Xueying Hu

Background: Hepatocellular carcinoma (HCC) is a major type of primary liver cancer. Previous studies have reported that interleukin enhancer–binding factor 3 (ILF3) is involved in the regulation of multiple cancers. This study investigated the molecular mechanisms whereby ILF3 promotes HCC progression. Methods: ILF3 expression levels were determined through immunohistochemistry (IHC) and Western blot (WB) analyses. The biological functions of ILF3 in HCC were evaluated using both in vitro assays and in vivo animal models. Co‐immunoprecipitation (Co‐IP) was carried out to identify HMGCL as a binding partner of ILF3. To clarify the potential molecular pathways underlying ILF3‐mediated regulation of HCC malignant behaviors, protein stability assays and in vitro ubiquitination experiments were performed. Results: ILF3 was significantly upregulated in HCC. The patients with high expression of ILF3 showed poor prognosis in our cohort. ILF3 knockdown inhibited the proliferation and metastasis of HCC cells both in vitro and in vivo in this study. Mechanistically, ILF3 was found to be bound to HMGCL and to accelerate its protein degradation. Additionally, we found that ILF3 promotes HCC cell proliferation and metastasis through HMGCL. Overexpression of HMGCL in ILF3‐upregulated HCC cells could significantly reverse the proliferation and invasion role of ILF3 on HCC cells. Moreover, USP38 was identified as a deubiquitinating enzyme that participates in promoting the stability of HMGCL. ILF3 disrupted the interaction between USP38 and HMGCL, thereby enhancing HMGCL ubiquitination and accelerating its degradation. Conclusion: ILF3 promotes the proliferation and metastasis of HCC by enhancing the ubiquitination of HMGCL by interfering with the interaction between the deubiquitinase USP38 and HMGCL.

Background: Head and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor–node–metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, an ideal biomarker leading to precise screening and treatment of HNSCC is urgently needed.Methods: Ten machine learning algorithms—Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partial least squares regression for Cox (plsRcox), random survival forest (RSF), generalized boosted regression modelling (GBM), supervised principal components (SuperPC), and survival support vector machine (survival-SVM)—as well as 85 algorithm combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS).Results: Based on the expression profiles of three cohorts comprising 719 patients with HNSCC, we identified 236 consensus prognostic genes, which were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several core clinical features and 51 previously reported signatures, enhanced the predictive capacity of the CIDGS to a level which was markedly superior to that of other signatures. Notably, patients with low CIDGS displayed fewer genomic alterations and higher immune cell infiltrate levels, as well as increased sensitivity to immunotherapy and other therapeutic agents, in addition to receiving better prognoses. The survival times of HNSCC patients with high CIDGS, in particular, were shorter. Moreover, CIDGS enabled accurate stratification of the response to immunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showed potential as therapeutic agents in HNSCC patients with high CIDGS.Conclusion: CIDGS may be used for stratifying risks as well as for predicting the outcome of patients with HNSCC in a clinical setting.