The transcriptional landscape and diagnostic potential of long non-coding RNAs in esophageal squamous cell carcinoma

Novel milestones for early esophageal carcinoma: From bench to bed

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.

Computational identification of DNA damage-relevant lncRNAs for predicting therapeutic efficacy and clinical outcomes in cancer

OBJECTIVES

The role of long non-coding RNAs (lncRNAs) in cancer treatment, particularly in modulating DNA repair programs, is an emerging field that warrants systematic exploration. This study aimed to systematically identify the lncRNA regulators that potentially regulate DNA damage response (DDR).

METHODS

Using genome-wide mRNA and lncRNA expression profiles of the same tumor patients, we proposed a novel computational framework. This framework performed Gene Set Variation Analysis to calculate DDR pathway enrichment score, which relies on weighting by tumor purity to obtain DDR activity score for each patient. Then, an in-depth differential expression profiling was conducted to identify pathway activity lncRNAs between high- and low-activity groups, utilizing a bootstrap-based randomization method.

RESULTS

We comprehensively charted the landscape of DDR-relevant lncRNAs across 23 epithelial-based cancer types. Its effectiveness was validated by assessing the consistency of these lncRNAs within various datasets of the same cancer type (hypergeometric test P < 0.001), examining the expression perturbation of these lncRNAs in response to treatment and demonstrating its application in prioritizing clinically-related lncRNAs. Furthermore, leveraging 820 epithelial ovarian cancer patients from four public datasets, we applied these lncRNAs identified by DDRLnc to establish and validate a risk stratification model to evaluate the benefits of platinum-based adjuvant chemotherapy for the improvement of clinical treatment outcomes.

CONCLUSIONS

Comprehensive pan-cancer analysis illustrates the utility of computational framework in identifying potentially lncRNAs involved in DDR, thereby offering novel insights into the complex realm of cancer research, and it will become a valuable tool for identifying potential therapeutic targets for cancer.

Genetic Impact of HOTAIR, LINC00951, POLR2E and HULC Polymorphisms in Histopathological and Laboratory Prognostic Factors in Esophageal Cancer in the West: A Case-Control Study

Long non-coding RNAs' HOTAIR rs920778, LINC00951 rs11752942, POLR2E rs3787016, and HULC rs7763881 are progressively reported having a close genetic affinity with esophageal carcinogenesis in the East. Nonetheless, their correlation with variables already endorsed as significant prognostic factors in terms of staging, guiding treatment and predicting recurrence, metastasis, and survival have yet to be explored. Herein, we investigated their prognostic value by correlating them with clinicopathological and laboratory prognostic markers in esophageal cancer in the West. Formalin-fixed paraffin-embedded tissue specimens from 95 consecutive patients operated on for esophageal cancer between 2014 and 2018 were compared with 121 healthy community controls. HULC was not detected differently in any of the cancer prognostic subgroups. LINC00951 was underrepresented in Ca19.9 elevated subgroup. HOTAIR was more frequent in both worse differentiation grade and positive Signet-Ring-Cell and Ca19.9 subgroups. POLR2E was identified less frequently in Adenocarcinoma, Signet-Ring-Cell, and Diffuse histologies, as well as in Perineural, Lymphovascular, and Perivascular Invasion positive, while it was overrepresented in CEA positive subgroup. These lncRNAs polymorphisms may hold great potential not only as future therapeutic agents but also as novel markers for predictive analysis of esophageal cancer risk, clinical outcome, and survival. Clinical implications of these findings need to be validated with prospective larger sample-size studies.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

DNA damage repair profiling of esophageal squamous cell carcinoma uncovers clinically relevant molecular subtypes with distinct prognoses and therapeutic vulnerabilities

BACKGROUND

DNA damage repair (DDR) is a critical process that maintains genomic integrity and plays essential roles at both the cellular and organismic levels. Here, we aimed to characterize the DDR profiling of esophageal squamous cell carcinoma (ESCC), investigate the prognostic value of DDR-related features, and explore their potential for guiding personalized treatment strategies.

METHODS

We analyzed bulk and single-cell transcriptomics data from 377 ESCC cases from our institution and other publicly available cohorts to identify major DDR subtypes. The heterogeneity in cellular and functional properties, tumor microenvironment (TME) characteristics, and prognostic significance of these DDR subtypes were investigated using immunogenomic analysis and in vitro experiments. Additionally, we experimentally validated a combinatorial immunotherapy strategy using syngeneic mouse models of ESCC.

FINDINGS

DDR alteration profiling enabled us to identify two distinct DDR subtypes, DDRactive and DDRsilent, which exhibited independent prognostic values in locoregional ESCC but not in metastatic ESCC. The DDRsilent subtype was characterized by an inflamed but immune-suppressed microenvironment with relatively high immune cell infiltration, abnormal immune checkpoint expression, T-cell exhaustion, and enrichment of cancer-related pathways. Moreover, DDR subtyping indicates that BRCA1 and HFM1 are robust and independent prognostic factors in locoregional ESCC. Finally, we proposed and verified that the concomitant triggering of GITR or blockade of BTLA with PD-1 blockade or cisplatin chemotherapy represents effective combination strategies for high-risk locoregional ESCC tumors.

INTERPRETATION

Our discovery of DDR-based molecular subtypes will enhance our understanding of tumor heterogeneity and have significant clinical implications for the therapeutic and management strategies of locoregional ESCC.

FUNDING

This study was supported by the National Key R&D Program of China (2021YFC2501000, 2022YFC3401003), National Natural Science Foundation of China (82172882), the Beijing Natural Science Foundation (7212085), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091), and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT310027).