Id protein family consists of four members namely Id-1 to Id-4. Different from other basic helix -loop -helix transcription factors, they lack the DNA binding domain. Id proteins have been shown to be dysregulated in many different cancer types and their prognostic value has also been demonstrated. Recently, Id-1 has been shown to be upregulated in oesophageal squamous cell carcinoma (ESCC). However, the prognostic implications of Id proteins in ESCC have not been reported. We examined the expression of the Id proteins in ESCC cell lines and clinical ESCC specimens and found that Id protein expressions were dysregulated in both the ESCC cell lines and specimens. By correlating the expression levels of Id proteins and the clinicopathological data of our patient cohort, we found that M1 stage tumours had significantly higher nuclear Id-1 expression (P ¼ 0.012) while high nuclear Id-1 expression could predict development of distant metastasis within 1 year of oesophagectomy (P ¼ 0.005). In addition, high levels of Id-2 expression in both cytoplasmic and nuclear regions predicted longer patient survival (P ¼ 0.041). Multivariate analysis showed that high-level expression of Id-2 in both cytoplasmic and nuclear regions and lower level of nuclear Id-1 expression were independent favourable predictors of survival in our ESCC patients. Our results suggest that Id-1 may promote distant metastasis in ESCC, and both Id-1 and Id-2 may be used for prognostication for ESCC patients.

Thirty seven cases of oesophageal squamous cell carcinoma were studied by applying DNA slot blot analysis and in situ hybridisation using type specific probes for HPV 6, 11, 16 and 18. Cases of condyloma accuminata, cervical carcinoma, and laryngeal papilloma were used as controls. Blocks including areas of invasive carcinoma, intraepithelial neoplasia, and normal epithelium were studied in each case. No HPV genome was detectable in any of the oesophageal cases. It is concluded that these types of HPV do not have an association with oesophageal squamous cell carcinoma.

This study aims to develop a liquid biopsy assay to identify HCC and differentially diagnose hepatocellular carcinoma (HCC) from colorectal carcinoma (CRC) liver metastasis. Methods: Thirty-two microRNAs ("HallMark-32" panel) were designed to target the ten cancer hallmarks in HCC. Quantitative PCR and supervised machine learning models were applied to develop an HCC-specific diagnostic model. One hundred thirty-three plasma samples from intermediate-stage HCC patients, colorectal cancer (CRC) patients with liver metastasis, and healthy individuals were examined. Results: Six differentially expressed microRNAs ("Signature-Six" panel) were identified after comparing HCC and healthy individuals. The microRNA miR-221-3p, miR-223-3p, miR-26a-5p, and miR-30c-5p were significantly down-regulated in the plasma of HCC samples, while miR-365a-3p and miR-423-3p were significantly up-regulated. Machine learning models combined with HallMark-32 and Signature-Six panels demonstrated promising performance with an AUC of 0.85-0.96 (p ≤ 0.018) and 0.84-0.93 (p ≤ 0.021), respectively. Further modeling improvement by adjusting sample quality variation in the HallMark-32 panel boosted the accuracy to 95% ± 0.01 and AUC to 0.991 (95% CI 0.96-1, p = 0.001), respectively. Even in alpha fetoprotein (AFP)-negative (< 20ng/mL) HCC samples, HallMark-32 still achieved 100% sensitivity in identifying HCC. The Cancer Genome Atlas (TCGA, n=372) analysis demonstrated a significant association between HallMark-32 and HCC patient survival. Conclusion: To the best of our knowledge, this is the first report to utilize circulating miRNAs and machine learning to differentiate HCC from CRC liver metastasis. In this setting, HallMark-32 and Signature-Six are promising non-invasive tests for HCC differential diagnosis and distinguishing HCC from healthy individuals.

Purpose: This study aims to develop liquid biopsy assays for early HCC diagnosis and prognosis. Methods: Twenty-three microRNAs were first consolidated as a panel (HCCseek-23 panel) based on their reported functions in HCC development. Serum samples were collected from 103 early-stage HCC patients before and after hepatectomy. Quantitative PCR and machine learning random forest models were applied to develop diagnostic and prognostic models. Results: For HCC diagnosis, HCCseek-23 panel demonstrated 81% sensitivity and 83% specificity for identifying HCC in the early-stage; it showed 93% sensitivity for identifying alpha-fetoprotein (AFP)-negative HCC. For HCC prognosis, the differential expressions of 8 microRNAs were significantly associated with disease-free survival (DFS) (Log-rank test p-value = 0.001). Further model improvement using these HCCseek-8 panel in combination with serum biomarkers (i.e. AFP, ALT, and AST) demonstrated a significant association with DFS (Log-rank p-value = 0.011 and Cox proportional hazards analyses p-value = 0.002). Conclusion:To the best of our knowledge, this is the first report to integrate circulating miRNAs, AST, ALT, AFP, and machine learning for predicting DFS in early HCC patients undergoing hepatectomy. In this setting, HCCSeek-23 panel is a promising circulating microRNA assay for diagnosis, while HCCSeek-8 panel is promising for prognosis to identify early HCC recurrence.