRNAs involved in the lipid metabolic reprogramming of cancer. Emphasis is placed on their regulatory targets in lipid metabolic reprogramming and associated mechanisms, including the clinical relevance in cancer through lipid metabolism modulation. Such insights will be pivotal in identifying new theranostic targets and treatment strategies for cancer patients afflicted with lipid metabolic reprogramming.

Identification of necroptosis-related lncRNAs for prognosis prediction and screening of potential drugs in patients with colorectal cancer

BACKGROUND

Tumor recurrence and metastasis lead to a poor prognosis in colorectal cancer (CRC). Necroptosis is closely related to the tumor microenvironment (TME) and affects tumor recurrence and metastasis. We aimed to stratify CRC patients according to necroptosis-related long noncoding RNAs (lncRNAs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs.

AIM

To stratify CRC patients according to necroptosis-related lncRNAs (NRLs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs.

METHODS

LncRNA expression profiles were collected from The Cancer Genome Atlas. NRLs were identified by coexpression analysis. Cox regression analysis identified a NRL signature. Then, the value of this signature was comprehensively and multidimensionally evaluated, and its reliability for CRC prognosis prediction was assessed with clinical CRC data and compared with that of six other lncRNA signatures. Gene set enrichment analysis, TME analysis and half-maximal inhibitory concentration (IC₅₀) prediction were also performed according to the risk score (RS) of the signature.

RESULTS

An 8-lncRNA signature significantly associated with overall survival (OS) was constructed, and its reliability was validated with clinical CRC data. Most of the areas under the receiver operating characteristic curves (AUCs) values for 1-, 3- and 5-year OS for this signature were higher than those for the other six lncRNA signatures. OS, disease-specific survival and the progression-free interval were all significantly poorer in the high-risk group. The RS of the signature showed good concordance with the predicted prognosis, with AUCs for 1-, 3- and 5-year OS of 0.79, 0.81 and 0.77, respectively. Additionally, the calibration plots for this signature combined with clinical factors showed that this combination could effectively improve the ability to predict OS. The RS was correlated with tumor stage, lymph node metastasis and distant metastasis. Most of the enriched Kyoto Encyclopedia of Genes and Genomes and Gene Ontology terms were tumor metastasis-related pathways in the high-risk group; these patients showed greater infiltration of immunosuppressive cells, such as cancer-associated fibroblasts, hematopoietic stem cells and M2 macrophages, but less infiltration of infiltrating antitumor effector immune cells, such as cluster of differentiation 8⁺ T cells and regulatory T cells (Tregs). We explored additional potential immune checkpoint genes and potential immunotherapeutic and chemotherapeutic drugs with relatively low IC₅₀ values.

CONCLUSION

We identified an NRL signature with strong fidelity that could stably predict prognosis and might be an indicator of the TME of CRC. Furthermore, additional potential immunotherapeutic and chemotherapeutic drugs were explored.

Regulatory Role of Fatty Acid Metabolism-Related Long Noncoding RNA in Prostate Cancer: A Computational Biology Study Analysis

In elderly men, prostate cancer is a leading cause of death. Tumor cells require more energy to progress than normal cells, and this energy is mainly dependent on the large amount of ATP support generated by lipid metabolism. Therefore, in this study, we focused on long noncoding RNAs related to lipid metabolism in prostate cancer to discover the biological mechanisms of lipid metabolism regulation. The TCGA-PRAD cohort was used in this study for computational biology analysis. In lipid metabolism biological pathways, 1959 long noncoding RNAs were identified by Pearson correlation coefficient analysis of protein-coding genes, then univariate regression with P values fewer than 0.05. We further identified 784 lncRNAs that were lipid metabolism-related lncRNAs considered to have prognostic value for disease-free survival. Subsequently, we constructed two lncRNA expression patterns of lipid metabolism based on these lncRNAs by nonnegative matrix dimensionality reduction. These two expression patterns showed significant differences in disease-free survival curves for those diagnosed with prostate cancer. We found significant differences in mRNA surveillance pathway and mRNA processing between C1 and C2 groups based on the WGCNA method to explore the biological characteristics of these two expression patterns. Finally, we constructed a disease-free survival (PFS) model based on these lncRNAs. The results identified lncRNAs involved in lipid metabolism and revealed differences in their expression patterns. Additionally, the results offer candidate ideas and approaches concerning the precision treatment of prostate cancer by studying lipid metabolism by candidate long noncoding RNAs.

Evaluation of the ability of fatty acid metabolism signature to predict response to neoadjuvant chemoradiotherapy and prognosis of patients with locally advanced rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) is widely used to treat patients with locally advanced rectal cancer (LARC), and treatment responses vary. Fatty acid metabolism (FAM) is closely associated with carcinogenesis and cancer progression. In this study, we investigated the vital role of FAM on the gut microbiome and metabolism in the context of cancer. We screened 34 disease-free survival (DFS)-related, FAM-related, and radiosensitivity-related genes based on the Gene Expression Omnibus database. Subsequently, we developed a five-gene FAM-related signature using the least absolute shrinkage and selection operator Cox regression model. The FAM-related signature was also validated in external validation from Fujian Cancer Hospital for predicting nCRT response, DFS, and overall survival (OS). Notably, patients with a low-risk score were associated with pathological complete response and better DFS and OS outcomes. A comprehensive evaluation of the tumor microenvironment based on the FAM-related signature revealed that patients with high-risk scores were closely associated with activating type I interferon response and inflammation-promoting functions. In conclusion, our findings indicate the potential ability of FAM to predict nCRT response and the prognosis of DFS and OS in patients with LARC.

The role of noncoding RNAs in cancer lipid metabolism

Research on noncoding ribonucleic acids (ncRNAs) is mostly and broadly focused on microRNAs (miRNAs), cyclic RNAs (circRNAs), and long ncRNAs (lncRNAs), which have been confirmed to play important roles in tumor cell proliferation, invasion, and migration. Specifically, recent studies have shown that ncRNAs contribute to tumorigenesis and tumor development by mediating changes in enzymes related to lipid metabolism. The purpose of this review is to discuss the characterized ncRNAs involved in the lipid metabolism of tumors to highlight ncRNA-mediated lipid metabolism-related enzyme expression in malignant tumors and its importance to tumor development. In this review, we describe the types of ncRNA and the mechanism of tumor lipid metabolism and analyze the important role of ncRNA in tumor lipid metabolism and its future prospects from the perspectives of ncRNA biological function and lipid metabolic enzyme classification. However, several critical issues still need to be resolved. Because ncRNAs can affect tumor processes by regulating lipid metabolism enzymes, in the future, we can study the unique role of ncRNAs from four aspects: disease prevention, detection, diagnosis, and treatment. Therefore, in the future, the development of ncRNA-targeted therapy will become a hot direction and shoulder a major task in the medical field.

circCAPRIN1 interacts with STAT2 to promote tumor progression and lipid synthesis via upregulating ACC1 expression in colorectal cancer

BACKGROUND

Circular RNAs (circRNAs) generated by back-splicing of precursor mRNAs (pre-mRNAs) are often aberrantly expressed in cancer cells. Accumulating evidence has revealed that circRNAs play a critical role in the progression of several cancers, including colorectal cancer (CRC). However, the current understandings of the emerging functions of circRNAs in CRC lipid metabolism and the underlying molecular mechanisms are still limited. Here, we aimed to explore the role of circCAPRIN1 in regulating CRC lipid metabolism and tumorigenesis.

METHODS

circRNA microarray was performed with three pairs of tumor and non-tumor tissues from CRC patients. The expression of circRNAs were determined by quantitative PCR (qPCR) and in situ hybridization (ISH). The endogenous levels of circRNAs in CRC cells were manipulated by transfection with lentiviruses overexpressing or silencing circRNAs. The regulatory roles of circRNAs in the occurrence of CRC were investigated both in vitro and in vivo using gene expression array, RNA pull-down/mass spectrometry, RNA immunoprecipitation assay, luciferase reporter assay, chromatin immunoprecipitation analysis, and fluorescence in situ hybridization (FISH).

RESULTS

Among circRNAs, circCAPRIN1 was most significantly upregulated in CRC tissue specimens. circCAPRIN1 expression was positively correlated with the clinical stage and unfavorable prognosis of CRC patients. Downregulation of circCAPRIN1 suppressed proliferation, migration, and epithelial-mesenchymal transition of CRC cells, while circCAPRIN1 overexpression had opposite effects. RNA sequencing and gene ontology analysis indicated that circCAPRIN1 upregulated the expressions of genes involved in CRC lipid metabolism. Moreover, circCAPRIN1 promoted lipid synthesis by enhancing Acetyl-CoA carboxylase 1 (ACC1) expression. Further mechanistic assays demonstrated that circCAPRIN1 directly bound signal transducer and activator of transcription 2 (STAT2) to activate ACC1 transcription, thus regulating lipid metabolism and facilitating CRC tumorigenesis.

CONCLUSIONS

These findings revealed the oncogenic role and mechanism of circCAPRIN1 in CRC. circCAPRIN1 interacted with STAT2 to promote CRC tumor progression and lipid synthesis by enhancing the expression of ACC1. circCAPRIN1 may be considered as a novel potential diagnostic and therapeutic target for CRC patients.

Role of a lipid metabolism-related lncRNA signature in risk stratification and immune microenvironment for colon cancer

Background

Energy metabolism disorder, especially lipid metabolism disorder, is an important biological characteristic of colon cancer. This research sought to examine the association between lipid metabolism-related long non-coding RNAs (lncRNAs) and prognoses among colon cancer patients.

Methods

The transcriptome profile and clinical data of patients with colon cancer were retrieved from The Cancer Genome Atlas database. Using consensus clustering, cases were divided into two clusters and Kaplan–Meier analysis was executed to analyze differences in their prognoses. The gene set enrichment analysis (GSEA) was used to discover biological processes and signaling pathways. A lipid metabolism-related lncRNA prognostic model (lipid metabolism-LncRM) was created utilizing the least absolute shrinkage and selection operator (LASSO) regression. The tumor microenvironment was evaluated on the basis of the composition of immune and stromal cells.

Results

The patients in Cluster 2 were found to have a better prognosis and higher expression of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) relative to Cluster 1. The results of GSEA showed the enrichment of energy metabolism pathways in Cluster 2. LASSO regression was used to identify the five LncRNAs that were shown to be most substantially linked to patient prognosis. These were NSMCE1-DT, LINC02084, MYOSLID, LINC02428, and MRPS9-AS1. Receiver operating characteristic (ROC) curves and survival analysis illustrated that the lipid metabolism-LncRM had a significant prognostic value. Further analysis showed that high- and low-risk groups were significantly different in terms of clinical characteristics and immune cells infiltration.

Conclusions

Lipid metabolism-related lncRNAs could predict the prognoses and tumor microenvironment of colon cancer and might be important biomarkers relevant to immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01369-8.

A novel lipid metabolism-related lncRNA signature predictive of clinical prognosis in cervical cancer

Background: Cervical cancer (CC) is a serious threat to women populations worldwide. Lipid metabolism is believed to have modulating functions in cancer. Long non-coding RNAs (lncRNAs) are potential biomarkers for the different tumor prognosis. Our work aims at investigating the prognostic value of lipid metabolism-related lncRNAs in CC.

Methods: LncRNA expression profiling was conducted in 291 patients from The Cancer Genome Atlas (TCGA). Patient samples were randomly assigned to the training or testing set in a 3:2 ratio. A novel lipid metabolism-related five-lncRNA signature with prognostic value for CC was built through the univariate Cox regression, least absolute contraction and selection operator (LASSO) regression and multivariate Cox regression analyses, and was further evaluated by the Kaplan-Meier methods. Relevant analyses were also applied to identify the independent clinicopathological factors. GO and KEGG analyses were conducted to investigate the biological functions and molecular pathways. Immune infiltration analysis was included to probe the relationship between lncRNA signature and cancer cell microenvironment.

Results: The novel lipid metabolism-related five-lncRNA signature was confirmed to be predictive of overall survival (OS) in CC patients. Risk score, cancer stage, pregnancy, and BMI were validated as independent factors with prognostic value. GO and KEGG indicated that lipid metabolism participated in several tumor associated functions and pathways. Moreover, our results suggested that the five-lncRNA expression has potential link with tumor immune microenvironment.

Conclusion: In conclusion, we built an innovative prognostic risk signature based upon lipid metabolism-related lncRNAs. The five-lncRNA signature may be beneficial to provide novel potential therapeutic targets and improve personalized treatment strategies for CC patients in future clinical treatments.

A Potential Fatty Acid Metabolism-Related Gene Signature for Prognosis in Clear Cell Renal Cell Carcinoma

Simple Summary

Clear Cell Renal Cell Carcinoma (ccRCC) is the most common and aggressive subtype of renal cancer. Abnormal fatty acid metabolism (FAM) is reported to be strongly associated with multiple malignancies, yet there is limited research in ccRCC. In this manuscript, we reported the significant role of abnormal FAM in predicting the prognosis of ccRCC. Three independent clinical cohorts (TCGA, EMTAB and our clinical cohorts with prognostic profiles and gene expression data, including RNA-seq, microarray and RT-qPCR) were applied as training and two external validation cohorts. As a result, we successfully constructed and validated a novel FAM-related gene signature (FAMGS) and nomogram for the overall survival of patients with ccRCC. Additionally, our study further elucidated the potential immune relevance and molecular mechanisms of abnormal FAM and the signature. In conclusion, the novel FAMGS constructed in this study offered a promising prognostic tool in clinic and potential therapeutic targets for ccRCC patients.

Abstract

This study aims to explore the role of abnormal fatty acid metabolism (FAM) in ccRCC and construct a novel fatty acid metabolism-related gene signature (FAMGS) for prognosis. Three independent ccRCC cohorts, including The Cancer Genome Atlas, E-MTAB-1980 and our clinical cohort (including RNA-seq, microarray and RT-qPCR data), were applied as training and two independent validation cohorts. Firstly, FAM levels were found to be significantly decreased in ccRCC and correlated with degrees of malignancy, confirming the pivotal role of FAM in ccRCC. Applying the least absolute shrinkage and selection operator cox regression, we established a novel FAMGS for overall survival (OS). The FAMGS divided patients into low or high-risk groups in the training cohort and were successfully validated in both the EMTAB and our clinical validation cohorts. Additionally, the FAMGS serves as an independent risk factor for OS of ccRCC. Results of the immune cell abundance identifier (ImmuCellAI) algorithm and gene set variation analysis (GSVA) revealed that patients in the high-risk group have comprehensively impaired metabolism, including lipids, amino acids and tricarboxylic acid cycle-related pathways and a more immunosuppressive tumor microenvironment. In conclusion, our study constructed and validated a novel FAMGS, which may improve the risk stratification optimization and personalized management of ccRCC.

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

Gastrointestinal cancers (GICs) are high-incidence malignant tumors that seriously threaten human health around the world. Their complexity and heterogeneity make the classic staging system insufficient to guide patient management. Recently, competing endogenous RNA (ceRNA) interactions that closely link the function of protein-coding RNAs with that of non-coding RNAs, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), has emerged as a novel molecular mechanism influencing miRNA-mediated gene regulation. Especially, ceRNA networks have proven to be powerful tools for deciphering cancer mechanisms and predicting therapeutic responses at the system level. Moreover, abnormal gene expression is one of the critical breaking events that disturb the stability of ceRNA network, highlighting the role of molecular biomarkers in optimizing cancer management and treatment. Therefore, developing prognostic signatures based on cancer-specific ceRNA network is of great significance for predicting clinical outcome or chemotherapy benefits of GIC patients. We herein introduce the current frontiers of ceRNA crosstalk in relation to their pathological implications and translational potentials in GICs, review the current researches on the prognostic signatures based on lncRNA or circRNA-mediated ceRNA networks in GICs, and highlight the translational implications of ceRNA signatures for GICs management. Furthermore, we summarize the computational approaches for establishing ceRNA network-based prognostic signatures, providing important clues for deciphering GIC biomarkers.

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by “pRRophetic” R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.

Violacein switches off low molecular weight tyrosine phosphatase and rewires mitochondria in colorectal cancer cells

In the last decade, emerging evidence has shown that low molecular weight protein tyrosine phosphatase (LMWPTP) not only contributes to the progression of cancer but is associated with prostate low survival rate and colorectal cancer metastasis. We report that LMWPTP favors the glycolytic profile in some tumors. Therefore, the focus of the present study was to identify metabolic enzymes that correlate with LMWPTP expression in patient samples. Exploratory data analysis from RNA-seq, proteomics, and histology staining, confirmed the higher expression of LMWPTP in CRC. Our descriptive statistical analyses indicate a positive expression correlation between LMWPTP and energy metabolism enzymes such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN). In addition, we examine the potential of violacein to reprogram energetic metabolism and LMWPTP activity. Violacein treatment induced a shift of glycolytic to oxidative metabolism associated with alteration in mitochondrial efficiency, as indicated by higher oxygen consumption rate. Particularly, violacein treated cells displayed higher proton leak and ATP-linked oxygen consumption rate (OCR) as an indicator of the OXPHOS preference. Notably, violacein is able to bind and inhibit LMWPTP. Since the LMWPTP acts as a hub of signaling pathways that offer tumor cells invasive advantages, such as survival and the ability to migrate, our findings highlight an unexplored potential of violacein in circumventing the metabolic plasticity of tumor cells.

Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma

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.