Harnessing the Cooperative Function of Duplex From pre-miR-16 Hairpin to Simultaneously Inhibit VEGF and Hypoxia Pathways in Human Non-Small Cell Lung Cancer

Harnessing the Cooperative Function of Duplex From pre-miR-16 Hairpin to Simultaneously Inhibit VEGF and Hypoxia Pathways in Human Non-Small Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Harnessing the Cooperative Function of Duplex From pre-miR-16 Hairpin to Simultaneously Inhibit VEGF and Hypoxia Pathways in Human Non-Small Cell Lung Cancer Wenyu Xue, Yuzhe Wang, Philipp Malakhov, Anna Smirnova, Margarita Pustovalova, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8912330/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Resistance to anti-angiogenic therapy remains a major obstacle in non–small cell lung cancer (NSCLC). Although bevacizumab effectively neutralizes VEGFA and suppresses neovascularization, its clinical benefit is often transient. Here, we demonstrate that VEGFA inhibition paradoxically intensifies intratumoral hypoxia, leading to stabilization of HIF1A and activation of a compensatory pro-malignant program. Increased HIF1A not only enhances tumor cell migration, epithelial–mesenchymal transition, and chemoresistance, but also upregulates CD105 (Endoglin), a hypoxia-responsive pro-angiogenic mediator that attenuates the anti-vascular effects of bevacizumab. Thus, anti-VEGFA therapy initiates a hypoxia-driven HIF1A–CD105 axis that sustains tumor aggressiveness and vascular adaptation despite VEGFA blockade. We identify the pre–miR-16-1 duplex as a physiological dual-regulatory system capable of simultaneously targeting this adaptive circuit. The guide strand miR-16-5p directly represses VEGFA, recapitulating the anti-angiogenic action of bevacizumab. In contrast, the passenger strand miR-16-1-3p suppresses HIF1A expression, thereby preventing hypoxia-induced malignant phenotypes and limiting CD105 upregulation. Functional analyses revealed that VEGFA inhibition alone promotes hypoxia-associated migration, cytoskeletal remodeling, and cisplatin resistance, whereas co-modulation of miR-16-1-3p abrogates these effects. In a chick chorioallantoic membrane xenograft model, dual regulation of VEGFA and HIF1A markedly reduced vascular density, tumor growth, and metastatic dissemination compared with single anti-angiogenic intervention. Collectively, our findings uncover a hypoxia-mediated resistance mechanism driven by the HIF1A–CD105 axis following VEGFA inhibition and establish the cooperative function of the miR-16 duplex as a strategy to concurrently suppress angiogenesis and its adaptive hypoxic feedback. Targeting both VEGFA and HIF1A may therefore improve the durability of anti-angiogenic therapy in NSCLC. Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance Biological sciences/Molecular biology/Non-coding RNAs/miRNAs Bevacizumab anti-angiogenic therapy resistance HIF1A CD105 hypoxia miR-16-5p miR-16-1-3p lung cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction About 85% of lung cancer cases are non-small cell lung cancer (NSCLC), mainly including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) 1 . Existing screening guidelines often overlook lung cancer patients, leading to late-stage diagnoses of NSCLC or diagnosis only when symptoms appear 2 . Despite the availability of various therapies for NSCLC, the five-year survival rate remains a disheartening 21%. This is primarily because 75% of patients are diagnosed at advanced stages and because many develop resistance to treatment 3 , 4 . The lack of research on mechanisms behind rapid NSCLC progression and therapy resistance has limited new treatment options. MiRNAs, the non-coding RNAs measuring 18–25 nucleotides, are significantly affecting tumor growth 5 . By interacting with matching sequences in the 3′-UTR of target mRNA, miRNAs successfully silence target gene expression. This occurs either through mRNA degradation or by inhibiting mRNA translation 6 , 7 . More than 60% of mRNAs are thought to be conserved targets of endogenous miRNAs, and including less conserved sites may reveal even more, hinting at a previously unknown post-transcriptional RNA signaling network 8 . Research shows that miRNAs are essential for regulating gene expression after transcription, playing a key role in maintaining cellular balance in both normal and NSCLC-related functions 9 , 10 . Exploring the connection between miRNA regulation and mechanisms of drug resistance marks a novel approach in tackling anti-tumor drug resistance. The microRNAome, constantly changing and made up of miRNAs, attracts attention because the mechanisms and creation processes of these molecules are still not fully understood. Initially, it was thought that the two strands were the complete products of the miRNA precursor (pre-miR), but the “passenger” strand (miR-3p or “star” strand- miRNA*), quickly degrades after the guide strand (miR-5p or “lead” strand) is used, making it rare in the mature miRNA pool. Despite uncertainty about how strand selection and dominant expression operate, numerous miRNA* species build up and exert considerable post-transcriptional regulation on their conserved targets 10 – 14 . Significant support for this concept has come from multiple experiments examining the regulatory roles of miRNAs*, which have not been identified as post-transcriptional modulators. Most functionally validated miRNAs were guide strands (miR-5p), with only a limited number of miR-3p or star strands identified as possible regulators. Despite modest signal-to-noise ratios, miRNA* species are currently considered as a key in the RNA regulatory network, leading to a new theory: “target-two-sets-of-genes-with-one-pre-miRNA” 11 . The regulatory functions of miRNA* species necessitate an overhaul of the miR/miR* naming convention, which should be achieved through a comprehensive annotation of miR-5p/3p sequences derived from their hairpin precursors. In this regard, the functional annotation of the miR-16 family (miR-16-1, miR-16-2, miR-16-5p) is important, as miR-16 and miR-34 are key tumor suppressor miRNAs in cancer 15 . The canonical miR-16, also called “guide” miR-16-5p strand, is synthesized by RNA polymerase II from either the MIR-16-1 loci on chromosome 13q14 or the MIR-16-2 loci on chromosome 3q25 in the human genome 16 . However, only MIR-16-1 loci encodes miR-16-1-3p, the “passenger” strand, also known as miR-16-1*. Insightfully, miR-16-5p is often missing or underexpressed in NSCLC compared to healthy individuals 17 . Nonetheless, the expression of miR-16 “passenger” strands in NSCLC has not been analyzed yet. Our research indicates that increasing miR-16-1* and miR-16-2* levels enhances NCLC cell sensitivity to X-rays, suggesting their unknown role in NSCLC resistance to DNA damage 18 . The functional significance of miR-16 family “passenger” strands in other NSCLC therapies remains elusive. Tumor angiogenesis remains a pivotal driver of malignant progression and metastasis, supplying oxygen and nutrients while shaping a permissive microenvironment for invasion and immune evasion 19 . Studies indicate that anti-angiogenic therapy has emerged as a pivotal treatment approach for NSCLC 20 . Accordingly, VEGFA-targeting agents, like bevacizumab, have emerged as essential elements in the first-line treatment of NSCLC and various other solid tumors 21 , 22 . While bevacizumab can temporarily benefit patients and hinder progression, it often results in resistance, recurrence, or metastasis, indicating the shortcomings of a single targeting approach 23 , 24 . Increased evidence shows that secondary hypoxia significantly contributes to drug resistance. Reduced vessel density limits blood flow and oxygen delivery, which activates HIF (hypoxia-inducible factor) activation 25 . Elevated HIF can reprogram metabolism, induce EMT, and engage alternative pro-angiogenic circuits, thus enabling tumor adaptation and regrowth despite VEGF inhibition 26 . This hypoxia-driven feedback is viewed as a crucial resistance element, but its initial triggers and later amplifiers are not fully clarified. In this context, non-coding RNAs have emerged as promising modulators due to their capacity for multitarget regulation 27 . Notably, miRNAs are often conceptualized as fine-tuners rather than binary switches, conferring robustness and minimizing drastic side effects from treatment 28 . Hypoxia induces the downregulation of miR-16, subsequently leading to an increase in VEGF-A expression in a human nasopharyngeal carcinoma cell line CNE 29 . Moreover, limited studies on miR-16 indicate that its passenger strands miR-16-1-3p may possess intrinsic tumor-suppressive function 30 , 31 , hinting at possible functional complementarity with the guide strand. However, such a functional "guide" and "passenger" strand complementary impact in anti-VEGF therapy resistance NSCLC is completely unexplored yet. Right now, very few studies examine the functional importance of dual sister strands in depth within one investigation. The absence of thorough exploration underscores a major deficiency in our comprehension of the complexities at play. As a result, due to unintentional oversight in miRNA* expression, some existing miRNA-targeting strategies in cancer research are seen as less reliable. Although dual sister strands are essential for various biological processes, few studies have dedicated themselves to uncovering their operational significance. There is a strong need for deeper analysis in this research area. Here, we propose and explore “dual regulatory strand” miR-16 strategy, that includes overexpressing miR-16-5p, which inhibits VEGFA-driven tumor and blood vessel growth, and overexpressing miR-16-1-3p, the passenger strand, that alleviate HIF1A hypoxia feedback. These naturally derived miRNA duplexes target the initiation and compensatory responses of the VEGF-HIF axis, resulting in a more balanced and potentially long-lasting intervention for angiogenesis and hypoxia in cancer. This phenotypic profiling of dual regulatory strands ensures that both aspects of the pathway are managed effectively, possibly leading to improved patient outcomes. Additionally, integrating functionally annotated natural miRNA-5p/3p in one study could significantly decrease adverse reaction risks, as dual sister strands of a miRNA precursor are rarely investigated simultaneously in the same lab. This research could greatly improve anti-cancer treatments for conditions related to excessive blood vessel growth and low oxygen levels. Results VEGFA inhibition by bevacizumab or overexpressing miR-16-5p suppresses tumor growth but triggers compensatory HIF1A upregulation Bevacizumab decreased micro-vessel density and tumor size in glioblastoma U251-HRE xenografts but raised intratumor hypoxia and HIF1 protein levels, along with its target gene transcripts 32 . To study the early anti-angiogenic response to VEGFA blockade in NSCLC, A549 xenografts were established using the CAM model. Bevacizumab treatment or higher levels of miR-16-5p in A549 cells significantly decreased both vascular density and tumor size, as shown in Fig. 1 A-B, compared to untreated samples. We show for the first time that both treatments greatly reduce metastases, as shown by the quantitative bio-fluorescent imaging data (Fig. 1 C) in spontaneous metastasis CAM model following our established protocol 33 , 34 . However, immunofluorescent analysis showed high levels of HIF1A in the tumor nodules (Fig. 1 D) after both treatments, suggesting that reduced blood vessel formation in the tumor microenvironment triggers a hypoxia-like response. Consistently, bioinformatics analysis of the GSE37138 cohort demonstrated that bevacizumab-treated lung cancer patients had reduced VEGFA expression and elevated HIF1A levels (Fig. 1 E). Together, these findings reveal that blocking VEGFA is effective in shrinking neovascularization and tumors and limit metastases, but may create a prolonged hypoxia-driven response, as reflected by increased HIF1A levels in NSCLC cells. Elevated HIF1A diminished cell adhesion and epithelial characteristics, while increasing survival and cisplatin resistance in NSCLC. HIF1A activation in tumor cells generally encourages tumor development, but its effects can change with the context, occasionally hindering growth by inhibiting nutrient pathways like aspartate or by stimulating self-digestion (autophagy) in cancer cells, though this is cell-type specific 35 – 37 . To delineate the functional consequences of HIF1A expression on NSCLC cells, A549 cells were transfected with an HIF1A-overexpressing plasmid (A549-FC-3398-HIF1A). Under normoxic conditions, HIF1A overexpression greatly enhanced 2D collective and 3D confined migrations (Fig. 2 A–B). Furtheremore, overexpressing HIF1A in A549 cells altered their morphology from cobblestone to fibroblast-like with a spindle shape. This change was marked by decreased E-cadherin levels and increased cytoskeletal remodeling (Fig. 2 C–D), indicating reduced cell adhesion and the acquisition of an EMT-like phenotype. Higher levels of HIF1A also decreased the number of apoptotic cells (Fig. 2 E) and raised cisplatin-induced IC50 values, indicating resistance to cisplatin (Fig. 2 F). Survival analysis of the TCGA-LUAD database demonstrated that patients with high HIF1A expression had significantly poorer overall survival (Fig. 2 G). Gene set enrichment analysis further revealed enrichment of hypoxia, EMT, and angiogenesis pathways in HIF1A-high tumors (Fig. 2 H), supporting its role in promoting malignant adaptation under stress. Hypoxia-induced HIF1A augments CD105 in vitro and reverses anti-angiogenic effects of VEGFA blockade in vivo. When oxygen levels are normal, the HIF1A is expressed but undergoes rapid degradation. Under low oxygen conditions, this subunit is stabilized, enters the nucleus 38 , and activates critical genes for survival, blood vessel formation, and adaptation 39 , 40 . Encoded by such genes, soluble factors, whether free or encapsulated in extracellular vesicles (EVs), are crucial for the tumor cells' relationship with their microenvironment. These factors play a crucial role in fostering tumor growth, creating pre-metastatic niches, and eventually contributing to metastasis 41 , 42 . Hence, we first examined the profile of A549 cell secretome under hypoxic conditions induced by CoCl 2 treatment. Under hypoxic conditions, CD105 protein levels in the conditioned medium significantly increased after treatment with bevacizumab or overexpressing miR-16-5p (Fig. 3 A). Western blot analysis confirmed the upregulation of cell-associated CD105 protein following bevacizumab- or miR-16-5p–mediated VEGFA targeting under hypoxia (Fig. 3 B). Bioinformatic analysis of the GSE37138 dataset also verified positive correlation between expressions of CD105 and HIF1A in bevacizumab-treated tumors (Fig. 3 C). To test this response in vivo, we generated stable HIF1A overexpression in both A549-miR-SCR and A549-miR-16-5p cells. Important, in such HIF-high paradigm, bevacizumab treatment no longer reduced vascularization. Instead, vessel density increased, accompanied by unchanged heightened tumor size and metastases (Fig. 3 D–F). Strikingly, hsa-miR-16-5p overexpressing was able to thwart the elevation of metastasis associated with HIF1A, yet the challenges of increased vascularization and tumor magnitude persisted (Fig. 3 D–F). In the same paradigm, both treatments effectively enhanced CD105 expression in tumor tissues, according to quantitative immunofluorescence analysis (Fig. 3 G). Hence, HIF1A overexpression leads to a vascular response through CD105, counteracting the anti-angiogenic effects of VEGFA inhibition, demonstrating a possible vital mechanism of adaptive resistance. Expression of passenger strand, miR-16-1-3p, targets HIF1A and complements miR-16-5p-mediated VEGFA suppression in vitro When the vicious cycle between HIF1A and VEGFA pathways is established, the low oxygen tension activates the expression of HIF-1α, which subsequently enters into the hypoxia-induced HIF pathways. Hence, we attempted to identify an agent that would specifically control HIF1A activity in terms of both protein and function to break the link between HIF1A and VEGFA pathways. Bioinformatics analysis using TargetScan, miRDB, and miRWalk found 456 common targets of miR-16-1-3p, with HIF1A being consistent across all three databases (Fig. 4 A). The direct binding of miR-16-1-3p to the HIF1A 3′-UTR was confirmed by a significant rise in normalized luciferase activity in the dual-luciferase reporter assay (Fig. 4 B). In the hypoxic conditions, overexpressing hsa-miR-16-1-3p using lentivirus significantly reduced HIF1A protein levels in A549 cells compared to the control (A549 cells overexpressing scrambled, miR-SCR sequence), while cell-associated VEGFA levels remained unchanged, as shown by Western blot (Fig. 4 C). Conversely, lentivirus-driven overexpression of hsa-miR-16-5p specifically decreased VEGFA expression (Fig. 4 D) in the same NSCLC cell line. These results imply that both passenger and lead strands of the same pre-miR-16-1 target different elements of the interplay between VEGFA and HIF1A, forming a dual-layer mechanism to control hypoxia-induced feedback (Fig. 4 E). miR-16-1-3p attenuates HIF1A-related malignant phenotype in vitro Functional rescue assays were performed to test if the hsa-miR-16-1-3p overexpression can counteracts HIF1A-related malignant phenotypes of A549 cells in vitro. Cells transfected with an empty vector (pcDNA) and miR-16-1-3p (miR-16-1 + pcDNA) significantly reduced both 2D (Fig. 5 A) and 3D (Fig. 5 B) migration compared to control cells transfected with empty vector and scrambled miR (miR-SCR+pcDNA). An increase in E-cadherin expression, actin cytoskeleton reorganization and epithelial-like shape featuring were observed in these cells compared to the control (Fig. 5 C, D). Transient overexpression of HIF1A in the same cells restored both migration types and inhibited E-cadherin and actin reorganization, confirming that these processes are related to HIF1A expression. Apoptosis increased after miR-16-1-3p overexpression, which was partially reversed by transient HIF1A overexpression as revealed by FACS (Fig. 5 E). Overexpression of miR-16-1-3p enhanced the sensitivity to cisplatin, while the restoration of HIF1A expression reestablished drug resistance (Fig. 5 F). Together, these findings indicate that miR-16-1-3p upregulation can exert its suppressive function in the hypoxic feedback loop, counteracting HIF1A-related pro-metastatic and anti-apoptotic effects. Overexpressing both guide and passenger miRNA strands decreases HIF1A and CD105 bursts caused by anti-VEGFA treatments, halting angiogenesis, tumor growth, and metastasis in vitro and in vivo. Finally, we assessed whether boosting “passenger” miR-16-1-3p strand could mitigate the effects of VEGFA blockage caused by either Avastin or elevated “guide” miR-16-5p strand overexpression. In the CAM model, combining overexpression of miR-16-1-3p with either miR-16-5p or Avastin showed the strongest effect in reducing vascularization, tumor size, and metastasis compared to each treatment alone (Fig. 6 A–C). The same combinations demonstrated concurrent downregulation of cell-associated both HIF1A and CD105 as indicated by Western blotting (Fig. 6 D), and by immunofluorescence analysis of tumor nodule sections (Fig. 6 F). For the first time, this data shows that using the natural strands of the pre-miR-16-1 duplex to target both VEGFA and HIF1A/CD105 interplay in the angiogenic pathway can effectively prevent NSCLC cell growth, angiogenesis, and spread. Discussion Anti-angiogenic therapy represents a cornerstone in the management of lung cancer and other solid malignancies. Among these agents, VEGFA inhibitors such as bevacizumab have become a standard component of first-line regimens for NSCLC 21 . By blocking the interaction between VEGFA and its receptors, bevacizumab suppresses tumor vascularization, reduces perfusion, and delays tumor growth. Traditionally, Bevacizumab's antitumor activity is linked to its capacity to suppress angiogenesis. This mechanism is founded on the concept that tumors require new blood vessels for growth and spread 43 . VEGFA binding with VEGFR2 on endothelial cells initiates signals for their proliferation, migration, thereby promoting angiogenesis 44 . Bevacizumab attaches to VEGFA, blocking its connection with VEGFRs on endothelial cells and stopping vital processes in tumor blood vessel development. However, despite its initial efficacy, clinical experience has revealed a recurrent pattern of resistance, relapse, and metastasis after prolonged treatment 23 , 24 . This paradox underscores a fundamental limitation of single-pathway blockade—namely, that inhibition of VEGF signaling can trigger complex adaptive responses within the tumor microenvironment, ultimately diminishing therapeutic efficacy. VEGFA-independent angiogenesis seems to underlay the bevacizumab resistance, yet attempts to inhibit it have often failed. These challenges significantly undermine the conventional anti-angiogenic strategies that utilize bevacizumab. Exploration of effects, in addition to anti-angiogenic properties, is needed. Research now targets the direct cytotoxic effects of bevacizumab on lung cancer cells both in vitro and in vivo 45 – 47 . We assessed the effects of VEGFA neutralization on established tumors by locally administering bevacizumab to pre-formed tumors in the CAM model. In this experimental design, bevacizumab was directly administered to the tumor mass post-implantation, effectively reducing the nonspecific exposure of the surrounding CAM tissue. Although bevacizumab treatment markedly reduced VEGFA levels in the A549 tumor cell secretome (Fig. 3 A), disrupting their VEGFA-mediated autocrine signaling, yet it does not fully prevent tumor growth in the CAM (Figs. 1 A–C). The results suggest that just inhibiting VEGFA is insufficient to prevent tumor development in the CAM model, indicating other non-VEGF pathways are activated. These results highlight the weaknesses of single-target anti-VEGF therapy and underscore the importance of using multi-level treatments to address adaptive resistance. Previous studies have identified HIF1A upregulation as a major driver of acquired resistance following VEGF inhibition. Reduced vascular density leads to impaired oxygen delivery and decreased intratumoral oxygen tension, activating HIF1A-mediated hypoxia response programs that promote tumor survival, migration, and vascular remodeling 32 , 48 – 51 . In line with this concept, our findings demonstrated that targeting VEGFA with miR-16-5p most effectively decreased early angiogenesis and lowered tumor size and metastasis in the CAM model (Fig. 1 A-C) compared to bevacizumab (as positive control), significantly complementing the range of tools modulating vessel pruning and inhibiting neoangiogenesis. Yet, both treatments resulted in a marked rise in HIF1A expression (Fig. 1 D), indicating the onset of severe hypoxic stress pointed previously 52 , 53 . Consistent with this, our in vitro experiments demonstrated that HIF1A activation was accompanied by increased migratory capacity (Fig. 2 A-B), enhanced acquisition of mesenchymal traits and resistance to cisplatin (Fig. 2 C-D, F), while reducing programmed cell death (apoptosis and necroptosis) (Fig. 2 E). These data confirm that hypoxia is the main cause of tumor angiogenesis, creating a vicious cycle between hypoxia and angiogenesis in tumors 54 . In low-oxygen conditions, HIF-1 enhances CD105 (Endoglin, encoded by ENG gene) protein, mRNA, and promoter activity by engaging with a specific HRE (Hypoxia-Responsive Element) in the ENG promoter 55 , 56 . Significant pile of data indicates that endoglin's role in tumor cell behavior is context-dependent, which means that different cancers need specific strategies 57 . Additionally, increased concentrations of endoglin are linked to unfavorable outcomes in certain cancer patients 58 – 61 . However, endoglin's impact on cancer is still controversial; it seems to block tumor angiogenesis but can also lead to a more aggressive form of myeloma and breast cancer. We still do not fully understand the role of endoglin, warranting additional research on various tumor types to analyze how ENG-expressing EVs might shape the tumor microenvironment. Moreover, the role of endoglin in NSCLC was not explored yet. Notably, both bevacizumab or miR-16-5p overexpression reprogrammed secretory profile under hypoxic conditions; markedly elevating both secreted and cell-associated CD105 levels (Fig. 3 A-B). The change had a significant impact in vivo: CD105 expression in tumor nodules increased, angiogenesis inhibition was reversed, micro-vessel density in the CAM improved, although both tumor volume and metastasis remained stable instead of declining (Fig. 3 D-F). For the first time, we demonstrate that endoglin’s expression correlates with neoangiogenesis under hypoxia in a human NSCLC line, differing from its previously identified tumor-promoting role in myeloma and breast cancer. Collectively, these findings delineate a compensatory feedback loop in which VEGFA inhibition not only suppresses angiogenesis but also intensifies tumor hypoxia, leading to HIF1A activation and the induction of downstream adaptive pathways. Sustained HIF1A upregulation enhanced tumor plasticity and motility while promoting CD105 expression and secretion, thereby reinitiating pro-angiogenic signaling. The resulting dual effect— the revascularization on the background of unchanged proliferative and metastatic activity—represents a morpho-functional rebound that undermines anti-VEGF efficacy. Hence, HIF1A-driven rejuvenation of CD105 may be a key factor in resistance to anti-angiogenic therapy. This assumption has very strong experimental and clinical grounds. Multiple studies have reported that CD105 (Endoglin) upregulation constitutes an alternative pro-angiogenic pathway following VEGF blockade 62 – 64 . In colorectal cancer, patients treated with bevacizumab and those with metastasis have higher CD105 levels compared to untreated patients. The link between CD105 and VEGF in untreated cases is lost after treatment, indicating that CD105 may operate independently when VEGF signaling is low 65 . Similarly, studies with animal models reveal that residual vasculature after VEGF inhibition has high CD105 expression, with CD105 mRNA being significantly upregulated 62 . Clinical trials combining bevacizumab with the anti-CD105 antibody TRC105 have shown better antitumor activity than using either treatment alone for recurrent glioblastoma and metastatic renal cell carcinoma 62 , 63 , 66 . CD105 transcription directly driven by HIF1A provides a mechanistic rationale for why CD105 re-expression occurs under hypoxic conditions induced by VEGFA inhibition. Indeed, bevacizumab is reported to increase TGFβ1 and CD105 expression, promoting angiogenesis in hypoxic conditions. In contrast, multitarget receptor tyrosine kinase (RTK) inhibitors can reverse this effect 64 , reinforcing the sequence: "VEGFA suppression → HIF1A/CD105 activation → compensatory angiogenesis." Importantly, CD105 expression is not restricted to endothelial cells. Emerging evidence shows that tumor cells themselves can produce and secrete CD105, thereby contributing directly to angiogenesis and immune evasion 67 – 70 . In EGFR-mutant NSCLC, blocking CD105 has even been shown to restore the drug sensitivity of osimertinib-resistant cells 68 . These reports collectively align with our findings, supporting the concept that CD105 functions as a pivotal nexus between endothelial remodeling and tumor cell–autonomous adaptation during anti-VEGF therapy. This discovery underscores the significance of understanding interactions between HIF1A and VEGFA pathways in cancer treatment, as tumors can adapt to therapies aimed at slowing their growth. Continuous exploration in these interactions will provide valuable insights into how tumors evade therapeutic strategies targeting angiogenesis. Beyond antibody-based anti-angiogenic therapeutics, microRNAs (miRNAs) offer a distinctive advantage due to their multi-target, network-level regulatory potential. We functionally explored the miR-16 family, where overexpressing guide miR-16-5p strand under hypoxia, similar to bevacizumab, lowers VEGFA expression (Fig. 4 D), thereby reducing blood vessel development and attenuating of tumor growth and spread (Fig. 1 A-C). However, the outcome of the overexpression was significantly increasing HIF1A expression suggesting the hypoxia onset (Fig. 1 D). Remarkably, miR-16-5p overexpression alone under hypoxia, like a bevacizumab treatment, augments HIF1A-driven expression of both secreted and cell-associated CD105 (Fig. 3 A-B) in vitro. Insightfully, this CD105 burst impedes completeness of anti-angiogenic and anti-tumorigenic effects of the guide strand on NSCLC cell line (Fig. 3 D-E). Our findings highlight that the majority of valid miRNAs are guide strands (like miR-16-5p), with only a small number of passenger or star strands as candidates for regulation. A key reason for this issue is that rare miRNAs have not been widely acknowledged as part of the post-transcriptional regulatory network. Concerns have been raised about the premature miRNA-targeting approach because it goes against the fact that most miRNA precursors produce two mature regulatory miRNAs. MiRNA maturation occurs in stages, starting from the primary transcript to the hairpin precursor, and ends with the mature functional form, which typically results in two separate regulatory single-stranded RNAs. Now, passenger miRNA species play a vital role in the RNA regulatory network, despite their low signal-to-noise ratios. A new theory, called "target-two-sets-of-genes-with-one-pre-miRNA," has been proposed 11 . In our study, passenger miR-16-1-3p strand was verified to reduce HIF1A expression (Fig. 4 B-C) and blood vessel formation, while also inhibiting HIF1A-driven EMT, apoptosis resistance and cell migration (Fig. 5 ). By overexpressing miR-16-5p and miR-16-1-3p together, both the initiation and compensation phases of the VEGF–HIF axis are suppressed, inhibiting VEGFA-driven angiogenesis and HIF1A feedback activation. In the CAM model, this “dual regulatory strand” strategy more effectively reduced vascular density, significantly reducing tumor growth and metastasis compared with single-pathway inhibition (Fig. 6 ). From a potential clinical standpoint, our study is distinct from previously explored combinations such as bevacizumab plus TRC105. In light of bevacizumab's frequent pairing with chemotherapeutics, researchers need to investigate the combined cytotoxic effects and how tumor cells resist this treatment in vitro. Consequently, insightful recommendations for clinical use can be delivered. Ultimately, it is essential to translate in vitro discoveries into in vivo environments to confirm the mechanisms and effects identified. Advanced experimental designs are crucial to distinguish the direct cytotoxic effects of bevacizumab from the indirect ones through immune modulation. The analysis should compare immunocompromised and immunocompetent animal models to reveal how immune mechanisms influence the antitumor effects of bevacizumab alone or in combination with miRNA. Manipulating pre-miRNA can effectively modify target miRNA expression, while also potentially causing random phenotypic effects due to changes in miRNA* expression, reflecting our inadequate understanding of methods. Our approach reveals a dual-target mechanism where overexpressing duplexes from each strand of the same pre-miRNA regulate VEGFA and HIF1A together, maintaining safe intrinsic molecular balance. Our findings somewhat bolster the promising concept of a microRNA-based therapeutic “One-Two Punch” strategy aimed at effectively targeting cancer 9 . Hence, since we identify the NSCLC-associated sister miRNAs, targeting the pre-miRNA-16-1 could be an effective treatment strategy—a “one-two punch.” Inspired by the therapeutic potential, several miRNA therapies are being trialed clinically to uncover their true value in treatment. In summary, this study highlights the key role of the HIF1A–CD105 pathway in anti-VEGF resistance and presents a new miR-16 “dual regulatory strand” strategy that targets both VEGFA and HIF1A. This strategy offers a low-toxicity alternative for addressing bevacizumab resistance in NSCLC, eliminating side effects of anti-VEGF therapies. Limitations Our experiments were performed in only one NSCLC cell line, A549. A549 is a standard cell line regarding miRNA expression and has been used in studies about miRNAs and biological interactions 71 – 74 . Correlations between AGO2 binding sites and RNA levels of target genes might vary in different cell lines, conditions, or in vivo environments. Establishing correlations on a case-by-case basis may be essential instead of presuming a direct link between binding and gene repression. The CAM model provides a rapid in vivo system for vascular evaluation but cannot fully capture long-term adaptive remodeling following chronic hypoxia. Although HIF1A overexpression partially simulates this process, validation in murine xenograft models and clinical specimens will be essential to confirm the translational potential of this “dual regulatory strand” miRNA approach. lncRNA PVT1 acts as a miRNA sponge and negatively regulates miR-16-5p expression. Targeted loss of miR-16-5p partially rescues the suppressive effect induced by PVT1 knockdown. Overexpression of VEGFA is known to modulate the AKT signaling cascade by activating vascular endothelial growth factor receptor 1 (VEGFR1). lncRNA PVT1 knockdown suppresses CRC progression via inhibiting miR-16-5p-mediated VEGFA/VEGFR1/AKT signaling [24]. We did not conduct deep RNA sequencing on A549 cells overexpressing miR-16-5p and miR-16-1-3p, highlighting the need for further research. Recent research indicates that resistin promotes VEGF-A expression and angiogenesis by inhibiting miR-16-5p expression through the PI3K/Akt signaling pathways in chondrosarcoma [25]. While it is outside the scope of this study, understanding the interactions between the PI3K/Akt signaling pathway and miR-16-5p/3p overexpression in NSCLC is important. The last but not least, the canonical activity of miR-16, which degrades oncogenes such as cyclin D3, is dysfunctional in uveal melanoma (UM). MiR-16 primarily engages in non-canonical base-pairing with a number of specific mRNAs, thereby enhancing their expression levels 75 . The upregulation of expression may stem from a non-canonical mechanism involving a miRNA that binds to the 3′-UTR and stimulates gene expression 76 . Our comprehensive study did not delve into the analysis of either the transcriptome or the proteome of A549 cells following the overexpression of both guide and passenger strands of miR-16. This gap left us without a clear picture of all the molecular changes resulting from this overexpression. The data we collected does not fully reveal how miR-16 affects gene expression and protein synthesis in A549 cells. Hence, further research would be required to explore these critical aspects more thoroughly. Materials and Methods Cell culture and treatments Human cell lines A549, HEK293T, and HeLa were obtained from (Russian Cell Culture Collection of Vertebrates, Institute of Cytology, RAS, Russia). All cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (NPP PanEko LLC, Moscow, Russia) supplemented with 10% fetal bovine serum, 1% penicillin–streptomycin (5000 U/mL), and 1% L-glutamine. Cultures were maintained at 37°C in a humidified atmosphere containing 5% CO₂, and the medium was replaced twice weekly. All cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be mycoplasma-free using the MycoReport Mycoplasma Detection Kit (Cat. No. MR001, Evrogene, Russia). The hypoxia in vitro was induced by the treatment of A549 cells with cobalt chloride as previously described 77 . The optimal time and CoCl₂ concentration used for hypoxia induction in vitro was determined experimentally (Supplementary Fig. 2). For in vitro experiments, including apoptosis assays and fluorescence staining, A549 cells were transiently transfected with miR-16-1-3p mimic or a scrambled negative control mimic (miR-Scr) using Lipofectamine™ 3000 (Thermo Fisher Scientific). Cells at 60–70% confluence were transfected with microRNA mimics at a final concentration of 50 nM, following the manufacturer’s instructions. After 24 hours of transfection, cells were collected for subsequent analyses. Plasmids The lentiviral transfer vector PLKO.3G was a gift from Christophe Benoist and Diane Mathis (Addgene plasmid #14748; http://n2t.net/addgene:14748 ). The lentiviral packaging plasmids pLP1, pLP2, and pVSVG were obtained from Invitrogen (Thermo Fisher Scientific, USA). For reporter and overexpression assays, three categories of HIF1A-related plasmids were obtained from Fubio Biotechnology Co., Ltd. (Suzhou, China). The dual-luciferase reporter plasmid FC-8635 contained the wild-type HIF1A 3′ untranslated region (3′UTR) cloned downstream of the firefly luciferase gene, and its corresponding empty vector control FV-149 was used for normalization. The sequence of the cloned HIF1A 3′UTR fragment is provided in Supplementary Table 1. For in vitro transient overexpression experiments, the HIF1A expression plasmid FC-2695 and its control FV-073 (pcDNA) were used. To generate stable overexpression cell lines, the lentiviral construct FC-3086 and its negative control FV-050 were employed. All plasmids were sequence-verified prior to use, and their detailed maps are shown in Supplementary Fig. 4. Cloning of miR-16 Constructs Lentiviral constructs PLKO.3G-miR-16, PLKO.3G-miR-16-1* and the scrambled microRNA control PLKO.3G-Scr miR, all co-expressing the EGFP reporter gene, were generated for the overexpression of miR-16, miR-16-1* and Scr miR, respectively. The procedure was as follows: DNA duplexes for (1) miR-16, (2) miR-16-1* and (3) Scr miR were prepared by annealing complementary oligonucleotide pairs (see Supplementary Table 2). The resulting duplexes were individually cloned into the Acc36I and EcoRI restriction sites of the PLKO.3G backbone. The accuracy of all lentiviral constructs was verified by PCR using PLKO-Dir and PLKO-Rev primers (see Supplementary Table 2), followed by Sanger sequencing with PLKO-15' and PLKO-Rev primers (see Supplementary Table 2). The scrambled miR (Scr miR) sequence was sourced from 78 . Lentivirus Production and Transduction Lentiviruses were produced by co-transfecting the lentiviral transfer vector with the packaging plasmids (pLP1, pLP2, and pVSVG) into HEK293T cells using Lipofectamine 3000 (Thermo Fisher Scientific). The viral supernatant was harvested 72 hours post-transfection, filtered through a 0.45 µm filter (Millipore), and stored at − 80°C. lentiviruses concentration was preformed following protocol 79 . The virus-containing medium was harvested 48–72 hours post-transfection and clarified by centrifugation at 3,000 × g followed by filtration through a 0.45 µm filter. The clarified medium was layered onto a sucrose cushion buffer (50 mM Tris-HCl, pH 7.4, 100 mM NaCl, 0.5 mM EDTA) at a 4:1 ratio (v/v) and centrifuged for 4 hours at the 14000 RCF and 4°C. After carefully removing the supernatant, the tube was inverted on tissue paper for 3 minutes to drain. The resulting pellet was resuspended in DMEM supplemented with 1% BSA and allowed to recover overnight at 4°C, then stored at -80°C. A549 cell lines were transduced with the lentiviral particles in 6-well plates. Subsequently, EGFP-positive A549 cells were isolated using a BIO-RAD S3e cell sorter (BIO-RAD, USA). All subsequent experiments were performed using this sorted, EGFP-positive populations of resulting A549-miR-16-5p and A549-miR-16-1-3p cell sublines. The sorted A549-miR-16-5p subline was further infected with concentrated miR-16-1-3p lentivirus for the generation of A549 subline co-expressing miR-16-5p and miR-16-1-3p. Following infection, total RNA was extracted, and qRT–PCR analysis was performed to confirm the successful overexpression of miR-16-1-3p. Stable overexpressing cell lines, including A549–miR-16-5p, A549–miR-16-1-3p, and A549–miR-16-5p + miR-16-1-3p, were validated by RT-qPCR analysis (Supplementary Fig. 1). For the establishment of HIF1A-overexpressing cell lines, A549 cells were infected with HIF1A lentiviral particles and subsequently selected with puromycin to obtain stable populations. A negative control group was included to ensure the accuracy and efficiency of the selection process. Following antibiotic selection, HIF1A overexpression in cell lines was confirmed by Western blotting (Supplementary Fig. 3). Experimental groups included A549-miR-16-5p, A549-miR-16-1-3p, and A549-miR-16-5p + miR-16-1-3p overexpressing cell lines, with A549-miR-SCR serving as the control for miRNA groups; for HIF1A-overexpression assays, cells carrying the empty vector plasmid (pcDNA) served as controls. RNA extraction and quantitative PCR Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol, and RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). To quantify microRNA expression, complementary DNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Cat. No. 4368814) together with stem-loop RT primers designed for individual microRNAs. Reverse transcription was performed at 25°C for 30 min, 37°C for 120 min, and 85°C for 5 min. The resulting cDNA was subjected to TaqMan probe–based quantitative PCR on a QuantStudio 5 Real-Time PCR System (Applied Biosystems). Each 20 µL reaction contained a microRNA-specific forward primer, a universal reverse primer, and the corresponding TaqMan probe (0.5 µM each). The thermal cycling profile was 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. Expression levels were normalized to U44 small nuclear RNA, and relative quantification was calculated using the 2^–ΔΔCt method. Dual Firefly–Renilla Luciferase Reporter Assay The HeLa cell line was selected for the reporter assay because it does not express endogenous miR-16 29 . Cells were seeded in 24-well plates and co-transfected with 300 ng of either the wild-type (HIF1A-3′UTR-WT, FC-8635) or control (empty pmiR-Glo, FV-149) reporter plasmid together with 200 pmol of hsa-miR-16-1-3p mimics or scrambled controls, using Lipofectamine 3000 (Thermo Fisher Scientific) following the manufacturer’s instructions. After 24 hours of incubation, luciferase activity was measured with the Dual-Lumi™ Luciferase Reporter Gene Assay Kit (Beyotime Biotechnology, Shanghai, China) and quantified using a ClarioStar microplate reader (BMG Labtech). All experiments were performed in triplicate. Firefly luciferase activity was normalized to Renilla luciferase activity to correct for transfection efficiency and ensure data comparability. Transient Transfection Transient transfection was performed to induce HIF1A overexpression. According to the manufacturer’s protocol, plasmid DNA was mixed with P3000 reagent and Lipofectamine 3000 (Thermo Fisher Scientific) in Opti-MEM medium to prepare transfection complexes. The mixture was added to cells at 60–70% confluence, and incubation was continued for 24 hours. Cells were then harvested for subsequent in vitro validation assays. Cells transfected with the empty vector plasmid (pcDNA) were used as the negative control. Trans-well invasion assay Cell invasive capacity was evaluated using 24-well Transwell chambers equipped with 8-µm pore polycarbonate membranes (SPLInsert™ Hanging, PET membrane, Cat. No. 37124, SPL Life Sciences). Prior to the assay, cells were serum-starved overnight to synchronize metabolic activity. On the following day, 5 × 10⁴ cells suspended in serum-free medium were seeded into the upper chambers, while complete medium containing 10% FBS was added to the lower chambers. After 24 h of incubation at 37°C in a humidified 5% CO₂ atmosphere, non-invading cells on the upper membrane surface were gently removed with a cotton swab. The membranes were then fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Invaded cells were counted under a Leica DFC7000 T microscope, selecting two random fields per replicate. Each experimental condition was performed in triplicate, and quantitative data were analyzed using GraphPad Prism 10. Wound healing assay Cells (1 × 10⁶ per well) were seeded into 6-well plates and cultured overnight until reaching approximately 90% confluence. A straight scratch was then made across the cell monolayer using a sterile 200-µL pipette tip, after which the detached cells were gently washed away with pre-warmed PBS. The medium was replaced with serum-free DMEM. Images of the scratch area were captured at 0 h and 24 h using the EVOS™ M5000 imaging system (Thermo Fisher Scientific). The wound closure was quantified using the image analysis algorithm described in this study 80 . Each experimental condition was performed in triplicate, and two random microscopic fields were analyzed per replicate. Cisplatin sensitivity assay Cells (5 × 10³ per well) were seeded into 96-well plates and allowed to adhere overnight. The next day, cells were treated with cisplatin at concentrations ranging from 0.25 to 80 µM, prepared by twofold serial dilution across ten concentration points, with vehicle-treated wells serving as controls. After 48 h of incubation, cells were fixed with 10% trichloroacetic acid (TCA) at 4°C for 1 h, washed with distilled water, and air-dried. Cells were then stained with 0.4% sulforhodamine B (SRB) at room temperature for 30 min, followed by rinsing with 1% acetic acid to remove unbound dye. The bound dye was solubilized in 10 mM Tris–HCl (pH 10.5), and absorbance was measured at 510 nm using a microplate reader. Absorbance from control wells was defined as 100% viability, and all experimental values were normalized accordingly. IC₅₀ values were determined by nonlinear regression analysis of the dose–response curves using GraphPad Prism 10. All assays were performed in triplicate to ensure reproducibility. Apoptosis assay Cell apoptosis was assessed using the Annexin V–FITC/PI Apoptosis Detection Kit (Seiverbios, Cat. No. G1511-100T). Based on preliminary optimization and published protocols, cisplatin was used at a final concentration of 5 µM, which produced consistent apoptotic responses in A549 cells 81 . After 48 h of incubation, both adherent and floating cells were collected, washed twice with cold PBS, and stained with Annexin V–FITC and propidium iodide (PI) according to the manufacturer’s instructions. Samples were analyzed on a flow cytometer (BD Biosciences, USA), and data were processed using FlowJo software (Tree Star, USA). All experiments were performed in triplicate to ensure statistical reliability. CAM (chick embryo chorioallantoic membrane) Bio-Imaging Assay. Fertilized specific pathogen–free (SPF) chicken eggs were purchased from a certified hatchery (Trade House Ptichnoe, Ltd., Moscow, Russia; https://ptichnoe-td.ru ). Upon arrival, eggs were gently cleaned with sterile water, placed horizontally in an incubator at 37°C and 70% humidity, and automatically rotated to prevent chorioallantoic membrane (CAM) adhesion. This time point was recorded as embryonic day 0 (EMD 0). At EMD 3–4, after visualization of the air chamber and primary blood vessels under candling, approximately 2 mL of albumen was aspirated through the blunt end using an 18 G needle to detach the CAM from the shell. A circular window (~ 2 cm in diameter) was then created on the shell using a precision rotary saw, sealed with 3M semi-permeable film, and returned to the incubator with rotation stopped. During the CAM bio-imaging experiments at EMD 8, A549-Katushka2S-T2A cells, produced following a previously established workflow 34 , were suspended in a serum-free medium combined in a 1:1 ratio with Matrigel (Corning, Cat. No. 356234). These mixtures were then seeded into O-rings (inner diameter 6 mm, outer diameter 10 mm) positioned on the vascular surface of the CAM. A total of 3 × 10⁶ cells in 20 µL suspension were inoculated per egg. A clinically approved anti-VEGFA monoclonal antibody, bevacizumab (Avastin®, 25 mg/mL; Roche, Moscow, Russia), was used for anti-angiogenic treatment in the CAM model. Tumor-bearing CAMs were treated locally at embryonic day 12 (EMD12) and embryonic day 14 (EMD14). For each treatment, 1 µL of bevacizumab standard solution was diluted with 1 µL sterile PBS and carefully applied directly into the PTFE ring containing the tumor. Control tumors received an equal volume (2 µL) of sterile PBS alone 82 . After inoculation, the openings were resealed and incubation continued until EMD 16, when tumors were collected. Primary tumors and tumor vessels were imaged using Leica M60 stereomicroscope, tumor volume was calculated using the formula in the previously study[16]. Blood vessel density was analyzed using the ImageJ based Vessel Analysis tool developed by the National Institutes of Health (USA) ( https://imagej.net/plugins/vessel-analysis ). Metastatic dissemination was assessed by LumoTrace® Fluo system (Abisense LLC, Russia) and calculated using Icy software ( https://icy.bioimageanalysis.org/ ). All experiments were completed before EMD 17, in accordance with international ethical standards exempting pre-hatching embryos from IACUC regulation and the Russian Animal Experiment and Welfare Guidelines 33 , 83 . Immunofluorescence Staining For cellular immunofluorescence, A549 cells were seeded onto sterile 8-well chamber slides (Biologix, Cat. No. B-07-2108). After incubation, cells were gently washed with PBS and fixed in 4% paraformaldehyde for 15 min at room temperature. Following fixation, samples were blocked with 5% bovine serum albumin (BSA) for 1 h and then incubated overnight at 4°C with anti–E-cadherin antibody (1:400, CST, #14472). The next day, slides were washed and incubated with Alexa Fluor 488–conjugated secondary antibody (1:500, Abcam, ab150113) for 1–2 h at room temperature in the dark. Nuclei were counterstained with DAPI mounting medium (Servicebio, G1407). The cytoskeletal organization was visualized using Phalloidin–TRITC conjugate (Solarbio, Cat. No. CA1620), following the manufacturer’s protocol. Images were acquired using the EVOS™ M5000 imaging system (Thermo Fisher Scientific). For tissue section immunofluorescence, CAM tumor nodules harvested on embryonic day 16 (EMD16) were fixed in 4% paraformaldehyde, followed by dehydration in 15% and 30% sucrose gradients. Samples were embedded in OCT compound, and 10 µm cryosections were prepared. Sections were washed with PBS; permeabilization was performed with 0.2% Triton X-100 for 10 min, except for CD105 staining, in which permeabilization was omitted to preserve membrane integrity. After blocking with 5% BSA for 1 h, sections were incubated overnight at 4°C with either rabbit anti–HIF-1α (1:400, Abcam, ab179483) or rabbit anti–CD105 (1:400, Proteintech, 10862-1-AP). The next day, slides were incubated with Alexa Fluor 647–conjugated secondary antibody (1:500, Abcam, ab150079) for 1 h at room temperature in the dark and then mounted. Fluorescence images were captured using the EVOS™ M5000 microscope, and the positive staining ratio of HIF-1α and CD105 was quantified using ImageJ software (NIH, USA). Western blot analysis Cells were lysed on ice in RIPA buffer (Thermo Fisher Scientific) supplemented with a protease inhibitor cocktail (1:100 dilution). Lysates were centrifuged at 12,000 × g for 15 min at 4°C, and the supernatants were collected. Protein concentrations were quantified using the BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of protein were loaded onto SDS–polyacrylamide gels, separated electrophoretically, and transferred to PVDF membranes. Membranes were blocked with 5% BCA in TBST for 1 h at room temperature, then incubated overnight at 4°C with primary antibodies against HIF-1α (1:1000, Abcam, ab179483), CD105 (1:1000, Proteintech, 10862-1-AP), and VEGFA (1:1000, CST, #2463). After three washes with TBST, membranes were incubated for 1 h at room temperature with HRP-conjugated secondary antibody (1:3000, Affinity, S0001). Protein bands were visualized using an ECL detection kit (Bio-Rad, 1705061) and imaged with a ChemiDoc XRS+ system (Bio-Rad). To verify equal protein loading, membranes were stripped using Restore™ Western Blot Stripping Buffer (Bio-Rad, Bulletin 6218), washed thoroughly, and re-probed with β-actin–HRP (1:3000, Servicebio, ZB15001-HRP) on the same blot. Signal intensities were quantified with ImageJ software, and relative protein expression was normalized to β-actin. Multiplex analysis of tumor cell secretome. The tumor secretome was gathered from tumor cell–conditioned medium (TCM) created from stable transfectants of A549 cells grown in hypoxic conditions. A549-miR-16-5p and A549-AVA-miR-SCR cells were used as experimental groups, with A549-miR-SCR cells serving as controls. Cells were cultured to 70–80% confluence, thoroughly washed to remove residual serum components, and subsequently incubated in phenol red–free, serum-free medium supplemented with CoCl₂ to ensure sustained HIF1A activation. After 48 h, conditioned media were collected and filtered through 0.22 µm membranes to obtain cell-free TCM. Secretome profiling was performed using a bead-based multiplex immunoassay targeting 41 human cytokines and chemokines (MILLIPLEX MAP, Millipore). A total of 25 µL TCM per sample was analyzed using Luminex technology on a QuattroPlex biomarker analysis system, following the manufacturer’s instructions. Cytokine concentrations were quantified based on standard curves generated from recombinant standards provided with the assay kit. Bioinformatics analysis RNA-seq data and clinical information of lung adenocarcinoma (LUAD) were obtained from The Cancer Genome Atlas (TCGA) database. All analyses were performed in R. Patients were divided into high- and low-HIF1A groups according to the median expression level. Survival analyses were conducted using the survival and survminer packages, and gene set enrichment analysis (GSEA) was performed with clusterProfiler based on the HALLMARK gene sets from MSigDB. Pathways with adjusted p < 0.05 were considered significantly enriched. To explore gene expression under anti-VEGF therapy, the GSE37138 dataset, derived from non-small cell lung cancer patients treated with bevacizumab, was analyzed. Expression correlations between HIF1A, VEGFA, and CD105 (ENG) were visualized as boxplots using the ggplot2 package. Predicted targets of miR-16-1-3p were obtained from TargetScan, miRDB, and miRWalk, and their intersection was illustrated with a Venn diagram generated by the VennDiagram package. Statistical analysis All statistical analyses were conducted using GraphPad Prism version 10 (GraphPad Software, San Diego, USA). Each experiment was independently performed at least three times, and quantitative data are presented as the mean ± standard deviation (SD). Differences between two groups were analyzed using an unpaired, two-tailed Student’s t-test, whereas comparisons among multiple groups were evaluated by one-way analysis of variance (ANOVA). A p-value < 0.05 was considered statistically significant. Statistical significance in the figures is indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001, and “ns.” denotes no significant difference. Declarations Acknowledgements Not available. Authorship contributions: Yuzhe Wang : Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation, Conceptualization. Wenyu Xue : Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. P.A. Malakhov : Writing – review & editing, Methodology, Investigation, Data curation. A.V. Smirnova : Writing – review & editing, Validation, Methodology, Investigation, Data curation. Margarita Pustovalova : Writing – review & editing, Visualization, Validation, Supervision, Conceptualization. Denis V Kuzmin : Writing – review & editing, Visualization, Supervision, Resources, Project administration, Funding acquisition, Data curation, Conceptualization. Sergey Leonov : Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Investigation, Conceptualization. Data availability The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. The bio-imaging data that support the findings of this study are available from Abisense LLC but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Abisense LLC. Competing interests The authors declare that they have no competing interests. Ethics declarations Not available. Funding This work was supported by the Russian Science Foundation (project No. 23-14-00220). Supplementary Information The following is a link to the electronic supplementary materials. References Herbst, R. 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Frontiers in Bioscience-Landmark 30, 36415 (2025). https://doi.org:10.31083/FBL36415 Additional Declarations There is no conflict of interest Supplementary Files Supplementarydata.pdf Supplemental Material WB.docx Original full length western blots Cite Share Download PDF Status: Under Review Version 1 posted Reviewer # 2 agreed at journal 07 May, 2026 Reviewer # 1 agreed at journal 19 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 Editor assigned by journal 18 Feb, 2026 First submitted to journal 18 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8912330","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":616514137,"identity":"c7f338cf-8bd0-4cfe-87c8-12d564ed6c0d","order_by":0,"name":"Wenyu Xue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYPACGwYDCOMAECcQpSUNpIWxgRQth0nQIj8jx/ABY9v5PHOJ9OcPPtTcYeBnzzHAq8XgRo6xAWPb7WJLoN7GGceeMUj2vCGgRSLHTILhzO3EDTdyGJt5Gw6DDMGvBegw8x8MZ84BtaQ/bP4L1GJPSAvDjRwzBoaKA0AtCYbNjCBbJAj55cyzYomEiuRigzNvDGf2HDvMI3HmWQF+h7Unb/zwwcAuz+B4+oMPP2oOy/G3J2/A7zCBBHBEJIAZQMCDXzkI8B8AUwkwxigYBaNgFIwCDAAAkdpPu+5boQcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2520-2218","institution":"Institute of Future Biophysics","correspondingAuthor":true,"prefix":"","firstName":"Wenyu","middleName":"","lastName":"Xue","suffix":""},{"id":616514138,"identity":"5e6f537d-def4-4d05-a76f-37afcb631665","order_by":1,"name":"Yuzhe Wang","email":"","orcid":"","institution":"Institute of Future Biophysics","correspondingAuthor":false,"prefix":"","firstName":"Yuzhe","middleName":"","lastName":"Wang","suffix":""},{"id":616514139,"identity":"623cfb8c-ccea-4045-bdef-35656e2de468","order_by":2,"name":"Philipp Malakhov","email":"","orcid":"","institution":"Institute of Future Biophysics","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Malakhov","suffix":""},{"id":616514140,"identity":"4ee1e9dc-a0c4-49cd-9d8e-0e938d8330b7","order_by":3,"name":"Anna Smirnova","email":"","orcid":"","institution":"Institute of Future Biophysics","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Smirnova","suffix":""},{"id":616514141,"identity":"acc84b06-6205-4f39-ab73-902fac5ed41e","order_by":4,"name":"Margarita Pustovalova","email":"","orcid":"","institution":"Institute of Future Biophysics","correspondingAuthor":false,"prefix":"","firstName":"Margarita","middleName":"","lastName":"Pustovalova","suffix":""},{"id":616514142,"identity":"292a3091-726c-44d7-8068-2e331411b91d","order_by":5,"name":"Denis Kuzmin","email":"","orcid":"","institution":"Phystech School of Biological and Medical Physics","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"Kuzmin","suffix":""},{"id":616514143,"identity":"93a01ca5-9727-4d34-a5f5-b7b838f14a73","order_by":6,"name":"Sergey Leonov","email":"","orcid":"https://orcid.org/0000-0002-3425-723X","institution":"Moscow Institute of Physics and Technology (MIPT)","correspondingAuthor":false,"prefix":"","firstName":"Sergey","middleName":"","lastName":"Leonov","suffix":""}],"badges":[],"createdAt":"2026-02-18 21:06:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8912330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8912330/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705716,"identity":"879ae949-ae61-4a11-9b39-3bec42d7ddd9","added_by":"auto","created_at":"2026-04-24 09:14:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":560608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of bevacizumab treatment or miR-16-5p overexpression in A549 cells in vivo. \u003c/strong\u003e(A) Representative light macroscopic CAM images (Left panel) of A549 xenografts captured at EMD16 in the PBS control (NT), and either bevacizumab- (AVA) treated or hsa-miR-16-5p overexpressing groups showing vascular networks. Violin plot (Right) quantifying vascular density (%). Scale bars = 2 mm\u003cstrong\u003e \u003c/strong\u003e(B) Representative macroscopic images of tumor nodule tissues (left) excised from CAM and quantified tumor nodule volumes (mm³) (right). Scale bar = 3 mm. (C) Bio-fluorescent images of metastases (left panel) and quantification of Katushka2S-T2A fluorescence-indicated tumor metastases (right graph) formed by control, AVA- and miR-16-5p–treated groups. (D) Immunofluorescence staining of HIF1A (magenta) and DAPI (blue) in the left panel images, and the quantification of fraction HIF1A-positive cells (right graph) in tumor nodule sections. Scale bar =150 μm. (E) Bioinformatic analysis of the GSE37138 transcriptome dataset showing inverse correlation between VEGFA and HIF1A expression in bevacizumab-treated lung cancer patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/3f990f6cb85167792099b1cc.png"},{"id":107560474,"identity":"af2b322d-cd33-4ba1-8c9c-cb8464c87f4b","added_by":"auto","created_at":"2026-04-22 15:50:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":418147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of HIF1A overexpression in vitro and in vivo. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Wound healing assay of the 2D collective migration of HIF1A-overexpressing (FC-3398-HIF1A) compared with control (pcDNA) cells: images of wound area (left) taken at 0h and 24h after “scratch” made, and its quantification (right). Scale bar= 300 μm. (\u003cstrong\u003eB\u003c/strong\u003e) 3D confined migration of HIF1A-overexpressing (A549-FC-3398-HIF1A) compared with control (A549-pcDNA) cells in Boyden chambers: Images at 20x magnification display crystal violet-stained cells on the outer membrane of the inner chamber (top). The quantification of cell migration rate 24 hours after seeding 100000 cells/well in the upper Boyden chamber of a 24-well Transwell insert (bottom). Scale bar = 50 μm. (C) Immunofluorescence staining for E-cadherin (green) and DAPI (blue) showing decreased epithelial marker expression upon HIF1A overexpression. Scale bar = 150μm. (D) HIF1A overexpression-promoted cytoskeletal reorganization and EMT-like phenotype indicated by phalloidin fluorescence staining of F-actin remodeling. Scale bar = 150 μm.\u0026nbsp; (E) Scatter plot (left panel) of flow-cytometric analysis of necrotic (Q1 region, PI+ staining), necroptotic (Q2 region, Annexin V-FITC+/ PI+ staining), truly apoptotic (Q3 region, Annexin V-FITC+), and quantification of the fractions of necroptotic and apoptotic cells (%, right graph). (F) SRB assay of dose-dependent proliferation response of cells at 48 hours after cisplatin treatment (Upper graph). IC50 values (Bottom table) were calculated using built-in algorithms of GraphPad\u003csup\u003eTM\u003c/sup\u003e software. (G) Kaplan–Meier overall survival curves for TCGA-LUAD patients stratified by HIF1A expression levels. (H) GSEA plots showing enrichment of hypoxia, EMT, and angiogenesis pathways in HIF1A-high tumors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/de0650547796c06a1be9df97.png"},{"id":107560476,"identity":"0e70236e-c548-4596-9d95-ff635fd56e89","added_by":"auto","created_at":"2026-04-22 15:50:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":475772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vitro and in vivo effects of hypoxia-induced HIF1A activation. \u003c/strong\u003eMutiplex secretome (A) and Western blot (B) analysis of secreted and cell-associated CD105 protein levels (B, right graph), respectively, in A549 cells under hypoxic conditions induced by treatment with 150 μM CoCl2 for 24 h. (C) Bioinformatic analysis of the transcriptomes of bevacizumab-treated lung cancer patients (GSE37138 dataset) showing correlation between HIF1A and CD105 expressions. In vivo effects of bevacizumab (AVA-HIF1A) treatment or miR-16-5p- overexpression (miR-16-5p-HIF1A) on parental (NT) and HIF1A-overexpressing (HIF1A) A549 cells in CAM in vivo model at EMD 16 post-implantation: D) Representative bright-field macroscopic images (left panel) depict the quantification of tumor-associated vascular density (Violin plot (%), right graph) in tumor cell nodules. Scale bar= 300 um. (E) Bright-field macroscopic images (left panel) of excised tumor nodules and quantification of their volumes (right graph). Scale bar= 300 μm. (F) Representative in vivo bio-fluorescent images (left panel) and quantification of metastasis area formed by Katushka2S-labeled A549 cells(right graph). (G) Immunofluorescent analysis of CD105 (CD105, magenta) expression and DAPI-positive staining of nuclei (DAPI, blue) and overlay images (last column to the right, Merge) in the sections of tumor in ovo nodules. Relative coverage area of CD105+ cells (Graph on the right) was quantified as % of all cells (DAPI+) using ImageJ software. Scale bar= 150 μm.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/cea072b33f60ddba7edabde6.png"},{"id":108490839,"identity":"2b618a3a-3002-4d2c-9785-273c7bdb4ac3","added_by":"auto","created_at":"2026-05-05 09:49:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of miR-16-1-3p and miR-16-5p overexpression in A549 cells in vitro. \u003c/strong\u003e(A) Venn diagram showing amounts of overlapping predicted targets of hsa-miR-16-1-3p derived from TargetScan, miRDB, and miRWalk databases.\u003cstrong\u003e \u003c/strong\u003e(B) Dual-luciferase reporter assay of direct binding of hsa-miR-16-1-3p to the 3′-UTR of HIF1A. Western blot analysis of cell-associated HIF1A and VEGFA production in A549 cells overexpressing either hsa-miR-16-1-3p (C) or hsa-miR-16-5p (D) under hypoxic conditions. Quantifications of the protein levels are shown on the right. (E) The schematic depicts the cooperative actions of hsa-miR-16-1-3p on HIF1A and hsa-miR-16-5p on VEGFA in the A549 cell line. (E) Functional annotation of miR-16-1 precursor by dual regulatory strands: hsa-miR-16-5p and hsa-miR-16-1-3p, the two mature products of tumor suppressive pre-miR-16-1, target HIF1A and VEGFA, respectively, the two master regulators that promote cancer growth, angiogenesis, and metastasis by being upregulated by hypoxia in the A549 cell line.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/6603cc74a8509dd73bc7c8cd.png"},{"id":107705557,"identity":"34f74a95-5db5-44b2-9b52-532fc497e969","added_by":"auto","created_at":"2026-04-24 09:13:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":517280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiR-16-1-3p mitigates HIF1A-related pro-metastatic and anti-apoptotic effects in A549 cells.\u003c/strong\u003e Cells overexpressing either miR-16-1-3p (miR-16-1+pcDNA) or scrambled miR (miR-SCR+pcDNA) sequences and co-transfected with empty vector (pcDNA) were transiently transfected with FC3398 plasmid encoding HIF1A (miR-16-1+FC3398-HIF1A): (A) 2D collective migration (Wound-healing assay) showing images of wound area (left) taken at 0h and 24h after “scratch” made, and its quantification (right). Scale bar= 300 μm. (B) 3D confined migration of cells in Boyden chambers: Images at 20x magnification display crystal violet-stained cells on the outer membrane of the inner chamber (left panel). The quantification of cell migration rate (graph on the right) 24 hours after seeding 100000 cells/well in the upper Boyden chamber of a 24-well Transwell insert. Scale bar= 50 μm. (C) Immunofluorescence staining for E-cadherin (green) and DAPI (blue) showing decreased epithelial marker expression upon HIF1A overexpression. Scale bar= 150 μm. (D) Cytoskeletal reorganization and mesenchymal-like phenotype indicated by phalloidin fluorescence staining of F-actin remodeling. Scale bar= 150μm. (E) Scatter plot (top panel) of flow-cytometric analysis of necrotic (Q1 region, PI+ staining), necroptotic (Q2 region, Annexin V-FITC+/ PI+ staining), truly apoptotic (Q3 region, Annexin V-FITC+), and quantification of the fractions of necroptotic and apoptotic cells (%, bottom graph). (F) SRB assay of dose-dependent proliferation response of cells at 48 hours after cisplatin treatment (Upper graph). IC50 values (Bottom table) were calculated using built-in algorithms of GraphPad\u003csup\u003eTM\u003c/sup\u003e software.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/972d10da121ea3ea01e0ecbf.png"},{"id":107560479,"identity":"9848ca85-9dc8-412c-8706-71e2e44a37fe","added_by":"auto","created_at":"2026-04-22 15:50:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":479958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined miR-16-5p and miR-16-1-3p overexpression synergistically suppresses angiogenesis, tumor growth, and metastasis. \u003c/strong\u003eEffects of bevacizumab (AVA) treatment, miR-16-5p (hsa-miR-16-5p) overexpression, and their combinations with hsa-miR-16-1-3p overexpression (“AVA+hsa-miR-16-1-3p” and “hsa-miR-16-5p+hsa-miR-16-1-3p”) in A549 compared to parental (NT) cells in CAM in vivo model at EMD 16 post-implantation: (A) Representative bright-field macroscopic images (left panel) depict the quantification of tumor-associated vascular density (Violin plot (%), right graph) in tumor cell nodules. Scale bar= 300 μm. (B) Bright-field macroscopic images (left panel) of excised tumor nodules and quantification of their volumes (right graph). Scale bar= 300 μm. (C) Representative in vivo bio-fluorescent images (left panel) and quantification of metastasis area formed by Katushka2S-labeled A549 cells (right graph). (D) Western blot analysis of cell-associated HIF1A and CD105 proteins in A549 cells under the same treatments as in vivo. Quantifications of protein levels are shown on the right. Immunofluorescence staining of either HIF1A (E, magenta) or CD105 (F, magenta), and DAPI (blue) in tumor nodule sections. Quantification of fluorescence intensity for each protein is shown on respective graphs on the right. Scale bar= 150 μm.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/81e1dc9c029a4d469d8da00f.png"},{"id":108494290,"identity":"f828dfd1-33d5-4be0-8293-6a08e13a6f0b","added_by":"auto","created_at":"2026-05-05 10:03:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2965358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/2cc5ff94-ba3c-49ab-ad16-2c0c81597058.pdf"},{"id":107560473,"identity":"3316546f-257a-4654-86db-3bf44181c606","added_by":"auto","created_at":"2026-04-22 15:50:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":441317,"visible":true,"origin":"","legend":"Supplemental Material","description":"","filename":"Supplementarydata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/1072a8ff1f322705c1ee6dad.pdf"},{"id":107706422,"identity":"07ea3afd-1000-40e8-a7e3-318f3fef0815","added_by":"auto","created_at":"2026-04-24 09:18:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27979510,"visible":true,"origin":"","legend":"Original full length western blots","description":"","filename":"WB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8912330/v1/d3c44db61fe7a22369faad13.docx"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Harnessing the Cooperative Function of Duplex From pre-miR-16 Hairpin to Simultaneously Inhibit VEGF and Hypoxia Pathways in Human Non-Small Cell Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbout 85% of lung cancer cases are non-small cell lung cancer (NSCLC), mainly including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Existing screening guidelines often overlook lung cancer patients, leading to late-stage diagnoses of NSCLC or diagnosis only when symptoms appear \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite the availability of various therapies for NSCLC, the five-year survival rate remains a disheartening 21%. This is primarily because 75% of patients are diagnosed at advanced stages and because many develop resistance to treatment \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The lack of research on mechanisms behind rapid NSCLC progression and therapy resistance has limited new treatment options.\u003c/p\u003e \u003cp\u003eMiRNAs, the non-coding RNAs measuring 18\u0026ndash;25 nucleotides, are significantly affecting tumor growth \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. By interacting with matching sequences in the 3\u0026prime;-UTR of target mRNA, miRNAs successfully silence target gene expression. This occurs either through mRNA degradation or by inhibiting mRNA translation \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. More than 60% of mRNAs are thought to be conserved targets of endogenous miRNAs, and including less conserved sites may reveal even more, hinting at a previously unknown post-transcriptional RNA signaling network \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Research shows that miRNAs are essential for regulating gene expression after transcription, playing a key role in maintaining cellular balance in both normal and NSCLC-related functions \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Exploring the connection between miRNA regulation and mechanisms of drug resistance marks a novel approach in tackling anti-tumor drug resistance. The microRNAome, constantly changing and made up of miRNAs, attracts attention because the mechanisms and creation processes of these molecules are still not fully understood.\u003c/p\u003e \u003cp\u003eInitially, it was thought that the two strands were the complete products of the miRNA precursor (pre-miR), but the \u0026ldquo;passenger\u0026rdquo; strand (miR-3p or \u0026ldquo;star\u0026rdquo; strand- miRNA*), quickly degrades after the guide strand (miR-5p or \u0026ldquo;lead\u0026rdquo; strand) is used, making it rare in the mature miRNA pool. Despite uncertainty about how strand selection and dominant expression operate, numerous miRNA* species build up and exert considerable post-transcriptional regulation on their conserved targets \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Significant support for this concept has come from multiple experiments examining the regulatory roles of miRNAs*, which have not been identified as post-transcriptional modulators. Most functionally validated miRNAs were guide strands (miR-5p), with only a limited number of miR-3p or star strands identified as possible regulators. Despite modest signal-to-noise ratios, miRNA* species are currently considered as a key in the RNA regulatory network, leading to a new theory: \u0026ldquo;target-two-sets-of-genes-with-one-pre-miRNA\u0026rdquo;\u003csup\u003e11\u003c/sup\u003e. The regulatory functions of miRNA* species necessitate an overhaul of the miR/miR* naming convention, which should be achieved through a comprehensive annotation of miR-5p/3p sequences derived from their hairpin precursors.\u003c/p\u003e \u003cp\u003eIn this regard, the functional annotation of the miR-16 family (miR-16-1, miR-16-2, miR-16-5p) is important, as miR-16 and miR-34 are key tumor suppressor miRNAs in cancer \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The canonical miR-16, also called \u0026ldquo;guide\u0026rdquo; miR-16-5p strand, is synthesized by RNA polymerase II from either the \u003cem\u003eMIR-16-1\u003c/em\u003e loci on chromosome 13q14 or the \u003cem\u003eMIR-16-2\u003c/em\u003e loci on chromosome 3q25 in the human genome\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, only \u003cem\u003eMIR-16-1\u003c/em\u003e loci encodes miR-16-1-3p, the \u0026ldquo;passenger\u0026rdquo; strand, also known as miR-16-1*. Insightfully, miR-16-5p is often missing or underexpressed in NSCLC compared to healthy individuals \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Nonetheless, the expression of miR-16 \u0026ldquo;passenger\u0026rdquo; strands in NSCLC has not been analyzed yet. Our research indicates that increasing miR-16-1* and miR-16-2* levels enhances NCLC cell sensitivity to X-rays, suggesting their unknown role in NSCLC resistance to DNA damage\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The functional significance of miR-16 family \u0026ldquo;passenger\u0026rdquo; strands in other NSCLC therapies remains elusive.\u003c/p\u003e \u003cp\u003eTumor angiogenesis remains a pivotal driver of malignant progression and metastasis, supplying oxygen and nutrients while shaping a permissive microenvironment for invasion and immune evasion \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Studies indicate that anti-angiogenic therapy has emerged as a pivotal treatment approach for NSCLC \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Accordingly, VEGFA-targeting agents, like bevacizumab, have emerged as essential elements in the first-line treatment of NSCLC and various other solid tumors \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While bevacizumab can temporarily benefit patients and hinder progression, it often results in resistance, recurrence, or metastasis, indicating the shortcomings of a single targeting approach \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Increased evidence shows that secondary hypoxia significantly contributes to drug resistance. Reduced vessel density limits blood flow and oxygen delivery, which activates HIF (hypoxia-inducible factor) activation \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Elevated HIF can reprogram metabolism, induce EMT, and engage alternative pro-angiogenic circuits, thus enabling tumor adaptation and regrowth despite VEGF inhibition \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This hypoxia-driven feedback is viewed as a crucial resistance element, but its initial triggers and later amplifiers are not fully clarified.\u003c/p\u003e \u003cp\u003eIn this context, non-coding RNAs have emerged as promising modulators due to their capacity for multitarget regulation \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Notably, miRNAs are often conceptualized as fine-tuners rather than binary switches, conferring robustness and minimizing drastic side effects from treatment \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Hypoxia induces the downregulation of miR-16, subsequently leading to an increase in VEGF-A expression in a human nasopharyngeal carcinoma cell line CNE \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Moreover, limited studies on miR-16 indicate that its passenger strands miR-16-1-3p may possess intrinsic tumor-suppressive function \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, hinting at possible functional complementarity with the guide strand. However, such a functional \"guide\" and \"passenger\" strand complementary impact in anti-VEGF therapy resistance NSCLC is completely unexplored yet.\u003c/p\u003e \u003cp\u003eRight now, very few studies examine the functional importance of dual sister strands in depth within one investigation. The absence of thorough exploration underscores a major deficiency in our comprehension of the complexities at play. As a result, due to unintentional oversight in miRNA* expression, some existing miRNA-targeting strategies in cancer research are seen as less reliable. Although dual sister strands are essential for various biological processes, few studies have dedicated themselves to uncovering their operational significance. There is a strong need for deeper analysis in this research area.\u003c/p\u003e \u003cp\u003eHere, we propose and explore \u0026ldquo;dual regulatory strand\u0026rdquo; miR-16 strategy, that includes overexpressing miR-16-5p, which inhibits VEGFA-driven tumor and blood vessel growth, and overexpressing miR-16-1-3p, the passenger strand, that alleviate HIF1A hypoxia feedback. These naturally derived miRNA duplexes target the initiation and compensatory responses of the VEGF-HIF axis, resulting in a more balanced and potentially long-lasting intervention for angiogenesis and hypoxia in cancer. This phenotypic profiling of dual regulatory strands ensures that both aspects of the pathway are managed effectively, possibly leading to improved patient outcomes. Additionally, integrating functionally annotated natural miRNA-5p/3p in one study could significantly decrease adverse reaction risks, as dual sister strands of a miRNA precursor are rarely investigated simultaneously in the same lab. This research could greatly improve anti-cancer treatments for conditions related to excessive blood vessel growth and low oxygen levels.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVEGFA inhibition by bevacizumab or overexpressing miR-16-5p suppresses tumor growth but triggers compensatory HIF1A upregulation\u003c/h2\u003e \u003cp\u003eBevacizumab decreased micro-vessel density and tumor size in glioblastoma U251-HRE xenografts but raised intratumor hypoxia and HIF1 protein levels, along with its target gene transcripts\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. To study the early anti-angiogenic response to VEGFA blockade in NSCLC, A549 xenografts were established using the CAM model. Bevacizumab treatment or higher levels of miR-16-5p in A549 cells significantly decreased both vascular density and tumor size, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B, compared to untreated samples. We show for the first time that both treatments greatly reduce metastases, as shown by the quantitative bio-fluorescent imaging data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) in spontaneous metastasis CAM model following our established protocol \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, immunofluorescent analysis showed high levels of HIF1A in the tumor nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) after both treatments, suggesting that reduced blood vessel formation in the tumor microenvironment triggers a hypoxia-like response. Consistently, bioinformatics analysis of the GSE37138 cohort demonstrated that bevacizumab-treated lung cancer patients had reduced VEGFA expression and elevated HIF1A levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Together, these findings reveal that blocking VEGFA is effective in shrinking neovascularization and tumors and limit metastases, but may create a prolonged hypoxia-driven response, as reflected by increased HIF1A levels in NSCLC cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eElevated HIF1A diminished cell adhesion and epithelial characteristics, while increasing survival and cisplatin resistance in NSCLC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHIF1A activation in tumor cells generally encourages tumor development, but its effects can change with the context, occasionally hindering growth by inhibiting nutrient pathways like aspartate or by stimulating self-digestion (autophagy) in cancer cells, though this is cell-type specific \u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo delineate the functional consequences of HIF1A expression on NSCLC cells, A549 cells were transfected with an HIF1A-overexpressing plasmid (A549-FC-3398-HIF1A). Under normoxic conditions, HIF1A overexpression greatly enhanced 2D collective and 3D confined migrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;B). Furtheremore, overexpressing HIF1A in A549 cells altered their morphology from cobblestone to fibroblast-like with a spindle shape. This change was marked by decreased E-cadherin levels and increased cytoskeletal remodeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;D), indicating reduced cell adhesion and the acquisition of an EMT-like phenotype. Higher levels of HIF1A also decreased the number of apoptotic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and raised cisplatin-induced IC50 values, indicating resistance to cisplatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Survival analysis of the TCGA-LUAD database demonstrated that patients with high HIF1A expression had significantly poorer overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Gene set enrichment analysis further revealed enrichment of hypoxia, EMT, and angiogenesis pathways in HIF1A-high tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), supporting its role in promoting malignant adaptation under stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHypoxia-induced HIF1A augments CD105 in vitro and reverses anti-angiogenic effects of VEGFA blockade in vivo.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhen oxygen levels are normal, the HIF1A is expressed but undergoes rapid degradation. Under low oxygen conditions, this subunit is stabilized, enters the nucleus \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and activates critical genes for survival, blood vessel formation, and adaptation \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Encoded by such genes, soluble factors, whether free or encapsulated in extracellular vesicles (EVs), are crucial for the tumor cells' relationship with their microenvironment. These factors play a crucial role in fostering tumor growth, creating pre-metastatic niches, and eventually contributing to metastasis \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHence, we first examined the profile of A549 cell secretome under hypoxic conditions induced by CoCl\u003csub\u003e2\u003c/sub\u003e treatment. Under hypoxic conditions, CD105 protein levels in the conditioned medium significantly increased after treatment with bevacizumab or overexpressing miR-16-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Western blot analysis confirmed the upregulation of cell-associated CD105 protein following bevacizumab- or miR-16-5p\u0026ndash;mediated VEGFA targeting under hypoxia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Bioinformatic analysis of the GSE37138 dataset also verified positive correlation between expressions of CD105 and HIF1A in bevacizumab-treated tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo test this response in vivo, we generated stable HIF1A overexpression in both A549-miR-SCR and A549-miR-16-5p cells. Important, in such HIF-high paradigm, bevacizumab treatment no longer reduced vascularization. Instead, vessel density increased, accompanied by unchanged heightened tumor size and metastases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u0026ndash;F). Strikingly, hsa-miR-16-5p overexpressing was able to thwart the elevation of metastasis associated with HIF1A, yet the challenges of increased vascularization and tumor magnitude persisted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u0026ndash;F). In the same paradigm, both treatments effectively enhanced CD105 expression in tumor tissues, according to quantitative immunofluorescence analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Hence, HIF1A overexpression leads to a vascular response through CD105, counteracting the anti-angiogenic effects of VEGFA inhibition, demonstrating a possible vital mechanism of adaptive resistance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExpression of passenger strand, miR-16-1-3p, targets HIF1A and complements miR-16-5p-mediated VEGFA suppression in vitro\u003c/h3\u003e\n\u003cp\u003eWhen the vicious cycle between HIF1A and VEGFA pathways is established, the low oxygen tension activates the expression of HIF-1α, which subsequently enters into the hypoxia-induced HIF pathways. Hence, we attempted to identify an agent that would specifically control HIF1A activity in terms of both protein and function to break the link between HIF1A and VEGFA pathways.\u003c/p\u003e \u003cp\u003eBioinformatics analysis using TargetScan, miRDB, and miRWalk found 456 common targets of miR-16-1-3p, with HIF1A being consistent across all three databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The direct binding of miR-16-1-3p to the HIF1A 3\u0026prime;-UTR was confirmed by a significant rise in normalized luciferase activity in the dual-luciferase reporter assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In the hypoxic conditions, overexpressing hsa-miR-16-1-3p using lentivirus significantly reduced HIF1A protein levels in A549 cells compared to the control (A549 cells overexpressing scrambled, miR-SCR sequence), while cell-associated VEGFA levels remained unchanged, as shown by Western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Conversely, lentivirus-driven overexpression of hsa-miR-16-5p specifically decreased VEGFA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) in the same NSCLC cell line. These results imply that both passenger and lead strands of the same pre-miR-16-1 target different elements of the interplay between VEGFA and HIF1A, forming a dual-layer mechanism to control hypoxia-induced feedback (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003emiR-16-1-3p attenuates HIF1A-related malignant phenotype in vitro\u003c/h3\u003e\n\u003cp\u003eFunctional rescue assays were performed to test if the hsa-miR-16-1-3p overexpression can counteracts HIF1A-related malignant phenotypes of A549 cells in vitro. Cells transfected with an empty vector (pcDNA) and miR-16-1-3p (miR-16-1\u0026thinsp;+\u0026thinsp;pcDNA) significantly reduced both 2D (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and 3D (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) migration compared to control cells transfected with empty vector and scrambled miR (miR-SCR+pcDNA). An increase in E-cadherin expression, actin cytoskeleton reorganization and epithelial-like shape featuring were observed in these cells compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). Transient overexpression of HIF1A in the same cells restored both migration types and inhibited E-cadherin and actin reorganization, confirming that these processes are related to HIF1A expression. Apoptosis increased after miR-16-1-3p overexpression, which was partially reversed by transient HIF1A overexpression as revealed by FACS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Overexpression of miR-16-1-3p enhanced the sensitivity to cisplatin, while the restoration of HIF1A expression reestablished drug resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Together, these findings indicate that miR-16-1-3p upregulation can exert its suppressive function in the hypoxic feedback loop, counteracting HIF1A-related pro-metastatic and anti-apoptotic effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverexpressing both guide and passenger miRNA strands decreases HIF1A and CD105 bursts caused by anti-VEGFA treatments, halting angiogenesis, tumor growth, and metastasis in vitro and in vivo.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFinally, we assessed whether boosting \u0026ldquo;passenger\u0026rdquo; miR-16-1-3p strand could mitigate the effects of VEGFA blockage caused by either Avastin or elevated \u0026ldquo;guide\u0026rdquo; miR-16-5p strand overexpression. In the CAM model, combining overexpression of miR-16-1-3p with either miR-16-5p or Avastin showed the strongest effect in reducing vascularization, tumor size, and metastasis compared to each treatment alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;C). The same combinations demonstrated concurrent downregulation of cell-associated both HIF1A and CD105 as indicated by Western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and by immunofluorescence analysis of tumor nodule sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). For the first time, this data shows that using the natural strands of the pre-miR-16-1 duplex to target both VEGFA and HIF1A/CD105 interplay in the angiogenic pathway can effectively prevent NSCLC cell growth, angiogenesis, and spread.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAnti-angiogenic therapy represents a cornerstone in the management of lung cancer and other solid malignancies. Among these agents, VEGFA inhibitors such as bevacizumab have become a standard component of first-line regimens for NSCLC \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. By blocking the interaction between VEGFA and its receptors, bevacizumab suppresses tumor vascularization, reduces perfusion, and delays tumor growth. Traditionally, Bevacizumab's antitumor activity is linked to its capacity to suppress angiogenesis. This mechanism is founded on the concept that tumors require new blood vessels for growth and spread \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. VEGFA binding with VEGFR2 on endothelial cells initiates signals for their proliferation, migration, thereby promoting angiogenesis \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Bevacizumab attaches to VEGFA, blocking its connection with VEGFRs on endothelial cells and stopping vital processes in tumor blood vessel development. However, despite its initial efficacy, clinical experience has revealed a recurrent pattern of resistance, relapse, and metastasis after prolonged treatment \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This paradox underscores a fundamental limitation of single-pathway blockade\u0026mdash;namely, that inhibition of VEGF signaling can trigger complex adaptive responses within the tumor microenvironment, ultimately diminishing therapeutic efficacy. VEGFA-independent angiogenesis seems to underlay the bevacizumab resistance, yet attempts to inhibit it have often failed. These challenges significantly undermine the conventional anti-angiogenic strategies that utilize bevacizumab. Exploration of effects, in addition to anti-angiogenic properties, is needed.\u003c/p\u003e \u003cp\u003eResearch now targets the direct cytotoxic effects of bevacizumab on lung cancer cells both in vitro and in vivo \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We assessed the effects of VEGFA neutralization on established tumors by locally administering bevacizumab to pre-formed tumors in the CAM model. In this experimental design, bevacizumab was directly administered to the tumor mass post-implantation, effectively reducing the nonspecific exposure of the surrounding CAM tissue. Although bevacizumab treatment markedly reduced VEGFA levels in the A549 tumor cell secretome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), disrupting their VEGFA-mediated autocrine signaling, yet it does not fully prevent tumor growth in the CAM (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;C). The results suggest that just inhibiting VEGFA is insufficient to prevent tumor development in the CAM model, indicating other non-VEGF pathways are activated.\u003c/p\u003e \u003cp\u003eThese results highlight the weaknesses of single-target anti-VEGF therapy and underscore the importance of using multi-level treatments to address adaptive resistance.\u003c/p\u003e \u003cp\u003ePrevious studies have identified HIF1A upregulation as a major driver of acquired resistance following VEGF inhibition. Reduced vascular density leads to impaired oxygen delivery and decreased intratumoral oxygen tension, activating HIF1A-mediated hypoxia response programs that promote tumor survival, migration, and vascular remodeling \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In line with this concept, our findings demonstrated that targeting VEGFA with miR-16-5p most effectively decreased early angiogenesis and lowered tumor size and metastasis in the CAM model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C) compared to bevacizumab (as positive control), significantly complementing the range of tools modulating vessel pruning and inhibiting neoangiogenesis. Yet, both treatments resulted in a marked rise in HIF1A expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating the onset of severe hypoxic stress pointed previously \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Consistent with this, our in vitro experiments demonstrated that HIF1A activation was accompanied by increased migratory capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B), enhanced acquisition of mesenchymal traits and resistance to cisplatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D, F), while reducing programmed cell death (apoptosis and necroptosis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These data confirm that hypoxia is the main cause of tumor angiogenesis, creating a vicious cycle between hypoxia and angiogenesis in tumors \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn low-oxygen conditions, HIF-1 enhances CD105 (Endoglin, encoded by \u003cem\u003eENG\u003c/em\u003e gene) protein, mRNA, and promoter activity by engaging with a specific HRE (Hypoxia-Responsive Element) in the \u003cem\u003eENG\u003c/em\u003e promoter \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Significant pile of data indicates that endoglin's role in tumor cell behavior is context-dependent, which means that different cancers need specific strategies \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Additionally, increased concentrations of endoglin are linked to unfavorable outcomes in certain cancer patients \u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. However, endoglin's impact on cancer is still controversial; it seems to block tumor angiogenesis but can also lead to a more aggressive form of myeloma and breast cancer. We still do not fully understand the role of endoglin, warranting additional research on various tumor types to analyze how ENG-expressing EVs might shape the tumor microenvironment. Moreover, the role of endoglin in NSCLC was not explored yet.\u003c/p\u003e \u003cp\u003eNotably, both bevacizumab or miR-16-5p overexpression reprogrammed secretory profile under hypoxic conditions; markedly elevating both secreted and cell-associated CD105 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). The change had a significant impact in vivo: CD105 expression in tumor nodules increased, angiogenesis inhibition was reversed, micro-vessel density in the CAM improved, although both tumor volume and metastasis remained stable instead of declining (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F). For the first time, we demonstrate that endoglin\u0026rsquo;s expression correlates with neoangiogenesis under hypoxia in a human NSCLC line, differing from its previously identified tumor-promoting role in myeloma and breast cancer.\u003c/p\u003e \u003cp\u003eCollectively, these findings delineate a compensatory feedback loop in which VEGFA inhibition not only suppresses angiogenesis but also intensifies tumor hypoxia, leading to HIF1A activation and the induction of downstream adaptive pathways. Sustained HIF1A upregulation enhanced tumor plasticity and motility while promoting CD105 expression and secretion, thereby reinitiating pro-angiogenic signaling. The resulting dual effect\u0026mdash; the revascularization on the background of unchanged proliferative and metastatic activity\u0026mdash;represents a morpho-functional rebound that undermines anti-VEGF efficacy. Hence, HIF1A-driven rejuvenation of CD105 may be a key factor in resistance to anti-angiogenic therapy.\u003c/p\u003e \u003cp\u003eThis assumption has very strong experimental and clinical grounds. Multiple studies have reported that CD105 (Endoglin) upregulation constitutes an alternative pro-angiogenic pathway following VEGF blockade \u003csup\u003e\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. In colorectal cancer, patients treated with bevacizumab and those with metastasis have higher CD105 levels compared to untreated patients. The link between CD105 and VEGF in untreated cases is lost after treatment, indicating that CD105 may operate independently when VEGF signaling is low \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Similarly, studies with animal models reveal that residual vasculature after VEGF inhibition has high CD105 expression, with CD105 mRNA being significantly upregulated \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Clinical trials combining bevacizumab with the anti-CD105 antibody TRC105 have shown better antitumor activity than using either treatment alone for recurrent glioblastoma and metastatic renal cell carcinoma \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. CD105 transcription directly driven by HIF1A provides a mechanistic rationale for why CD105 re-expression occurs under hypoxic conditions induced by VEGFA inhibition. Indeed, bevacizumab is reported to increase TGFβ1 and CD105 expression, promoting angiogenesis in hypoxic conditions. In contrast, multitarget receptor tyrosine kinase (RTK) inhibitors can reverse this effect \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, reinforcing the sequence: \"VEGFA suppression \u0026rarr; HIF1A/CD105 activation \u0026rarr; compensatory angiogenesis.\" Importantly, CD105 expression is not restricted to endothelial cells. Emerging evidence shows that tumor cells themselves can produce and secrete CD105, thereby contributing directly to angiogenesis and immune evasion \u003csup\u003e\u003cspan additionalcitationids=\"CR68 CR69\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. In EGFR-mutant NSCLC, blocking CD105 has even been shown to restore the drug sensitivity of osimertinib-resistant cells \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. These reports collectively align with our findings, supporting the concept that CD105 functions as a pivotal nexus between endothelial remodeling and tumor cell\u0026ndash;autonomous adaptation during anti-VEGF therapy. This discovery underscores the significance of understanding interactions between HIF1A and VEGFA pathways in cancer treatment, as tumors can adapt to therapies aimed at slowing their growth. Continuous exploration in these interactions will provide valuable insights into how tumors evade therapeutic strategies targeting angiogenesis.\u003c/p\u003e \u003cp\u003eBeyond antibody-based anti-angiogenic therapeutics, microRNAs (miRNAs) offer a distinctive advantage due to their multi-target, network-level regulatory potential. We functionally explored the miR-16 family, where overexpressing guide miR-16-5p strand under hypoxia, similar to bevacizumab, lowers VEGFA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), thereby reducing blood vessel development and attenuating of tumor growth and spread (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). However, the outcome of the overexpression was significantly increasing HIF1A expression suggesting the hypoxia onset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Remarkably, miR-16-5p overexpression alone under hypoxia, like a bevacizumab treatment, augments HIF1A-driven expression of both secreted and cell-associated CD105 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B) in vitro. Insightfully, this CD105 burst impedes completeness of anti-angiogenic and anti-tumorigenic effects of the guide strand on NSCLC cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E).\u003c/p\u003e \u003cp\u003eOur findings highlight that the majority of valid miRNAs are guide strands (like miR-16-5p), with only a small number of passenger or star strands as candidates for regulation. A key reason for this issue is that rare miRNAs have not been widely acknowledged as part of the post-transcriptional regulatory network. Concerns have been raised about the premature miRNA-targeting approach because it goes against the fact that most miRNA precursors produce two mature regulatory miRNAs. MiRNA maturation occurs in stages, starting from the primary transcript to the hairpin precursor, and ends with the mature functional form, which typically results in two separate regulatory single-stranded RNAs. Now, passenger miRNA species play a vital role in the RNA regulatory network, despite their low signal-to-noise ratios. A new theory, called \"target-two-sets-of-genes-with-one-pre-miRNA,\" has been proposed \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In our study, passenger miR-16-1-3p strand was verified to reduce HIF1A expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C) and blood vessel formation, while also inhibiting HIF1A-driven EMT, apoptosis resistance and cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). By overexpressing miR-16-5p and miR-16-1-3p together, both the initiation and compensation phases of the VEGF\u0026ndash;HIF axis are suppressed, inhibiting VEGFA-driven angiogenesis and HIF1A feedback activation. In the CAM model, this \u0026ldquo;dual regulatory strand\u0026rdquo; strategy more effectively reduced vascular density, significantly reducing tumor growth and metastasis compared with single-pathway inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a potential clinical standpoint, our study is distinct from previously explored combinations such as bevacizumab plus TRC105. In light of bevacizumab's frequent pairing with chemotherapeutics, researchers need to investigate the combined cytotoxic effects and how tumor cells resist this treatment in vitro. Consequently, insightful recommendations for clinical use can be delivered. Ultimately, it is essential to translate in vitro discoveries into in vivo environments to confirm the mechanisms and effects identified. Advanced experimental designs are crucial to distinguish the direct cytotoxic effects of bevacizumab from the indirect ones through immune modulation. The analysis should compare immunocompromised and immunocompetent animal models to reveal how immune mechanisms influence the antitumor effects of bevacizumab alone or in combination with miRNA. Manipulating pre-miRNA can effectively modify target miRNA expression, while also potentially causing random phenotypic effects due to changes in miRNA* expression, reflecting our inadequate understanding of methods. Our approach reveals a dual-target mechanism where overexpressing duplexes from each strand of the same pre-miRNA regulate VEGFA and HIF1A together, maintaining safe intrinsic molecular balance. Our findings somewhat bolster the promising concept of a microRNA-based therapeutic \u0026ldquo;One-Two Punch\u0026rdquo; strategy aimed at effectively targeting cancer \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Hence, since we identify the NSCLC-associated sister miRNAs, targeting the pre-miRNA-16-1 could be an effective treatment strategy\u0026mdash;a \u0026ldquo;one-two punch.\u0026rdquo; Inspired by the therapeutic potential, several miRNA therapies are being trialed clinically to uncover their true value in treatment.\u003c/p\u003e \u003cp\u003eIn summary, this study highlights the key role of the HIF1A\u0026ndash;CD105 pathway in anti-VEGF resistance and presents a new miR-16 \u0026ldquo;dual regulatory strand\u0026rdquo; strategy that targets both VEGFA and HIF1A. This strategy offers a low-toxicity alternative for addressing bevacizumab resistance in NSCLC, eliminating side effects of anti-VEGF therapies.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eOur experiments were performed in only one NSCLC cell line, A549. A549 is a standard cell line regarding miRNA expression and has been used in studies about miRNAs and biological interactions \u003csup\u003e\u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Correlations between AGO2 binding sites and RNA levels of target genes might vary in different cell lines, conditions, or in vivo environments. Establishing correlations on a case-by-case basis may be essential instead of presuming a direct link between binding and gene repression.\u003c/p\u003e \u003cp\u003eThe CAM model provides a rapid in vivo system for vascular evaluation but cannot fully capture long-term adaptive remodeling following chronic hypoxia. Although HIF1A overexpression partially simulates this process, validation in murine xenograft models and clinical specimens will be essential to confirm the translational potential of this \u0026ldquo;dual regulatory strand\u0026rdquo; miRNA approach.\u003c/p\u003e \u003cp\u003elncRNA PVT1 acts as a miRNA sponge and negatively regulates miR-16-5p expression. Targeted loss of miR-16-5p partially rescues the suppressive effect induced by PVT1 knockdown. Overexpression of VEGFA is known to modulate the AKT signaling cascade by activating vascular endothelial growth factor receptor 1 (VEGFR1). lncRNA PVT1 knockdown suppresses CRC progression via inhibiting miR-16-5p-mediated VEGFA/VEGFR1/AKT signaling [24]. We did not conduct deep RNA sequencing on A549 cells overexpressing miR-16-5p and miR-16-1-3p, highlighting the need for further research.\u003c/p\u003e \u003cp\u003eRecent research indicates that resistin promotes VEGF-A expression and angiogenesis by inhibiting miR-16-5p expression through the PI3K/Akt signaling pathways in chondrosarcoma [25]. While it is outside the scope of this study, understanding the interactions between the PI3K/Akt signaling pathway and miR-16-5p/3p overexpression in NSCLC is important.\u003c/p\u003e \u003cp\u003eThe last but not least, the canonical activity of miR-16, which degrades oncogenes such as cyclin D3, is dysfunctional in uveal melanoma (UM). MiR-16 primarily engages in non-canonical base-pairing with a number of specific mRNAs, thereby enhancing their expression levels \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. The upregulation of expression may stem from a non-canonical mechanism involving a miRNA that binds to the 3\u0026prime;-UTR and stimulates gene expression \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Our comprehensive study did not delve into the analysis of either the transcriptome or the proteome of A549 cells following the overexpression of both guide and passenger strands of miR-16. This gap left us without a clear picture of all the molecular changes resulting from this overexpression. The data we collected does not fully reveal how miR-16 affects gene expression and protein synthesis in A549 cells. Hence, further research would be required to explore these critical aspects more thoroughly.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and treatments\u003c/h2\u003e \u003cp\u003eHuman cell lines A549, HEK293T, and HeLa were obtained from (Russian Cell Culture Collection of Vertebrates, Institute of Cytology, RAS, Russia). All cells were cultured in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM) (NPP PanEko LLC, Moscow, Russia) supplemented with 10% fetal bovine serum, 1% penicillin\u0026ndash;streptomycin (5000 U/mL), and 1% L-glutamine. Cultures were maintained at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂, and the medium was replaced twice weekly. All cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be mycoplasma-free using the MycoReport Mycoplasma Detection Kit (Cat. No. MR001, Evrogene, Russia).\u003c/p\u003e \u003cp\u003eThe hypoxia in vitro was induced by the treatment of A549 cells with cobalt chloride as previously described \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The optimal time and CoCl₂ concentration used for hypoxia induction in vitro was determined experimentally (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eFor in vitro experiments, including apoptosis assays and fluorescence staining, A549 cells were transiently transfected with miR-16-1-3p mimic or a scrambled negative control mimic (miR-Scr) using Lipofectamine\u0026trade; 3000 (Thermo Fisher Scientific). Cells at 60\u0026ndash;70% confluence were transfected with microRNA mimics at a final concentration of 50 nM, following the manufacturer\u0026rsquo;s instructions. After 24 hours of transfection, cells were collected for subsequent analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePlasmids\u003c/h3\u003e\n\u003cp\u003eThe lentiviral transfer vector PLKO.3G was a gift from Christophe Benoist and Diane Mathis (Addgene plasmid #14748; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:14748\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:14748\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The lentiviral packaging plasmids pLP1, pLP2, and pVSVG were obtained from Invitrogen (Thermo Fisher Scientific, USA). For reporter and overexpression assays, three categories of HIF1A-related plasmids were obtained from Fubio Biotechnology Co., Ltd. (Suzhou, China).\u003c/p\u003e \u003cp\u003eThe dual-luciferase reporter plasmid FC-8635 contained the wild-type HIF1A 3\u0026prime; untranslated region (3\u0026prime;UTR) cloned downstream of the firefly luciferase gene, and its corresponding empty vector control FV-149 was used for normalization. The sequence of the cloned HIF1A 3\u0026prime;UTR fragment is provided in Supplementary Table\u0026nbsp;1. For in vitro transient overexpression experiments, the HIF1A expression plasmid FC-2695 and its control FV-073 (pcDNA) were used. To generate stable overexpression cell lines, the lentiviral construct FC-3086 and its negative control FV-050 were employed. All plasmids were sequence-verified prior to use, and their detailed maps are shown in Supplementary Fig.\u0026nbsp;4.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCloning of miR-16 Constructs\u003c/h2\u003e \u003cp\u003eLentiviral constructs PLKO.3G-miR-16, PLKO.3G-miR-16-1* and the scrambled microRNA control PLKO.3G-Scr miR, all co-expressing the EGFP reporter gene, were generated for the overexpression of miR-16, miR-16-1* and Scr miR, respectively. The procedure was as follows: DNA duplexes for (1) miR-16, (2) miR-16-1* and (3) Scr miR were prepared by annealing complementary oligonucleotide pairs (see Supplementary Table\u0026nbsp;2). The resulting duplexes were individually cloned into the Acc36I and EcoRI restriction sites of the PLKO.3G backbone. The accuracy of all lentiviral constructs was verified by PCR using PLKO-Dir and PLKO-Rev primers (see Supplementary Table\u0026nbsp;2), followed by Sanger sequencing with PLKO-15' and PLKO-Rev primers (see Supplementary Table\u0026nbsp;2). The scrambled miR (Scr miR) sequence was sourced from\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLentivirus Production and Transduction\u003c/h2\u003e \u003cp\u003eLentiviruses were produced by co-transfecting the lentiviral transfer vector with the packaging plasmids (pLP1, pLP2, and pVSVG) into HEK293T cells using Lipofectamine 3000 (Thermo Fisher Scientific). The viral supernatant was harvested 72 hours post-transfection, filtered through a 0.45 \u0026micro;m filter (Millipore), and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. lentiviruses concentration was preformed following protocol \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. The virus-containing medium was harvested 48\u0026ndash;72 hours post-transfection and clarified by centrifugation at 3,000 \u0026times; g followed by filtration through a 0.45 \u0026micro;m filter. The clarified medium was layered onto a sucrose cushion buffer (50 mM Tris-HCl, pH 7.4, 100 mM NaCl, 0.5 mM EDTA) at a 4:1 ratio (v/v) and centrifuged for 4 hours at the 14000 RCF and 4\u0026deg;C. After carefully removing the supernatant, the tube was inverted on tissue paper for 3 minutes to drain. The resulting pellet was resuspended in DMEM supplemented with 1% BSA and allowed to recover overnight at 4\u0026deg;C, then stored at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003eA549 cell lines were transduced with the lentiviral particles in 6-well plates. Subsequently, EGFP-positive A549 cells were isolated using a BIO-RAD S3e cell sorter (BIO-RAD, USA). All subsequent experiments were performed using this sorted, EGFP-positive populations of resulting A549-miR-16-5p and A549-miR-16-1-3p cell sublines. The sorted A549-miR-16-5p subline was further infected with concentrated miR-16-1-3p lentivirus for the generation of A549 subline co-expressing miR-16-5p and miR-16-1-3p. Following infection, total RNA was extracted, and qRT\u0026ndash;PCR analysis was performed to confirm the successful overexpression of miR-16-1-3p. Stable overexpressing cell lines, including A549\u0026ndash;miR-16-5p, A549\u0026ndash;miR-16-1-3p, and A549\u0026ndash;miR-16-5p\u0026thinsp;+\u0026thinsp;miR-16-1-3p, were validated by RT-qPCR analysis (Supplementary Fig.\u0026nbsp;1). For the establishment of HIF1A-overexpressing cell lines, A549 cells were infected with HIF1A lentiviral particles and subsequently selected with puromycin to obtain stable populations. A negative control group was included to ensure the accuracy and efficiency of the selection process. Following antibiotic selection, HIF1A overexpression in cell lines was confirmed by Western blotting (Supplementary Fig.\u0026nbsp;3). Experimental groups included A549-miR-16-5p, A549-miR-16-1-3p, and A549-miR-16-5p\u0026thinsp;+\u0026thinsp;miR-16-1-3p overexpressing cell lines, with A549-miR-SCR serving as the control for miRNA groups; for HIF1A-overexpression assays, cells carrying the empty vector plasmid (pcDNA) served as controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and quantitative PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer\u0026rsquo;s protocol, and RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eTo quantify microRNA expression, complementary DNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Cat. No. 4368814) together with stem-loop RT primers designed for individual microRNAs. Reverse transcription was performed at 25\u0026deg;C for 30 min, 37\u0026deg;C for 120 min, and 85\u0026deg;C for 5 min. The resulting cDNA was subjected to TaqMan probe\u0026ndash;based quantitative PCR on a QuantStudio 5 Real-Time PCR System (Applied Biosystems). Each 20 \u0026micro;L reaction contained a microRNA-specific forward primer, a universal reverse primer, and the corresponding TaqMan probe (0.5 \u0026micro;M each). The thermal cycling profile was 95\u0026deg;C for 10 min, followed by 40 cycles of 95\u0026deg;C for 15 s and 60\u0026deg;C for 1 min. Expression levels were normalized to U44 small nuclear RNA, and relative quantification was calculated using the 2^\u0026ndash;ΔΔCt method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDual Firefly\u0026ndash;Renilla Luciferase Reporter Assay\u003c/h2\u003e \u003cp\u003eThe HeLa cell line was selected for the reporter assay because it does not express endogenous miR-16 \u003csup\u003e29\u003c/sup\u003e. Cells were seeded in 24-well plates and co-transfected with 300 ng of either the wild-type (HIF1A-3\u0026prime;UTR-WT, FC-8635) or control (empty pmiR-Glo, FV-149) reporter plasmid together with 200 pmol of hsa-miR-16-1-3p mimics or scrambled controls, using Lipofectamine 3000 (Thermo Fisher Scientific) following the manufacturer\u0026rsquo;s instructions. After 24 hours of incubation, luciferase activity was measured with the Dual-Lumi\u0026trade; Luciferase Reporter Gene Assay Kit (Beyotime Biotechnology, Shanghai, China) and quantified using a ClarioStar microplate reader (BMG Labtech). All experiments were performed in triplicate. Firefly luciferase activity was normalized to Renilla luciferase activity to correct for transfection efficiency and ensure data comparability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTransient Transfection\u003c/h2\u003e \u003cp\u003eTransient transfection was performed to induce HIF1A overexpression. According to the manufacturer\u0026rsquo;s protocol, plasmid DNA was mixed with P3000 reagent and Lipofectamine 3000 (Thermo Fisher Scientific) in Opti-MEM medium to prepare transfection complexes. The mixture was added to cells at 60\u0026ndash;70% confluence, and incubation was continued for 24 hours. Cells were then harvested for subsequent in vitro validation assays. Cells transfected with the empty vector plasmid (pcDNA) were used as the negative control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTrans-well invasion assay\u003c/h2\u003e \u003cp\u003eCell invasive capacity was evaluated using 24-well Transwell chambers equipped with 8-\u0026micro;m pore polycarbonate membranes (SPLInsert\u0026trade; Hanging, PET membrane, Cat. No. 37124, SPL Life Sciences). Prior to the assay, cells were serum-starved overnight to synchronize metabolic activity. On the following day, 5 \u0026times; 10⁴ cells suspended in serum-free medium were seeded into the upper chambers, while complete medium containing 10% FBS was added to the lower chambers. After 24 h of incubation at 37\u0026deg;C in a humidified 5% CO₂ atmosphere, non-invading cells on the upper membrane surface were gently removed with a cotton swab. The membranes were then fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Invaded cells were counted under a Leica DFC7000 T microscope, selecting two random fields per replicate. Each experimental condition was performed in triplicate, and quantitative data were analyzed using GraphPad Prism 10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eCells (1 \u0026times; 10⁶ per well) were seeded into 6-well plates and cultured overnight until reaching approximately 90% confluence. A straight scratch was then made across the cell monolayer using a sterile 200-\u0026micro;L pipette tip, after which the detached cells were gently washed away with pre-warmed PBS. The medium was replaced with serum-free DMEM. Images of the scratch area were captured at 0 h and 24 h using the EVOS\u0026trade; M5000 imaging system (Thermo Fisher Scientific). The wound closure was quantified using the image analysis algorithm described in this study\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Each experimental condition was performed in triplicate, and two random microscopic fields were analyzed per replicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCisplatin sensitivity assay\u003c/h2\u003e \u003cp\u003eCells (5 \u0026times; 10\u0026sup3; per well) were seeded into 96-well plates and allowed to adhere overnight. The next day, cells were treated with cisplatin at concentrations ranging from 0.25 to 80 \u0026micro;M, prepared by twofold serial dilution across ten concentration points, with vehicle-treated wells serving as controls. After 48 h of incubation, cells were fixed with 10% trichloroacetic acid (TCA) at 4\u0026deg;C for 1 h, washed with distilled water, and air-dried. Cells were then stained with 0.4% sulforhodamine B (SRB) at room temperature for 30 min, followed by rinsing with 1% acetic acid to remove unbound dye. The bound dye was solubilized in 10 mM Tris\u0026ndash;HCl (pH 10.5), and absorbance was measured at 510 nm using a microplate reader. Absorbance from control wells was defined as 100% viability, and all experimental values were normalized accordingly. IC₅₀ values were determined by nonlinear regression analysis of the dose\u0026ndash;response curves using GraphPad Prism 10. All assays were performed in triplicate to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eApoptosis assay\u003c/h2\u003e \u003cp\u003eCell apoptosis was assessed using the Annexin V\u0026ndash;FITC/PI Apoptosis Detection Kit (Seiverbios, Cat. No. G1511-100T). Based on preliminary optimization and published protocols, cisplatin was used at a final concentration of 5 \u0026micro;M, which produced consistent apoptotic responses in A549 cells \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. After 48 h of incubation, both adherent and floating cells were collected, washed twice with cold PBS, and stained with Annexin V\u0026ndash;FITC and propidium iodide (PI) according to the manufacturer\u0026rsquo;s instructions. Samples were analyzed on a flow cytometer (BD Biosciences, USA), and data were processed using FlowJo software (Tree Star, USA). All experiments were performed in triplicate to ensure statistical reliability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCAM (chick embryo chorioallantoic membrane) Bio-Imaging Assay.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFertilized specific pathogen\u0026ndash;free (SPF) chicken eggs were purchased from a certified hatchery (Trade House Ptichnoe, Ltd., Moscow, Russia; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ptichnoe-td.ru\u003c/span\u003e\u003cspan address=\"https://ptichnoe-td.ru\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Upon arrival, eggs were gently cleaned with sterile water, placed horizontally in an incubator at 37\u0026deg;C and 70% humidity, and automatically rotated to prevent chorioallantoic membrane (CAM) adhesion. This time point was recorded as embryonic day 0 (EMD 0). At EMD 3\u0026ndash;4, after visualization of the air chamber and primary blood vessels under candling, approximately 2 mL of albumen was aspirated through the blunt end using an 18 G needle to detach the CAM from the shell. A circular window (~\u0026thinsp;2 cm in diameter) was then created on the shell using a precision rotary saw, sealed with 3M semi-permeable film, and returned to the incubator with rotation stopped.\u003c/p\u003e \u003cp\u003eDuring the CAM bio-imaging experiments at EMD 8, A549-Katushka2S-T2A cells, produced following a previously established workflow\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, were suspended in a serum-free medium combined in a 1:1 ratio with Matrigel (Corning, Cat. No. 356234). These mixtures were then seeded into O-rings (inner diameter 6 mm, outer diameter 10 mm) positioned on the vascular surface of the CAM. A total of 3 \u0026times; 10⁶ cells in 20 \u0026micro;L suspension were inoculated per egg. A clinically approved anti-VEGFA monoclonal antibody, bevacizumab (Avastin\u0026reg;, 25 mg/mL; Roche, Moscow, Russia), was used for anti-angiogenic treatment in the CAM model. Tumor-bearing CAMs were treated locally at embryonic day 12 (EMD12) and embryonic day 14 (EMD14). For each treatment, 1 \u0026micro;L of bevacizumab standard solution was diluted with 1 \u0026micro;L sterile PBS and carefully applied directly into the PTFE ring containing the tumor. Control tumors received an equal volume (2 \u0026micro;L) of sterile PBS alone\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. After inoculation, the openings were resealed and incubation continued until EMD 16, when tumors were collected. Primary tumors and tumor vessels were imaged using Leica M60 stereomicroscope, tumor volume was calculated using the formula in the previously study[16]. Blood vessel density was analyzed using the ImageJ based Vessel Analysis tool developed by the National Institutes of Health (USA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imagej.net/plugins/vessel-analysis\u003c/span\u003e\u003cspan address=\"https://imagej.net/plugins/vessel-analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Metastatic dissemination was assessed by LumoTrace\u0026reg; Fluo system (Abisense LLC, Russia) and calculated using Icy software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icy.bioimageanalysis.org/\u003c/span\u003e\u003cspan address=\"https://icy.bioimageanalysis.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All experiments were completed before EMD 17, in accordance with international ethical standards exempting pre-hatching embryos from IACUC regulation and the Russian Animal Experiment and Welfare Guidelines\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence Staining\u003c/h2\u003e \u003cp\u003eFor cellular immunofluorescence, A549 cells were seeded onto sterile 8-well chamber slides (Biologix, Cat. No. B-07-2108). After incubation, cells were gently washed with PBS and fixed in 4% paraformaldehyde for 15 min at room temperature. Following fixation, samples were blocked with 5% bovine serum albumin (BSA) for 1 h and then incubated overnight at 4\u0026deg;C with anti\u0026ndash;E-cadherin antibody (1:400, CST, #14472). The next day, slides were washed and incubated with Alexa Fluor 488\u0026ndash;conjugated secondary antibody (1:500, Abcam, ab150113) for 1\u0026ndash;2 h at room temperature in the dark. Nuclei were counterstained with DAPI mounting medium (Servicebio, G1407). The cytoskeletal organization was visualized using Phalloidin\u0026ndash;TRITC conjugate (Solarbio, Cat. No. CA1620), following the manufacturer\u0026rsquo;s protocol. Images were acquired using the EVOS\u0026trade; M5000 imaging system (Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eFor tissue section immunofluorescence, CAM tumor nodules harvested on embryonic day 16 (EMD16) were fixed in 4% paraformaldehyde, followed by dehydration in 15% and 30% sucrose gradients. Samples were embedded in OCT compound, and 10 \u0026micro;m cryosections were prepared. Sections were washed with PBS; permeabilization was performed with 0.2% Triton X-100 for 10 min, except for CD105 staining, in which permeabilization was omitted to preserve membrane integrity. After blocking with 5% BSA for 1 h, sections were incubated overnight at 4\u0026deg;C with either rabbit anti\u0026ndash;HIF-1α (1:400, Abcam, ab179483) or rabbit anti\u0026ndash;CD105 (1:400, Proteintech, 10862-1-AP). The next day, slides were incubated with Alexa Fluor 647\u0026ndash;conjugated secondary antibody (1:500, Abcam, ab150079) for 1 h at room temperature in the dark and then mounted. Fluorescence images were captured using the EVOS\u0026trade; M5000 microscope, and the positive staining ratio of HIF-1α and CD105 was quantified using ImageJ software (NIH, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eCells were lysed on ice in RIPA buffer (Thermo Fisher Scientific) supplemented with a protease inhibitor cocktail (1:100 dilution). Lysates were centrifuged at 12,000 \u0026times; g for 15 min at 4\u0026deg;C, and the supernatants were collected. Protein concentrations were quantified using the BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of protein were loaded onto SDS\u0026ndash;polyacrylamide gels, separated electrophoretically, and transferred to PVDF membranes. Membranes were blocked with 5% BCA in TBST for 1 h at room temperature, then incubated overnight at 4\u0026deg;C with primary antibodies against HIF-1α (1:1000, Abcam, ab179483), CD105 (1:1000, Proteintech, 10862-1-AP), and VEGFA (1:1000, CST, #2463). After three washes with TBST, membranes were incubated for 1 h at room temperature with HRP-conjugated secondary antibody (1:3000, Affinity, S0001). Protein bands were visualized using an ECL detection kit (Bio-Rad, 1705061) and imaged with a ChemiDoc XRS+ system (Bio-Rad).\u003c/p\u003e \u003cp\u003eTo verify equal protein loading, membranes were stripped using Restore\u0026trade; Western Blot Stripping Buffer (Bio-Rad, Bulletin 6218), washed thoroughly, and re-probed with β-actin\u0026ndash;HRP (1:3000, Servicebio, ZB15001-HRP) on the same blot. Signal intensities were quantified with ImageJ software, and relative protein expression was normalized to β-actin.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultiplex analysis of tumor cell secretome.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe tumor secretome was gathered from tumor cell\u0026ndash;conditioned medium (TCM) created from stable transfectants of A549 cells grown in hypoxic conditions. A549-miR-16-5p and A549-AVA-miR-SCR cells were used as experimental groups, with A549-miR-SCR cells serving as controls. Cells were cultured to 70\u0026ndash;80% confluence, thoroughly washed to remove residual serum components, and subsequently incubated in phenol red\u0026ndash;free, serum-free medium supplemented with CoCl₂ to ensure sustained HIF1A activation. After 48 h, conditioned media were collected and filtered through 0.22 \u0026micro;m membranes to obtain cell-free TCM.\u003c/p\u003e \u003cp\u003eSecretome profiling was performed using a bead-based multiplex immunoassay targeting 41 human cytokines and chemokines (MILLIPLEX MAP, Millipore). A total of 25 \u0026micro;L TCM per sample was analyzed using Luminex technology on a QuattroPlex biomarker analysis system, following the manufacturer\u0026rsquo;s instructions. Cytokine concentrations were quantified based on standard curves generated from recombinant standards provided with the assay kit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003eRNA-seq data and clinical information of lung adenocarcinoma (LUAD) were obtained from The Cancer Genome Atlas (TCGA) database. All analyses were performed in R. Patients were divided into high- and low-HIF1A groups according to the median expression level. Survival analyses were conducted using the survival and survminer packages, and gene set enrichment analysis (GSEA) was performed with clusterProfiler based on the HALLMARK gene sets from MSigDB. Pathways with adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched. To explore gene expression under anti-VEGF therapy, the GSE37138 dataset, derived from non-small cell lung cancer patients treated with bevacizumab, was analyzed. Expression correlations between HIF1A, VEGFA, and CD105 (ENG) were visualized as boxplots using the ggplot2 package. Predicted targets of miR-16-1-3p were obtained from TargetScan, miRDB, and miRWalk, and their intersection was illustrated with a Venn diagram generated by the VennDiagram package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using GraphPad Prism version 10 (GraphPad Software, San Diego, USA). Each experiment was independently performed at least three times, and quantitative data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Differences between two groups were analyzed using an unpaired, two-tailed Student\u0026rsquo;s t-test, whereas comparisons among multiple groups were evaluated by one-way analysis of variance (ANOVA). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical significance in the figures is indicated as follows: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u0026ldquo;ns.\u0026rdquo; denotes no significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYuzhe Wang\u003c/strong\u003e: Writing – review \u0026amp; editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eWenyu Xue\u003c/strong\u003e: Writing – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eP.A. Malakhov\u003c/strong\u003e: Writing – review \u0026amp; editing, Methodology, Investigation, Data curation. \u003cstrong\u003eA.V. Smirnova\u003c/strong\u003e: Writing – review \u0026amp; editing, Validation, Methodology, Investigation, Data curation. \u003cstrong\u003eMargarita Pustovalova\u003c/strong\u003e: Writing – review \u0026amp; editing, Visualization, Validation, Supervision, Conceptualization. \u003cstrong\u003eDenis V Kuzmin\u003c/strong\u003e: Writing – review \u0026amp; editing, Visualization, Supervision, Resources, Project administration, Funding acquisition, Data curation, Conceptualization. \u003cstrong\u003eSergey Leonov\u003c/strong\u003e: Writing – review \u0026amp; editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Investigation, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eThe bio-imaging data that support the findings of this study are available from Abisense LLC but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Abisense LLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Russian Science Foundation (project No. 23-14-00220).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following is a link to the electronic supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHerbst, R. S., Morgensztern, D. \u0026amp; Boshoff, C. 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Although bevacizumab effectively neutralizes VEGFA and suppresses neovascularization, its clinical benefit is often transient. Here, we demonstrate that VEGFA inhibition paradoxically intensifies intratumoral hypoxia, leading to stabilization of HIF1A and activation of a compensatory pro-malignant program. Increased HIF1A not only enhances tumor cell migration, epithelial\u0026ndash;mesenchymal transition, and chemoresistance, but also upregulates CD105 (Endoglin), a hypoxia-responsive pro-angiogenic mediator that attenuates the anti-vascular effects of bevacizumab. Thus, anti-VEGFA therapy initiates a hypoxia-driven HIF1A\u0026ndash;CD105 axis that sustains tumor aggressiveness and vascular adaptation despite VEGFA blockade.\u003c/p\u003e \u003cp\u003eWe identify the pre\u0026ndash;miR-16-1 duplex as a physiological dual-regulatory system capable of simultaneously targeting this adaptive circuit. The guide strand miR-16-5p directly represses VEGFA, recapitulating the anti-angiogenic action of bevacizumab. In contrast, the passenger strand miR-16-1-3p suppresses HIF1A expression, thereby preventing hypoxia-induced malignant phenotypes and limiting CD105 upregulation. Functional analyses revealed that VEGFA inhibition alone promotes hypoxia-associated migration, cytoskeletal remodeling, and cisplatin resistance, whereas co-modulation of miR-16-1-3p abrogates these effects. In a chick chorioallantoic membrane xenograft model, dual regulation of VEGFA and HIF1A markedly reduced vascular density, tumor growth, and metastatic dissemination compared with single anti-angiogenic intervention.\u003c/p\u003e \u003cp\u003eCollectively, our findings uncover a hypoxia-mediated resistance mechanism driven by the HIF1A\u0026ndash;CD105 axis following VEGFA inhibition and establish the cooperative function of the miR-16 duplex as a strategy to concurrently suppress angiogenesis and its adaptive hypoxic feedback. Targeting both VEGFA and HIF1A may therefore improve the durability of anti-angiogenic therapy in NSCLC.\u003c/p\u003e","manuscriptTitle":"Harnessing the Cooperative Function of Duplex From pre-miR-16 Hairpin to Simultaneously Inhibit VEGF and Hypoxia Pathways in Human Non-Small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 15:50:00","doi":"10.21203/rs.3.rs-8912330/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-07T07:08:34+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-19T08:16:04+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-04-15T08:06:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T10:06:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T21:04:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cell Death Discovery","date":"2026-02-18T21:04:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-discovery","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddiscovery","sideBox":"Learn more about [Cell Death Discovery](http://www.nature.com/cddiscovery/)","snPcode":"41420","submissionUrl":"https://mts-cddiscovery.nature.com/","title":"Cell Death Discovery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abaed3bc-c870-46eb-b461-436ead9af8c5","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-07T07:08:34+00:00","index":2,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65606338,"name":"Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer"},{"id":65606339,"name":"Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance"},{"id":65606340,"name":"Biological sciences/Molecular biology/Non-coding RNAs/miRNAs"}],"tags":[],"updatedAt":"2026-04-22T15:50:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 15:50:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8912330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8912330","identity":"rs-8912330","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}