Sulfarotene, a synthetic retinoid, overcomes stemness and sorafenib resistance of hepatocellular carcinoma via suppressing SOS2-RAS pathway is a research paper published in Journal of Experimental & Clinical Cancer Research (2021). On theSindex it has a DataRank of 0.728. It has been cited 19 times, with 2 citing works in its 1-hop citation network.
Abstract Background Recurrent hepatocellular carcinoma (HCC) shows strong resistance to sorafenib, and the tumor-repopulating cells (TRCs) with cancer stem cell-like properties are considered a driver for its high recurrent rate and drug resistance. Methods Suppression of TRCs may thus be an effective therapeutic strategy for treating this fatal disease. We evaluated the pharmacology and mechanism of sulfarotene, a new type of synthetic retinoid, on the cancer stem cell-like properties of HCC TRCs, and assessed its preclinical efficacy in models of HCC patient-derived xenografts (PDXs). Results Sulfarotene selectively inhibited the growth of HCC TRCs in vitro and significantly deterred TRC-mediated tumor formation and lung metastasis in vivo without apparent toxicity, with an IC50 superior to that of acyclic retinoid and sorafenib, to which the recurrent HCC exhibits significant resistance at advanced stage. Sulfarotene promoted the expression and activation of RARα, which down-regulated SOS2, a key signal mediator associated with RAS activation and signal transduction involved in multiple downstream pathways. Moreover, sulfarotene selectively inhibited tumorigenesis of HCC PDXs with high expression for SOS2. Conclusions Our study identified sulfarotene as a selective inhibitor for the TRCs of HCC, which targets a novel RARα-SOS2-RAS signal nexus, shedding light on a new, promising strategy of target therapy for advanced liver cancer.
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Base Score Contribution
0.449
From this paper's citation signal
Citation Network Contribution
0.279
From 2 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 62% comes from its base citations and 38% from the citation network (2 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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