Cancer stem-like cells (CSCs) play pivotal roles in both chemoresistance and recurrence of many cancer types, including urothelial bladder cancer (UBC). In addition to intrinsic signaling pathways, extracellular cues from the tumor microenvironment (TME) are indispensable for the maintenance of CSCs. To better understand the mechanisms involved in TME-mediated generation and support of UBC CSCs, we focused on the role of cancer-associated fibroblasts (CAFs) in this study. Overexpression of miR-146a-5p in CAFs promoted CAF-to-UBC cell interactions, cancer stemness, and chemoresistance to treatment with gemcitabine and cisplatin. Mechanistically, miR-146-5p upregulated SVEP1 in CAFs by enhancing the recruitment of transcriptional factor YY1. Meanwhile, by targeting the 3’UTR of mRNAs of ARID1A and PRKAA2 (also known as AMPKα2) in UBC cells, CAF-secreted miR-146a-5p promoted cancer stemness and chemoresistance. Downregulation of ARID1A resulted in the inhibition of SOCS1 and subsequent STAT3 activation, and downregulated PRKAA2 led to the activation of mTOR signaling. Elevated levels of exosomal miR-146a-5p in the serum of UBC patients were correlated with both tumor stage and relapse risk. These findings altogether indicate that CAF-derived miR-146a-5p can promote stemness and enhance chemoresistance in UBC. Exosomal miR-146a-5p may be a biomarker of UBC recurrence and a potential therapeutic target.

Research Summary The reach of the modern enterprise relies on the power of information technology (IT) tools such as sensors, databases, and machine learning. But tool improvements must be fueled by increased computing power (e.g., faster hardware) or getting more productivity from existing systems (e.g., through better computer algorithms). New research has uncovered that this second source, algorithm progress, is more important than previously realized—sometimes orders of magnitude more important than hardware—and thus could be an important technological stepping‐stone to give competitive advantage to a country's firms. Analyzing this “Algorithm Commons” reveals that the United States has been the largest contributor to algorithm progress, with universities and large private labs (e.g., IBM) leading the way, but that U.S. leadership has faded in recent decades. Managerial Summary Companies are increasingly tackling problems with big data and sophisticated analysis techniques (e.g., Machine Learning). To meet the increased computational demands of these approaches, the capability of computers must improve. One important technique for doing this is to redesign algorithms, the recipes that computers follow to perform calculations. As a result, firms that develop better algorithms, or get access to them first, can get important advantages over their competitors. This paper shows that it is U.S. corporations and U.S. universities that have produced most of the important algorithm improvements, and thus suggests that better algorithms may have been a source of advantage for U.S. multinationals.

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