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Background: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. Methods: We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. Results: The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. Conclusions: We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds. [ABSTRACT FROM AUTHOR]

We take Graph $g={9.8}\ \textrm {m}\ \textrm {s}^{-2}$, Graph $\alpha ={1\times 10^{-4}}\ \textrm {K}^{{-1}}$ (Sohail I et al. i 2018), Graph $L=1\times 10^{7}\ \textrm {m}$, Graph $\varDelta ={10}\ \textrm {K}$, Graph $\kappa ={1\times 10^{-7}}\ \textrm {m}^2\ \textrm {s}^{-1}$ and Graph $\nu =1\times 10^{-6}\ \textrm {m}^2\ \textrm {s}^{-1}$ (Siggers, Kerswell & Balmforth 2004). The normalized height of topography Graph $h/H$ corresponds to Graph $0$, Graph $0.25$, Graph $0.5$ and Graph $0.75$ in figure 3( I a i - I d i ). Furthermore, as shown in figures 5 and 8, both Graph $\phi \tau$ and Graph $Nu$ increase as Graph $\omega$ decreases, and note that Graph $Nu$ is proportional to Graph $G(APE)$. In the current study, Graph $Ra$, Graph $Pr$ and Graph $\varGamma$ are fixed as Graph $Ra=1\times 10^{10}$, Graph $Pr=8.0$ and Graph $\varGamma =0.2$. Because Graph $\phi i$ is small compared with Graph $G(APE)$ for sufficiently large Graph $Ra$ (Peltier & Caulfield 2003; Gayen I et al. i 2013), Graph $\eta '$ asymptotically approaches its upper limit of 1 as Graph $Ra$ increases. [Extracted from the article]

Circular RNAs (circRNAs) are a class of regulatory RNAs with complex roles in healthy and diseased tissues. However, the oncogenic role of circRNAs in hepatocellular carcinoma (HCC) remains poorly understood, including the mechanisms by which the circular ubiquitin-binding associated protein 2 (circUBAP2) contributes to tumorigenesis. We analyzed the expression of circUBAP2 in 20 paired samples of HCC and healthy tissue as well as in seven HCC cell lines via quantitative real-time polymerase chain reaction. Functional experiments, such as CCK8 viability assays, colony formation assays, wound healing, transwell assays and flow cytometry, were conducted to assess the effects of circUBAP2 in vitro. To further elucidate the mechanisms by which circUBAP2 acts, we conducted dual-luciferase assays, western blots, RNA pull-down assays and rescue experiments. CircUBAP2 was highly upregulated in most HCC tissues and was associated with poor prognosis. HCC patients with high circUBAP2 expression had greater vascular invasion and worse differentiation. Functionally, circUBAP2 overexpression enhanced HCC cell proliferation, migration and invasion and inhibited apoptosis. Furthermore, we found that circUBAP2 upregulated c-Myc expression by sponging miR-1294, thus contributing to hepatocarcinogenesis. Inhibiting circUBAP2 expression in HCC attenuated the oncogenic effects of c-Myc. These findings suggest that circUBAP2 promotes HCC growth and metastasis. CircUBAP2 may have value as an independent prognostic biomarker or as a new target for the treatment of HCC. [ABSTRACT FROM AUTHOR]