<b><i>Background:</i></b> Primary liver cancer, around 90% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. <b><i>Summary:</i></b> Since the publication of <i>Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2017 Edition)</i> in 2018, additional high-quality evidence has emerged with relevance to the diagnosis, staging, and treatment of liver cancer in and outside China that requires the guidelines to be updated. The new edition <i>(2019 Edition)</i> was written by more than 70 experts in the field of liver cancer in China. They reflect the real-world situation in China regarding diagnosing and treating liver cancer in recent years. <b><i>Key Messages:</i></b> Most importantly, the new guidelines were endorsed and promulgated by the Bureau of Medical Administration of the National Health Commission of the People’s Republic of China in December 2019.

Circular RNAs (circRNAs) have been implicated in cancer progression through largely unknown mechanisms. Herein, we identify an N6-methyladenosine (m6A) modified circRNA, circNSUN2, frequently upregulated in tumor tissues and serum samples from colorectal carcinoma (CRC) patients with liver metastasis (LM) and predicts poorer patient survival. The upregulated expression of circNSUN2 promotes LM in PDX metastasis models in vivo and accelerates cancer cells invasion in vitro. Importantly, N6-methyladenosine modification of circNSUN2 increases export to the cytoplasm. By forming a circNSUN2/IGF2BP2/HMGA2 RNA-protein ternary complex in the cytoplasm, circNSUN2 enhances the stability of HMGA2 mRNA to promote CRC metastasis progression. Clinically, the upregulated expressions of circNSUN2 and HMGA2 are more prevalent in LM tissues than in primary CRC tissues. These findings elucidate that N6-methyladenosine modification of circNSUN2 modulates cytoplasmic export and stabilizes HMGA2 to promote CRC LM, and suggest that circNSUN2 could represent a critical prognostic marker and/or therapeutic target for the disease.

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The American Joint Committee on Cancer (AJCC) staging system is inadequate for an accurate prognosis in nasopharyngeal carcinoma (NPC). Thus, new biomarkers are under intense investigation. Here, we investigated whether the density of TILs could predict prognosis in NPC. First, we used 1490 cases of nasopharyngeal carcinoma samples from two independent cohorts to evaluate the density and distribution of tumor-infiltrating lymphocytes (TILs). Second, in one cohort, we assessed associations between TILs and clinical outcomes in 593 randomly selected samples (defined as the training set) and validated findings in the remaining 593 samples (defined as the validation set). Furthermore, we confirmed the prognostic value of TILs in a second independent cohort of 304 cases (defined as the independent set). Based on multivariable Cox regression analysis, we also established an effective prognostic nomogram including TILs to improve accuracy in predicting disease-free survival (DFS) for patients with nondisseminated NPC. We found that high TILs in the training set were significantly associated with favorable DFS [hazard ratio (HR) 0.41, 95% confidence interval (CI) 0.28-0.58, p < 0.001], overall survival (OS, HR 0.42, 95% CI 0.27-0.64, p < 0.001), distant metastasis-free survival (DMFS, HR 0.37, 95% CI 0.23-0.58, p < 0.001) and local-regional recurrent free survival (LRRFS, HR 0.43, 95% CI 0.25-0.73, p = 0.002). Multivariate analysis showed that TILs are an independent prognostic indicator for DFS in all cohorts. In summary, this study indicated that TILs may reflect the immunological heterogeneity of NPC and could represent a new prognostic biomarker.

Background Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. MethodsIn this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

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The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

Background: Collagen type VI alpha 1 (COL6A1) has been found to be dysregulated in several human malignancies. However, the role of COL6A1 in osteosarcoma (OS) progression remains largely unclear. Here, we aimed to explore the clinical significance and biological involvement of COL6A1 in the OS cell migration and invasion. Material and Methods: We used immunohistochemistry, qRT-PCR and western blot to detect the expression of COL6A1 in 181 OS patient samples. Chromatin immunoprecipitation (ChIP) and PCR were carried out to verify the regulatory interaction of p300, c-Jun and COL6A1 promoter. The invasion and migration function of COL6A1 in OS was detected in vitro and in vivo . RNA sequence was performed to detect the downstream pathway of COL6A1, and then co-immunoprecipitation (co-IP), ubiquitination assays and rescue experiments were performed to determine the regulatory effect of COL6A1 and signal transducers and activators of transcription (STAT1). Exosomes derived from OS cell lines were assessed for the ability to promote cancer progression by co-cultured assay and exosomes tracing. Results: COL6A1 was commonly upregulated in OS tissues, especially in lung metastasis tissues, which was associated with a poor prognosis. c-Jun bound p300 increased the enrichment of H3K27ac at the promoter region of the COL6A1 gene, which resulted in the upregulation of COL6A1 in OS. Overexpression of COL6A1 promoted OS cell migration and invasion via interacting with SOCS5 to suppress STAT1 expression and activation in an ubiquitination and proteasomal degradation manner. Most interestingly, we found that exosomal COL6A1 derived from OS cells convert normal fibroblasts to cancer-associated fibroblasts (CAFs) by secreting pro-inflammatory cytokines, including IL-6 and IL-8. The activated CAFs could promote OS cell invasion and migration by mediating TGF-β/COL6A1 signaling pathway. Conclusion: Our data demonstrated that upregulation of COL6A1 activated by H3K27 acetylation promoted the cell migration and invasion by suppressing STAT1 pathway in OS cells. Moreover, COL6A1 can be packaged into OS cell-derived exosomes and activate CAFs to promote OS metastasis.