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Backgrounds/Aims Intrahepatic cholangiocarcinoma (ICC) is a highly desmoplastic tumor with poor prognosis even after curative resection. We investigated the associations between the composition of the ICC stroma and immune cell infiltration and aimed to develop a stromal-immune signature to predict prognosis in surgically treated ICC. Methods We recruited 359 ICC patients and performed immunohistochemistry to detect α-smooth muscle actin (α-SMA), CD3, CD4, CD8, Foxp3, CD68, and CD66b. Aniline was used to stain collagen deposition. Survival analyses were performed to detect prognostic values of these markers. Recursive partitioning for a discrete-time survival tree was applied to define a stromal-immune signature with distinct prognostic value. We delineated an integrated stromal-immune signature based on immune cell subpopulations and stromal composition to distinguish subgroups with different recurrence-free survival (RFS) and overall survival (OS) time. Results We defined four major patterns of ICC stroma composition according to the distributions of α-SMA and collagen: dormant (α-SMAlow/collagenhigh), fibrogenic (α-SMAhigh/collagenhigh), inert (α-SMAlow/collagenlow), and fibrolytic (α-SMAhigh/collagenlow). The stroma types were characterized by distinct patterns of infiltration by immune cells. We divided patients into six classes. Class I, characterized by high CD8 expression and dormant stroma, displayed the longest RFS and OS, whereas Class VI, characterized by low CD8 expression and high CD66b expression, displayed the shortest RFS and OS. The integrated stromal-immune signature was consolidated in a validation cohort. Conclusions We developed and validated a stromal-immune signature to predict prognosis in surgically treated ICC. These findings provide new insights into the stromal-immune response to ICC.

Abstract Patients with hepatocellular carcinoma (HCC) have poor long-term survival following curative resection because of the high rate of tumor early recurrence. Little is known about the trajectory of genomic evolution from primary to early-recurrent HCC. In this study, we performed whole-genome sequencing (WGS) on 40 pairs of primary and early-recurrent hepatitis B virus (HBV)-related HCC tumors from patients who received curative resection, and from four patients whose primary and recurrent tumor were extensively sampled. We identified two recurrence patterns: de novo recurrence (18/40), which developed genetically independently of the primary tumor and carried different HCC drivers, and ancestral recurrence (22/40), which was clonally related to the primary tumor and progressed more rapidly than de novo recurrence. We found that the recurrence location was predictive of the recurrence pattern: distant recurrence tended to display the de novo pattern, whereas local recurrence tended to display the ancestral pattern. We then uncovered the evolutionary trajectories based on the subclonal architecture, driver-gene mutations, and mutational processes observed in the primary and recurrent tumors. Multi-region WGS demonstrated spatiotemporal heterogeneity and polyclonal, monophyletic dissemination in HCC ancestral recurrence. In addition, we identified recurrence-specific mutations and copy-number gains in BCL9, leading to WNT/β-catenin signaling activation and an immune-excluded tumor microenvironment, which suggests that BCL9 might serve as a new therapeutic target for recurrent HCC. Collectively, our results allow us to view with unprecedented clarity the genomic evolution during HBV-related HCC early recurrence, providing an important molecular foundation for enhanced understanding of HCC with implications for personalized therapy to improve patient survival.

Abstract Background Plasmacytoid dendritic cells (pDCs) are present in various primary and metastatic human neoplasms; however, their clinical significance in intrahepatic cholangiocarcinoma is not clear. Methods To evaluate pDCs’ distributions in and around tumors as well as their potential function and predictive value for prognosis in patients undergoing curative resection, we performed immunohistochemistry to examine the expression of pDC marker BDCA2, and CD3, CD4, CD8 and Foxp3 in intratumoral and peritumoral tissues from 359 patients with intrahepatic cholangiocarcinoma and compared with prognostic and clinicopathologic factors. Results Results showed that patients with high numbers of BDCA2+ pDCs in peritumoral tissues were more likely to have elevated levels of carbohydrate antigen 19-9 and gamma-glutamyl transferase, larger and more tumors, advanced tumor-node-metastasis staging, more vascular/bile duct invasion, and lymphatic metastasis in association with greater chance of recurrence and shorter overall survival. Peritumoral tissues with larger numbers of pDCs also showed increased Foxp3+ regulatory T cell infiltration, both of which were found to be independent factors for predicting time to recurrence and overall survival. By contrast, patient outcomes were not associated with the presence of intratumoral pDCs. Conclusions Peritumoral pDC infiltration may indicate an immune tolerogenic peritumor microenvironment and can be used to predict a poor prognosis for patients undergoing curative resection for intrahepatic cholangiocarcinoma.

Abstract Background Long noncoding RNAs (lncRNAs) are functionally associated with cancer development and progression. Although gene copy number variation (CNV) is common in hepatocellular carcinoma (HCC), it is not known how CNV in lncRNAs affects HCC progression and recurrence. We aimed to identify a CNV‐related lncRNA involved in HCC progression and recurrence and illustrate its underlying mechanisms and prognostic value. Methods We analyzed the whole genome sequencing (WGS) data of matched cancerous and noncancerous liver samples from 49 patients with HCC to identify lncRNAs with CNV. The results were validated in another cohort of 238 paired HCC and nontumor samples by TaqMan copy number assay. We preformed Kaplan‐Meier analysis and log‐rank test to identify lncRNA CNV with prognostic value. We conducted loss‐ and gain‐of‐function studies to explore the biological functions of LINC01133 in vitro and in vivo. The competing endogenous RNAs (ceRNAs) mechanism was clarified by microRNA sequencing (miR‐seq), quantitative real‐time PCR (qRT‐PCR), western blot, and dual‐luciferase reporter assays. We confirmed the binding mechanism between lncRNA and protein by RNA pull‐down, RNA immunoprecipitation, qRT‐PCR, and western blot analyses. Results Genomic copy numbers of LINC01133 were increased in HCC, which were positively related with the elevated expression of LINC01133. Increased copy number of LINC01133 predicted the poor prognosis in HCC patients. LINC01133 overexpression in HCC cells promoted proliferation and aggressive phenotypes in vitro, and facilitated tumor growth and lung metastasis in vivo, whereas LINC01133 knockdown had the opposite effects. LINC01133 sponged miR‐199a‐5p, resulting in enhanced expression of SNAI1, which induced epithelial‐to‐mesenchymal transition (EMT) in HCC cells. In addition, LINC01133 interacted with Annexin A2 (ANXA2) to activate the ANXA2/STAT3 signaling pathway. Conclusions LINC01133 promotes HCC progression by sponging miR‐199a‐5p and interacting with ANXA2. LINC01133 CNV gain is predictive of poor prognosis in patients with HCC.

Due to the poor underground environment, which makes the images of underground video in coal mines seriously degraded. However, the existing semantic segmentation model based on deep learning has the problem of fuzzy edge segmentation after image sharpness. A new method is proposed, which uses the fusion edge optimization module to process the boundary information and uses the gated convolution layer to connect the traditional feature extraction module to process the information in parallel. In order to supervise the learning of contour information, the binary cross entropy loss function was used to improve the learning effect, and the segmentation effect was optimized together with the loss function of conventional branches. The experimental results show that compared with other methods, the method based on the fusion edge optimization module improves the overall semantic segmentation accuracy and edge segmentation accuracy when the semantic segmentation task of the clarified coal mine image is performed.