Little is known about endogenous inhibitors of angiogenic growth factors. In this study, we identified a novel endogenous anti-angiogenic factor expressed in pericytes and clarified its underlying mechanism and clinical significance.Herein, we found Kai1 knockout mice showed significantly enhanced angiogenesis. Then, we investigated the anti-angiogenic roll of Kai1 in vitro and in vivo.KAI1 was mainly expressed in pericytes rather than in endothelial cells. It localized at the membrane surface after palmitoylation by zDHHC4 enzyme and induced LIF through the Src/p53 pathway. LIF released from pericytes in turn suppressed angiogenic factors in endothelial cells as well as in pericytes themselves, leading to inhibition of angiogenesis. Interestingly, KAI1 had another mechanism to inhibit angiogenesis: It directly bound to VEGF and PDGF and inhibited activation of their receptors. In the two different in vivo cancer models, KAI1 supplementation significantly inhibited tumor angiogenesis and growth. A peptide derived from the large extracellular loop of KAI1 has been shown to have anti-angiogenic effects to block the progression of breast cancer and retinal neovascularization in vivo.KAI1 from PC is a novel molecular regulator that counterbalances the effect of angiogenic factors.The online version contains supplementary material available at 10.1186/s13045-021-01147-6. Angiogenesis is a process of new blood vessel formation from the preexisting vasculature. This process is mainly controlled by the interactions between two vascular cell types, endothelial cells (ECs), and pericytes (PCs) . AlthougAngiogenesis is strictly controlled by maintaining a proper balance between pro- and anti-angiogenic factors . Most anAdult C57BL/6 mice  were purchased from The Jackson Laboratory . All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) at Seoul National University Hospital and complied with the National Research Council (NRC) ‘Guidelines for the Care and Use of Laboratory Animals’.Kai1/Cd82-EmGFP (Kai1-GFP) mice were generated by Macrogen, Inc., using the CRISPR system. The mice were interbred and maintained under pathogen-free conditions. Protocols were reviewed and approved by the Institutional Animal Care and Use Committees (IACUC). C57BL/6N female mice were treated with pregnant mare serum gonadotropin and human chorionic gonadotropin. After 48 h, the female mice were mated with C57BL/6N stud male mice. On the next day, the vaginal plug was checked in female mice, and the mice were killed to harvest the fertilized embryos. Single-guide RNA , Cas9 nuclease, and dsDonor were mixed and microinjected into one cell embryo. Microinjected embryos were incubated at 37 °C for 1–2 h. Fourteen to sixteen injected one cell staged embryo were transplanted into the oviducts of pseudopregnant recipient mice (ICR). After the founders were born, genotyping of tail samples was performed by PCR . PCR-positive samples were subjected to TA cloning and analyzed by sequencing.l-lysine-coated dishes (15ul diluted in 10 mL of distilled water) and maintained in Pericyte Medium .MS1  and B16 (mouse melanoma cell line) cells were cultured in DMEM high glucose (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum  and 1× antibiotics-antimycotics (Thermo Fisher Scientific). 10T1/2 was maintained in RPMI 1640 HEPES (Thermo Fisher Scientific) supplemented with 10% FBS (Thermo Fisher Scientific) and 1× antibiotics-antimycotics (Thermo Fisher Scientific). Mouse primary perivascular cells (PVCs) and mouse primary aorta endothelial cells (ECs) were harvested and expanded in DMEM high glucose supplemented with 20% FBS. Human umbilical vein endothelial cells  were obtained from Lonza  and cultured in EGM-2MV (Lonza). HUVEC were cultured in 1.5% gelatin-coated dishes. Human brain vessel pericyte  was grown on poly-Eyeballs including the optic nerve were collected from the mice at postnatal day 2 and 5. The retinae were stained with Lectin from Bandeiraea simplicifolia fluorescein isothiocyanate (FITC) conjugate , anti-CD31 phycoerythrin (PE) conjugate , anti-NG2 Alexa 488 conjugate , anti-NG2 cy3 conjugate , anti-collagen IV antibody , and anti-GFP . MS1 and 10T1/2 cells were fixed in 2% cold paraformaldehyde (Wako) for 10 min on ice. After washing with phosphate-buffered saline (PBS), the samples were blocked with 1% bovine serum albumin and 0.1% Triton X-100, followed by staining with anti-KAI1 , anti-CD9 , anti-alpha-smooth muscle actin Cy3 conjugate , anti-CD31 PE conjugate (BD Biosciences), and Bandeiraea simplicifolia fluorescein isothiocyanate conjugate (Sigma-Aldrich). After staining, the tissue and cells were examined by confocal microscopy (Zeiss).Total RNAs were separated and purified and cell harvesting at representative time points. RNeasy® mini kit  and QIAshredder  were used to separate and purify RNA from cells. To synthesis the cDNA from RNA, we used qPCR RT master mix from Toyobo . Real-time PCR was performed with SyBR Green I Mastermix (Applied Biosystems) using an ABI PRISM TM 7500 Sequence Detection System (Applied Biosystems). RT-PCR was performed using the S1000 Thermal Cycler (BIO-RAD) and detected the agarose gel with Gel Doc XR + Gel Documentation System (BIO-RAD). Information on the primers used is provided in the Supplemental Materials.416), and pSrc (Tyr527) , Myc , p53 , Pbx1 , Cav-1 , Flot1 , p-VEGFR2 , total VEGFR2 , p-PDGFRβ , total PDGFRβ , PDGF-BB , and VEGF-A  followed by incubation in horseradish peroxidase-conjugated secondary antibodies (Jackson Laboratory). GAPDH  and beta-actin  were used as an internal control.Cells were lysed with lysis buffer  containing protease inhibitor cocktail . Total protein was immunoblotted with primary antibodies against KAI1 , LIF , tSrc, pSrc (TyrCells were collected in FACS buffer [dPBS (Thermo Fisher Scientific) containing 1% FBS and 0.1% bovine serum albumin (Amresco)]. Cells were stained with antibodies, anti-CD82 (Miltenyi Biotec.), CD31 (Santa Cruz), and PDGFRβ (R&D Systems) for 30 min on ice washed out with FACS buffer after staining and analyzed with a BD FACSCanto II™ (BD Biosciences), followed by sorting using a BD FACSAria™ (BD Biosciences) instrument.Adenovirus carrying mouse KAI1 was created with the pAdEasy vector system and titrated using the plaque-forming unit assay. 10T1/2 and MS-1 cells were transduced at a multiplicity of infection of 500 and 1000 pfu (plaque-forming unit), respectively, with 1 μg/mL polybrene (Sigma-Aldrich). For lentivirus transduction, MS1 cells were transduced by GFP encoded lentivirus using polybrene.4 MS1 (EC) and 1 × 104 WT/Kai1 K/O mouse primary PVCs were seeded onto the polymerized GFR-matrigel with EBM 5% media. For the EC-conditioned medium set, 3 × 104 MS1 cells were seeded onto a matrigel coated µ-Dish (Ibidi) with conditioned media of primary PVCs from WT or Kai1 K/O mouse. For the three-dimensional spheroid sprouting assay, spheroids of PVCs, ECs, and B16 cells combinations were generated using the hanging drop method. Spheroids seeded onto polymerized matrigel. For the tube formation assay using an overconfluent PVC monolayer, a high density of 10T1/2 cells was incubated for 5 days in growth media and GFP expressing-ECs (MS1) were co-cultured with the PVC monolayer for 3 days.For the Matrigel two-dimensional tube formation assay, confocal dishes (Ibidi) were coated with 80 μL matrigel (Corning) and incubated for 30 min in a 37 °C incubator for polymerization. For EC-pericyte co-culture experiments, 3 × 10Tube formation images were acquired by fluorescence microscopy and using a Zeiss LSM-710 META confocal microscope. Random fields were measured in terms of tube length and number of branching points by ImageJ software .Lipid raft isolation in mouse PCs (10T1/2) and ECs (MS-1) was performed. For lipid-protein crosslinking, the cells were treated with 1.25 mM DTSSP  solution and incubated for 1 h on ice at 4 °C. The cells were washed with cold PBS and 5 mM EDTA and then 0.75 mL cold PBS and 5 mM EDTA were added. The samples were frozen overnight at − 80 °C. The cells were lysed with 0.8 mL 0.1% Triton X-100 membrane raft isolation buffer  by passing through a 23-gauge needle on a 5-mL syringe 20 times. Next, 500 μL supernatant 1 mL OptiPrep  separation medium (60% iodixanol) were added, resulting in a 40% iodixanol solution of lysed cells. Using a Pasteur pipet, the 40% iodixanol solution was carefully overlaid with equal amounts of 30% iodixanol solution, followed by 5% iodixanol solution. The gradient was visible to the naked eye. Samples were ultra-centrifuged for 5 h at 132,000 g and 4 °C. The membrane rafts were present in the second fraction and were visible. Western blotting was performed as described above.Leukemia inhibitory factor (LIF) concentrations in the conditioned media of adenovirus-transduced MS1 and 10T1/2 cells were determined using a mouse LIF Quantikine ELISA Kit (R&D Systems). 1:2 diluted standard, control, and samples were incubated for 2 h at room temperature followed by a total of five washes. Next, 100 μL of mouse LIF Conjugate was added and incubated for 2 h at room temperature, followed by five washes, and then 100 μL of substrate solution was added and incubated for 30 min at room temperature in the dark. Finally, 100 μL of stop solution was added and the optical density of each sample was determined using a microplate reader (Promega). The concentration was calculated by four-parameter logistic (4-PL) regression.N-ethylmaleimide, 1 μM PMSF (Sigma-Aldrich), 200X PIC (Biovision), 50 mM Tris–HCl pH 7.4, and 150 mM NaCl. Additionally, DHHC3 and DHHC4 knockdown mPVCs (101/2) by short interfering RNA (Santa Cruz) were harvested in PLB buffer. Lysates were briefly sonicated and incubated at 4 °C for 24 h, followed by centrifugation at 13,300 rpm 30 min at 4 °C. The supernatant was subject to chloroform–methanol protein precipitation (4:1:3 = methanol:chloroform:water). The protein pellet was solubilized in 0.5% SDS buffer and briefly sonicated. Concentrated proteins were treated with 0.5 M hydroxylamine  or Tris–HCl pH 7.4 buffer (negative control) for 2 h at room temperature. Next, the proteins were precipitated by the chloroform–methanol precipitation method, solubilized in 0.5% SDS, and then briefly sonicated. Solubilized proteins were diluted with 1/10 dilution to 0.05% SDS and immunoprecipitated with 40 μL neutravidin (Invitrogen) for 2 h at 4 °C. Immunoprecipitated beads were washed three times with PLB and eluted in 2× DTT-containing SDS-PAGE sample buffer (3 M). Samples obtained through the ABE assay were performed through the western blotting described above.mPVCs (101/2) and mECs (MS-1) were harvested by using palmitoylation lysis buffer (PLB) containing 1% NP-40, 10% glycerol, 50 mM Kai1 K/O mice. RNA sequencing reads were aligned to the mouse genome build mm9 (NCBI37) using TopHat 2.0.9 and Bowtie 0.12.9 with a segment-length of 21, which allowed 2 mismatches per read. Expression levels for 23,170 RefSeq genes were measured by reads per kilobase per million mapped reads (RPKM). Differentially expressed genes between conditions were tested by Cuffdiff. Differentially expressed genes were defined as having more than a twofold change and an adjusted p value less than 0.05. Hierarchical clustering of samples was performed by R (www.R-project.org). Gene Ontology (GO) analysis was performed (http://geneontology.org/). Gene Set Enrichment Analysis (GSEA) was performed against custom-made lists from the GO database. The RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) database (GSE114465).RNA was extracted from pericytes of WT and http://www.sabiosciences.com/chipqpcrsearch.php?app=TFBS&qs=1490097319). 10T1/2 cells were transfected with FLAG-tagged mouse KAI1 plasmid  that had been incubated with polyethylenimine (Sigma-Aldrich) to improve transfection efficiency. For Src inhibition, 5 µM of PP2  was added to the normal growth medium. The cells were fixed in 1% formaldehyde  for 10 min at room temperature for crosslinking and collected in a tube. The supernatant was discarded, and ChIP RIPA buffer  was added to the pellet. To shear the DNA, both fractions were sonicated 10 times with a BIORUPTOR . The immunoprecipitated sample was supplemented with a p53  and pbx1  antibody (2 µg) and rotated overnight. Protein A/G sepharose  were added to pulldown the antibodies, and the samples were washed sequentially three times with homemade washing buffers 1, 2, and 3 . Samples were incubated overnight at 65 °C for decrosslinking and recovered using a PCR purification kit (Qiagen). Recovered DNA was analyzed by semi-quantitative PCR.Putative p53 and pbx1 binding sites were predicted using the EpiTect ChIP qPCR Primers website  and renilla luciferase reporter plasmid pRL-TK  by using Neon transfection system (Thermo Fisher Scientific). Lif promoter binding affinity-luciferase activity was detected using Dual-Glo Luciferase Assay system (Promega), following the protocol provided by the manufacturer.The surface plasmon resonance (SPR) binding studies were performed by Woojung BSC Inc.  using a Biacore T200  instrument optical biosensor and CM5 chip . The data were analyzed by using BIAevaluation software version 3.0 . Real-time data showing the binding of recombinant KAI proteins to angiogenic cytokines was obtained using a Blitz instrument (ForteBio).Kai1-Gfp fusion vector and Dhhc-Ha vectors were co-transfected into 293T cells using ViaFect (Promega) according to the manufacturer’s instructions. pEF-Bos-zDHHC-HA constructs were a generous gift from Professor Masaki Fukata . 293T cells were harvested in IP buffer  to confirm the interaction between Kai1-VEGF-A and Kai1-PDGF-BB, respectively, and incubated for 10 min at 4 °C. Following incubation, centrifuge for 10 min at 4 °C and lysates transfer to a new tube. Lysates were incubated for overnight at 4 °C with anti-VEGF-A (Merck Millipore)  antibody or normal rabbit IgG . After incubation with the antibody, the lysates were incubated with protein A/G sepharose (Abcam) for 2 h at 4 °C. After washing the beads were resuspended in 2× reducing sample buffer and heated for 5 min at 95 °C to dissociate captured antigen from beads. Samples obtained through the immunoprecipitation were performed through the western blotting described above.10T1/2 cells were used for Kai1 and PDGF-BB, Kai1 and VEGF-A binding assay . Cells were seeded on confocal dishes (Ibidi) and incubated for 24 h. After cells were attached, we treated 2-bp (5 μM) for 24 h. Then, cells were fixed 2% cold paraformaldehyde (Wako) for 10 min on ice and incubated with primary antibodies, Kai1 , PDGF-BB , and VEGF-A , as for IF. Duolink™  experiments were performed using the protocol provided by Sigma-Aldrich. After PLA, the cells were examined by confocal microscopy (Zeiss).6 PC-3 (human prostate cancer cell line), B16 (mouse melanoma cancer cell line) cells and MDA-MB231 cells were mixed with rhKAI1 protein  4 μg or conditioned media of vehicle or Kai1 O/E 10T1/2 cell line or KAI1 peptide , and subsequently embedded in 100 μL of matrigel . NSG and C57BL/6 mice were subcutaneously injected with 100 μL of matrigel-cell mixture in the dorsal area. After 14 days, mice were killed in order to harvest tumor tissues. The tissues were fixed with 4% paraformaldehyde (Wako), paraffin-embedded and mounted on slide glasses. The samples were examined by immunofluorescence analysis.For efficacy evaluation of rhKAI1 and KAI1 overexpressing cells, 2 × 102) from postnatal day 7 to 12 and returned to normal room air. At postnatal day 14, we intravitreally injected 1 μL of PBS, KAI WT, rhKAI1 and KAI Mut into the right eyes of mice (n = 4). At P17, the enucleated eyes were prepared for immunofluorescent staining of whole-mount retinas with Alexa Fluor® 594 isolectin GS-IB4 conjugate . The whole-mount retinas were viewed with a fluorescence microscope . Then, the neovascular tufts were marked and calculated using Image J (NIH). The area of neovascular tufts was normalized to the area of whole retina.Newborn mice were exposed to hyperoxia . The resulting Kai1 knockout (Kai1−/−) mice exhibited the accelerated retinal vascularization, as observed in the immunofluorescence (IF) images of the retinal vascular bed compared to in wild-type (WT) mice  rather than in ECs (NG2−IB4+) of the Kai1-GFP mouse retinae  were expressed predominantly in PCs  and found that the co-culture matrigel tube formation assay was significantly enhanced, compared to co-culture of WT mice primary PCs and MS1 cells  than on mouse ECs (MS1) . We found that Kai1−/− PCs expressed significantly lower level of “negative angiogenic regulators” (GO: 0016525) and “blood vessel remodelling genes” (GO: 0001974) compared to control PCs did , 525, SulfO/E Fig. c, and foECs Fig. d. The LiECs Fig. e. And G0The baseline expression level of Lif was also high in PCs compared to in ECs in mouse and human cells. However, the expression level of its receptor LIFR was comparable between PCs and ECs in both species, mouse and human , whereas inactive form of Src was increased (phosphorylation at tyrosine 527 of Src) compared to in WT PCs. In Kai1 O/E PCs, the opposite results were obtained  [Lif gene was confirmed using prediction program. Interestingly, we found that Kai1 induced binding of p53 at + 605 position of the Lif intron, which was reversed by the Src inhibitor (PP2)  . We idenPreviously, anti-KAI1 antibody was shown to inhibit VEGF signalling by regulating the KAI1 distribution in the lipid rafts . We predTaken together, KAI1 exerts anti-angiogenic effects by two mechanisms. First, KAI1 induces LIF through the Src/p53 axis, which downregulates angiogenic genes. Second, KAI1 directly binds to VEGF-A and PDGF-BB, leading to inhibition of VEGF/PDGF signalling.+) rather than in ECs (VE-cadherin+) of the normal tissues , ECs; MS1, and PCs; 10T1/2). Spheres treated with rhKAI1 showed a reduced sprouting activity compared to cells treated with vehicle . Before transplant cancer cells into the mouse, we determined the Lif mediates Kai1 function in this system. First, we measured the secreted Lif level in the media of three-dimensional hybrid spheres. We detected more secreted Lif in Kai1 overexpressed group. In contrast, the Vegfa level was decreased in Kai1 overexpressed group  with and without KAI1 supplementation  of KAI1 has been demonstrated to be essential for at least some of the biological activities of tetraspanins . In thisLif through the Src/p53 axis, and leads to downregulation of angiogenic genes in PCs  and in ECs . Finally, KAI1 inhibits angiogenesis. Another anti-angiogenic mechanism of KAI1 is its binding to VEGF-A and PDGF-BB, which leads to inhibition of phosphorylation or activation of VEGFR2 or PDGFRβ. Finally, we demonstrated the therapeutic potential of KAI1 using an in vivo diseases model by showing that angiogenesis can be suppressed by treatment with recombinant or peptide of KAI1.We demonstrated that the KAI1/CD82 is a key molecule to controlling angiogenesis and switching angiogenic milieu to a quiescent state. When a quiescent situation is required and induced, KAI1 is palmitoylated by zDHHC4 and localizes to the lipid raft membrane of PCs, induces transcription of Previous studies of the function of KAI1 mostly focused on activation of T cells and inhibition of metastasis and migration through suppression of Rac1/RhoA activity in the cancer cells , 26. Rec. who demonstrated that a lack of Kai1 in endothelial cells promotes angiogenesis in a pathological state. However, they did not identify the vascular cells that predominantly express KAI1. Based on this research, we investigated the main cells expressing KAI1 and the underlying mechanism of how KAI1 inhibits angiogenesis in the complex network between vascular cells. We generated Kai1-GFP transgenic mice and found that PCs, but not ECs, are the main cells expressing KAI1 among vascular cells. We demonstrated that ECs express KAI1 very weakly and that Kai1 overexpression in ECs did not significantly inhibit angiogenesis, whereas KAI1 in PCs efficiently inhibited angiogenesis. We next determined the underlying mechanisms of how KAI1 on PCs control ECs to suppress angiogenesis.During our investigation of how KAI1 to regulates angiogenesis, a paper  was publKai1-GFP mice model, we confirmed that KAI1 is expressed not only in PCs, but also in leukocytes and erythrocytes. Previous studies also reported KAI1 expression in T cells, B cells, M1-like macrophages, and erythrocytes [In the hrocytes , 25, 28.The most important difference in the intracellular distribution of KAI1 was that KAI1 was observed in the membrane layer of PCs but not in ECs, possibly because of palmitoylation of KAI1. Palmitoylation of KAI1 is observed mainly in the PCs but not in ECs. After screening of 23 enzymes in the zDHHC family, zDHHC4 was found to be the palmitoyltransferase responsible for the palmitoylation of KAI1. The recently discovered zDHHC4 molecule has not been widely studied , and itsKai1−/− PCs, performed GSEA, and found LIF is the downstream effector of KAI1. We demonstrated that both EC and PC express LIF receptors, suggesting that an autocrine and paracrine network of KAI1/LIF and angiogenic factors functions between these cells.To identify the distal effector molecule of KAI1 in PCs, we analyzed the transcriptome of WT and Kai1 is thought to activate SRC through the same mechanism by binding of KAI1 proteins to each other or by enhancing interactions with other tetraspanin molecules with high expression in PCs.Tetraspanin is commonly considered as a molecular facilitator that induces signalling by assisting other molecules without directly signalling itself, as it does not possess a signalling motif . RecentlLif through SRC signalling activated by KAI1, whereas Pbx1 binds to the promoter region of Lif independently of SRC signalling. Binding of Pbx1 to the Lif promoter may be facilitated via SRC-independent signalling by KAI1 or other signalling molecules of the tetraspanin-enriched web activated by KAI1. Moreover, the pbx1 binding site mutation did not affect activation of the Lif promoter. Here, we demonstrated the potential anti-angiogenic functions of p53 such as increasing LIF in response to KAI1. This new role for p53 in vascular cells and cancer cells requires further analysis.We found that the transcription factor p53 enhances LIF expression in response to KAI1 stimulation. Interestingly, p53 binds to the gene-regulating element of Tetraspanins are distributed in a broad tissues and enriched in the membrane of exosome . ExosomeAng2, Esm1, Dll4, Vefgr2 and Sox17 in EC and Vegf, Fgf2, Pdgfbb, Dll4, Vegfr2, and Ang2 in PC). Notably, key regulators of angiogenesis in ECs were inhibited, such as Sox17 which enhances VEGF signalling in a positive feedback loop and increases the tip cell-related genes essential for angiogenesis [Sox17 expression in ECs by the KAI1-LIF axis in PCs may have potent anti-angiogenic effects in ECs by blocking VEGF signalling through multiple aspects in the angiogenic microenvironment.Downstream of LIF, our study revealed that LIF from PCs or recombinant LIF inhibited the expression of various angiogenic factors in the neighboring cells  through Src/P53 pathway in pericytes. LIF from pericytes turns down several angiogenic genes in ECs (Sox17 and others) as well as in pericytes themselves under paracrine and autocrine way. Intriguingly, Kai1 had another anti-angiogenic mechanism that it directly bound to VEGF or PDGF and inhibited their signalling. Clinical applicability was well confirmed by two different cancer-angiogenesis in vivo models, where Kai1 supplement significantly suppressed tumor angiogenesis and growth.We found that KAI1 in pericytes as the new endogenous counter-regulator of pro-angiogenic factors such as VEGF and PDGF.Detailed mechanism how Kai1 in pericytes inhibits angiogenesis, would be the important new knowledge that can be basis for the future drug development to control angiogenesis.The finding that KAI1/LIF from pericytes inhibits the expression of angiogenic genes in ECs leading inhibition of angiogenesis, clearly demonstrates the paracrine mechanism of angiogenesis and the importance of pericytes.Evidence of direct binding between KAI1 to VEGF and PDGF may open the new research field of biology around angiogenic growth factors as well as development of multi-target inhibitor against angiogenic growth factors.The findings from cancer-angiogenesis in vivo models suggest that the peptide derived from KAI1 may provide the promising future platform of new drug development for cancer or other vascular proliferative disease.Finally, a 20-mer peptide derived from the large extracellular loop (LEL) of KAI1 has been shown to have anti-angiogenic effects to block retinal neovascularization and the progression of breast cancer in vivo.Additional file 1: Figure EV1. KAI1 was expressed in PC more than EC and plays a role in the anti-angiogenic effect in PC. (A) Left, vessel regression  in the WT and Kai1−/− mice. Arrowheads, empty sleeves. Scale bar, 50 μm. Right, quantification of the obtained results . (B) Confocal imaging in retinal of WT mice using Kai1 (white) antibody. Scale bar, 20 μm. (C) Confocal imaging of Kai1-GFP expression in brain and heart of the Kai1-GFP fusion transgenic mice. Scale bar, 5 μm. (D) FACS plot showing primary ECs (CD45−/CD31+) and primary PCs (CD45−/Pdgfrβ+) in the bone marrow of Kai1-GFP fusion transgenic mice. Left, KAI1 surface expression of human cell lines, HUVECs and HBVP, analyzed with FACS. Right, quantification of the obtained results. MFI . (F) CD9 expression as an example in MS1 and 10T1/2 cells. Scale bar, 50 μm. (G) Expression of tetraspanins  in ECs (MS1) and PCs (10T1/2) by using quantitative RT-PCR. (H) In vitro matrigel tube formation assay, performed using co-culture spheroids, consisting of ECs (MS1) and PCs (10T1/2), transduced with the indicated adenovirus at 0, 14, and 107 h, and observed using time-lapse microscopy. Scale bar, 50 μm. Figure EV2. KAI1 localizes at the lipid rafts of PCs and binds with zDHHC3 and zDHHC4. (A) Lipid rafts isolated from ECs (MS1) and PCs (10T1/2) using OptiPrep medium (frozen and crosslinks method). Endogenous Kai1 localization is confirmed. Flotillin-1 (Flot-1) and caveolin-1 (Cav-1) was detected lipid raft membrane fraction of MS1 and 10T1/2, respectively. Fraction 1; no cellular protein, Fraction 2; Lipid raft proteins. Fraction 3; Non-raft membrane proteins. Fraction 4 and 5; Cytosolic proteins. (B) Semi-quantitative RT-PCR analysis for zDHHC family member expression in MS1 and 10T1/2 cells. The genes were normalized by Gapdh . (C) zDHHC3 and zDHHC4 binding to KAI1, confirmed by co-immunoprecipitation. Figure EV3. Identification of the downstream effect molecule induced by KAI1. (A) Semi-quantitative RT-PCR analysis for mRNA levels of top 17 genes in “negative angiogenic regulators” in WT and Kai1−/− primary PCs. The genes were normalized by Gapdh and the mRNA expression value are as fold change relative to WT . (B) Mouse EC cell line, MS1, cell cycle analyzed with FACS . (C) LIF, LIFR, and GP130 expression in mouse and human ECs and PCs by using semi-quantitative RT-PCR. Figure EV4. KAI1 induces LIF through Src/p53. (A) Lif and Kai1 mRNA expression in 10T1/2 cells pre-treated with inhibitor of p38, Erk, Pkc, or Src and then transfected with Kai1-O/E using Flag-Kai1 vector. (B) The effects of Kai1 overexpression sup. and PP2 (Src inhibitor) on angiogenesis using tube formation assay . (C) The effects of Kai1 overexpression sup. and PP2 (Src inhibitor) on angiogenesis using migration assay . (D) Protein level of p53 and Pbx1 by Kai1 O/E in 10T1/2 cells. The quantification of the obtained results . Figure EV5. KAI1 inhibits VEGFR2 and PDGFRβ signaling through direct binding to VEGF-A and PDGF-BB. (A) Protein-protein interactions between rhKAI1 and rhVEGF-R2 observed using surface plasmon resonance (BLItz system), which suggests no binding between KAI1 and VEGF-R2. (B) The effects of KAI1 on angiogenesis using tube formation. (C) The effects of KAI1 on angiogenesis using migration. rhVEGF-A (40 ng/ml), rhKAI1 (800 ng/ml) were used. ns; not significant, *p < 0.01, **p < 0.05, ***p < 0.001. Figure EV6. The expression of KAI1 proteins in human prostate cancer and melanomas.  Representative immunohistochemistry staining for KAI1/CD82 protein expression in normal/malignant prostate cancer tissue and normal skin/melanomas. Scale bar, 50 μm.  Representative fluorescence images of the immunostained normal prostate and skin.  Representative fluorescence images of the immunostained malignant prostate cancer tissue and melanomas. Scale bar, 8 μm. DIC . (G) Representative immunohistochemistry staining for KAI1/CD82 protein expression in normal/tumor pancreatic tissue. Scale bar, 100 μm. Figure EV7. Identification of the role of B16 proliferation by Kai1. WST1 assay measuring B16 cell proliferation rate after Kai1 overexpression .Figure EV8. The anti-angiogenic effect through Lif induction in vivo by Kai1. (A) Immunofluorescence image after transducing the Kai1-Flag vector into the mouse TA muscle and detected co-localized Kai1 and Lif. (B) Quantification of the obtained results . **p < 0.05, Scale bar, 100 μm. Figure EV9. Therapeutic potential of KAI1 and characteristics of KAI1 peptide. (A) Venn diagram showing the design of peptide which interact with VEGF and PDGF, we screened the consensus amino acid sequences of VEGF- and PDGF-receptor but exclusive with FGF-receptor sequence in KAI1 large extracellular loop. (B) Schematic figure showing the KAI 1 peptide and characteristics. Figure EV10. Wild-type peptide of KAI1 interact with VEGF and PDGF. Mutant peptide does not interact with them. (A) Peptide-protein interactions between KAI1 peptide and VEGF observed using surface plasmon resonance (BLItz system), which suggests binding between KAI1 WT peptide and VEGF but no binding between mutant peptide and VEGF. (B) Peptide-protein interactions between KAI1 peptide and PDGF observed using BLItz system, which suggests binding between KAI1 WT peptide and PDGF but no binding between mutant peptide and PDGF. Figure EV11. Comparative analysis of exosome miRNAs in the aorta of WT and Kai1 knockout mice. Left, Comparison of expression pattern of miR-181a in exosomes of WT and Kai1 knockout mice. Right, Comparison of the expression pattern of miR-107 in exosomes of WT and Kai1 knockout mice. *p < 0.01. Expanded View movie 1. KAI1 in PCs has anti-angiogenic effects. In vitro matrigel tube formation assay, performed using co-culture spheroids, consisting of ECs (MS1) and PCs (10T1/2), transduced with the indicated adenovirus at 0, 14, and 107 h, and observed using time-lapse microscopy. (A) Mock virus transduced MS1 and 10T1/2 co-culture spheroids. (B) Kai1-O/E MS1 and 10T1/2 co-culture spheroids. Video sprouting from the spheroid. Scale bar, 50 μm.


Maize has played a key role in the sustenance and cultural traditions of the inhabitants of the southwestern USA for many centuries. Blue maize is an important component of the diverse landraces still cultivated in the region but the degree to which they are related is unknown. This research was designed to ascertain the genotypic, morphological, and phenotypic diversity of six representative southwestern blue maize landraces. Their genotypic diversity was examined using tunable genotyping-by-sequencing (tGBS™). A total of 81,038 high quality SNPs were identified and obtained through tGBS. A total of 45 morphological and biochemical traits were evaluated at two locations in New Mexico. The varieties Los Lunas High and Flor del Rio were genetically less related with other southwestern landraces whereas diffusion between Navajo Blue, Hopi Blue, Yoeme Blue, and Taos Blue demonstrated that these landraces were genetically related. Phenotypic variability was highest for kernel traits and least for plant traits. Plant, ear, and kernel traits were fairly consistent within and across locations. Principal component analysis and tGBS showed that Corn Belt variety ‘Ohio Blue’ was distinctly different from southwestern landraces. Genotypic analysis displayed that southwestern landraces are genetically closely related, but selection has resulted in differing phenotypes. This study has provided additional insight into the genetic relatedness of southwestern blue maize landraces. The development of modern cultivars and farming systems narrows the germplasm base and heightens crop genetic vulnerability ,2. Less Historically, the richness of in situ crop genetic diversity has been protected within “cultural landscapes”. In Mexico, the birthplace of maize , HernandThe southwestern region of the USA (the Southwest) is such a relatively unknown hotspot for traditional cultivation of diverse maize landraces. Archeological evidence confirms that native communities of the Southwest have practiced maize farming for more than 4100 years ,7,8; makIncreasingly dynamic movement, exchange, and interaction of diverse crop germplasm likely occurred with the arrival of new farming cultures in the Southwest during the Spanish Colonial and U.S. Territorial eras. The increasing movement of peoples and seed made possible by improvements in transportation likely facilitated a higher frequency of genetic exchange between races resulting in the occurrence of racial admixtures. Anderson and Cutler  noted thSturtevant  noted thToday, blue maize is highly valued by southwestern Hispanic and Native American communities, especially in northern New Mexico. It also appears with lower frequency in other parts of the Southwest, e.g., in the homelands of the Yoeme . It is not represented among the predominant landrace (Pima-Papago) of the Pima and Tohono O-odham tribes of Arizona. Of the two major southwestern races, Pima-Papago is considered to be relatively uniform phenotypically, whereas Pueblo maize may display traits that have resulted from recent influxes of races such as Southeastern and Southern Dent or even Corn Belt Dent ,21. DoebMaize landraces are open-pollinated varieties from which farmers save seed for subsequent planting. They are not static populations since they continue to evolve in response to farmer-directed and natural selection for adaptation to local physical, social, and cultural environments within a particular geographical region ,24,25. TUse of specific landrace varieties is typically associated with perceptual distinctiveness (PD) traits, which serve as indicators for identification and maintenance of landrace integrity . These tThe PD traits, which allow recognition of individual landraces by farmers, can also be used by taxonomists to create and manage racial diversity ,29. The Tunable genotyping by sequencing (tGBS) of blue southwestern maize landraces was performed to identify their genetic relatedness. We also examined morphological and biochemical traits previously used to classify maize races and sub-races to allow comparison with tGBS findings. The genotypic analysis (tGBS) showed degree of relatedness among the southwestern landraces, but all (except Navajo Blue) were unrelated to the Corn Belt variety ‘Ohio Blue’. Morphological and kernel compositional traits ascertained phenotypic and biochemical variation. The highest variability between southwestern landraces was observed for kernel traits and the lowest for plant traits.Several sets of single nucleotide polymorphisms (SNPs) were generated during tGBS analysis. The first set of 1,437,967 polymorphic sites included all sites that differed from the reference in at least one sample. This set was generated after all reads that aligned to the reference genome. Then we examined, sample-by-sample and identified a set of 217,178 “ALL SNPS”, which aligned to the polymorphic sites. Subsequently, ALL SNPs were further filtered and a subset of high-quality SNPs was identified that had less than 50% missing data (low missing data or LMD50) across the 105 individuals. The resulting LMD50 (each of which was genotyped in at least 50% of the samples) SNP set contained 81,038 SNPs.Distributions of various characteristics for the LMD50 SNPs dataset, including quantity of missing data, minor allele frequency, heterozygosity and genotype number, are summarized in Based on 81,038 SNPs from 105 individuals of seven accessions, the population structure within southwestern blue maize landraces was investigated. We ran the Admixture software with K ranging from 1 to 10 in the assumptions of 1 to 10 sub-populations defined in the studied genotypes. By the cross-validation error (CV error) among K = 1 to 10, the minimum CV error was detected at K = 2  and consThe phylogenetic tree of all 105 sequenced genotypes is shown in In phenotypic evaluations, pre-harvest plant and post-harvest trait diversity were discerned through detailed examination of variation related to agro-morphological features of plant, ear, and kernel descriptors . Trait vThe relative proportion of color classes for different morphological traits and presence of kernel dent phenotype between different blue maize landraces is shown in The descriptive statistics for kernel biochemical traits across locations are shown in In order to assess the morphological and biochemical diversity between southwestern blue maize landraces we examined the traits in groupings that could reflect associated PD traits. In this manner, pre- and post-harvest morphological traits were also examined at the specific plant organ level. PCA analyses were then performed on pre-harvest plant traits, post-harvest ear and kernel traits, and kernel biochemical traits. The variation contributed by these traits can be seen in Variability contributed by different plant traits can be seen in Diversity associated with kernel traits was mainly related to post-harvest kernel morphological and biochemical traits. PCA biplot for kernel traits is shown in Genotypic, morphological, and biochemical traits were used to determine the genetic diversity and relatedness of southwestern U.S. blue maize landraces. The landraces were representative of different geographic regions of the Southwest, and a Corn Belt Dent variety was included for comparison. The relatedness of Hopi Blue, Yoeme Blue, Taos Blue, Los Lunas High and Navajo Blue suggests that there has been a common origin, with some gene flow between distinct regional landraces. Noteworthy is the east-west diffusion between Taos Blue and Navajo Blue and between Yoeme Blue and Hopi Blue across northern and southern Arizona/northern Sonora, Mexico. Overall findings suggest that the southwestern landraces are genetically closely related, but selection has resulted in differing phenotypes. A key finding of our study was the dissimilarity of natural classifications achieved by phenotypic and genotypic analysis. Conclusions regarding groupings will vary depending on the type of analysis, number of traits evaluated in a given analysis, and uncontrolled variation accrued from sample size, individual trait attributes , and environmental factors.Genotypic analysis was based on 81,043 high quality SNPs whereas phenotypic analysis was conducted using 40 diverse morphological traits. The disproportionate number of traits could have been a major factor for these differences. Evaluation of morphological traits included plant, ear and kernel traits. Kernel traits were more variable than the plant and ear traits, therefore our findings showed similarity for some traits whereas dissimilarity among others. The genotypic analysis was done based on the genetic sequences and none of the variation was accrued from environmental factors, which were likely a major source of variation in phenotypic analysis. Our findings, taken together provide a fuller picture of both the genotypic and phenotypic relatedness between the landraces.The findings from our PCA analysis for morphological traits showed a variation of 57.7%, 14.1%, and 11.7% due to PC1, PC2, and PC3, respectively. These values were lower than those observed by Sánchez et al.  which suHernandez and Flores  studied The qualitative traits of tiller, silk, glume, midrib, shoot and cob color of blue maize have not been studied previously for racial classification with the exception of the Soleri and Smith  study. TSánchez and Goodman  classifiDiversity of germplasm collections can be studied at phenotypic or morphological, geographical, molecular, and functional levels . GenotypWe examined six blue maize landraces representative of the blue maize found in Arizona and New Mexico. The six landraces and two improved open-pollinated populations were evaluated during the 2014 field study. The southwestern landraces: Taos Blue (NS/S ZM03-015), Yoeme Blue (NS/S ZM01-011), Hopi Blue (NS/S ZM02-147), and Flor del Rio (NS/S ZP-093 Popcorn), were made available through Native Seeds/SEARCH (NSS) . Navajo Blue was obtained from Plants of the Southwest , Santa Ana Blue, and Los Lunas “High” populations were contributed by the New Mexico State University (NMSU) Agricultural Science Center, Los Lunas, NM. Los Lunas “High” was selected from Santa Ana Blue and possibly other Puebloan blue maize varieties. For comparison Ohio Blue, a Corn Belt Dent variety derived from Blue Clarage and Ned’s Blue, was obtained from the Ohio Agricultural Research and Development Center, Ohio State University, Wooster, OH. Clarage is the oldest documented landrace in Ohio and was once widely cultivated in central and northern Ohio . Other rDuring 2014, blue maize landraces were planted at NMSU Agriculture Research Centers at Los Lunas and Alcalde, New Mexico. A randomized complete block design with three replications was used. Seeds were planted in 6.1 m long plots with planting distance of 0.45 and 0.61 m between hills (2 seeds per hill) at Los Lunas and Alcalde, respectively. Los Lunas is located in Valencia County, NM and is situated at latitude of 32.28 and longitude of −106.76 with elevation of 1480 m. Alcalde is located in Rio Arriba County, NM and is situated at 36.68 latitude and −106.05 longitude with elevation of 1741 m.A total of 105 samples representing 15 plants from each of six open-pollinated varieties of southwestern blue maize, and one of Corn Belt blue maize, were genotyped with Data2Bio’s tunable genotyping by sequencing technology .Prior to alignment, the nucleotides of each raw read were scanned for low quality bases. Bases with PHRED quality value <15 (out of 40) ,44, i.e.Trimmed reads were aligned to the Maize reference genome using GSNAP  and conf○The most common allele was supported by at least 80% of all the aligned reads covering that position.○At least 5 unique reads supported the most common allele.○Polymorphisms in the first and last 3 bp of each read were ignored.○Each polymorphic base had at least a PHRED base quality value of 20 (≤ 1% error rate).Homozygous SNP calling○Each of the two most common alleles was supported by at least 30% of all aligned reads covering that position.○At least 5 unique reads supported each of the two most common alleles.○The sum of reads of the two most common alleles accounted for at least 80% of all aligned reads covering that nucleotide position.○Polymorphisms in the first and last 3 bp of each quality-trimmed read were ignored.Heterozygous SNP callingThe coordinates of confident and single (unique) alignments to the consensus reference sequence that passed our filtering criteria were used for SNP discovery. Polymorphisms at each potential SNP site were carefully examined and putative homozygous and heterozygous SNPs were identified in each sample separately using the following criteria:Each polymorphic base had at least a PHRED base quality value of 20 (≤1% error rate).Population genetic structure was analyzed using Admixture software version 1.3.0 . AdmixtuPairwise distances were estimated between genotyped individuals using an unbiased model of substitution frequencies. Distance estimates were then used to construct a phylogenetic tree using the Neighbor-Joining like algorithm described by Criscuolo and Gascuel  and impl® ULTRATECH, Secaucus, NJ, USA). Weights of ear, cob, kernel, and 100-kernel samples were measured using a NewClassic MS Balance .A total of 40 morphological traits were examined . Pre-harFive kernel biochemical traits were analyzed from representative kernel samples produced at Los Lunas and Alcalde in 2014. The Experiment Station Chemical Laboratory of the University of Missouri analyzed the kernel constituents. The traits of total fatty acids, oil, protein, starch, and anthocyanin were examined. Oil and protein were measured by AOAC method 920.39 (A) and 990.03. Starch content was analyzed using base method: American Association of Cereal Chemists, approved method 76-13 and total fatty acids were analyzed by AOAC official methods 996.06 and Ca 5b-71. Total anthocyanin content was analyzed according to Li et al.  and Nankggplot2 version 3.3.3 [missMDA version 1.18 [FactoMineR version 2.4 [Factoextra version 1.0.7 [Analyses of variance for both pre- and post-harvest traits were performed for each location separately as well as interaction between accession and location (A*L) was also evaluated using pooled data across both experiment locations. Analysis of variance of quantitative traits was performed using “PROC GLM” and Chi-Square analysis of qualitative traits was performed using “PROC FREQ”. Landraces were considered as fixed effects and locations were considered as random effects. Means were compared by least significant differences test using “LSD” mean separation. Statistical analysis was performed with SAS V9.3 . Principon 3.3.3 , missMDAion 1.18 , FactoMision 2.4 , and Facon 1.0.7  R packagIn this research, we have employed data on morphological, biochemical, and molecular variation to characterize the genetic diversity of southwestern blue maize landraces. The use of a molecular technique tGBS was more effective than morphological or biochemical traits for determining distinct varieties among southwestern blue corn landraces. The coalesced analysis of genetic structure, phylogeny, and principal component analysis proved to be effective in elucidating genetic structure of southwestern blue maize landraces from the midwestern Ohio Blue variety. However, the majority of southwestern landraces appeared to display interracial diffusion and belong to the same cohort, with the exceptions of Los Lunas High and Flor del Rio. Among the southwestern blue maize landraces, Navajo Blue displayed noticeable variation whereas Santa Ana, Los Lunas High, Flor del Rio, Yoeme Blue, Hopi Blue and Taos Blue showed little variation. Weight of cob, ear, kernel, 100 kernels and kernels per ear contributed to the variability in Navajo Blue. The groupings were more robust when performed using post-harvest traits. Our findings confirm that southwestern blue maize landraces are genetically related, and reflect the attributes of admixtures, but are phenotypically uniform. Diversity of trait values suggested that selection for a strong PD (blue kernel color) did not result in uniformity for other traits, but that overlap in phenotypic traits was consistent with earlier evidence of genetic exchange between southwestern landraces of maize.


Squamous cell carcinomas (SCCs) in the anogenital and head and neck regions are associated with high-risk types of human papillomaviruses (HR-HPV). Deregulation of miRNA expression is an important contributor to carcinogenesis. This study aimed to pinpoint commonly and uniquely deregulated miRNAs in cervical, anal, vulvar, and tonsillar tumors of viral or non-viral etiology, searching for a common set of deregulated miRNAs linked to HPV-induced carcinogenesis. RNA was extracted from tumors and nonmalignant tissues from the same locations. The miRNA expression level was determined by next-generation sequencing. Differential expression of miRNAs was calculated, and the patterns of miRNA deregulation were compared between tumors. The total of deregulated miRNAs varied between tumors of different locations by two orders of magnitude, ranging from 1 to 282. The deregulated miRNA pool was largely tumor-specific. In tumors of the same location, a low proportion of miRNAs were exclusively deregulated and no deregulated miRNA was shared by all four types of HPV-positive tumors. The most significant overlap of deregulated miRNAs was found between tumors which differed in location and HPV status . Our results imply that HPV infection does not elicit a conserved miRNA deregulation in SCCs. Human papillomaviruses (HPV), species-specific viruses infecting mucosal and cutaneous epithelium of the host, can cause benign diseases such as papillomata or warts; however, some HPV types have oncogenic potential and are reported to be responsible for up to 5% of all tumors worldwide and up to 30% of those whose development is attributed to infectious agents . The mosThe proper molecular profiling of HPV-positive and HPV-negative tumors and comparisons of differences between the two etiologies is necessary for expanding our understanding of the mechanisms of carcinogenesis. There are only few publications addressing the differences in molecular profiles between HPV-associated and HPV-independent tumors. Koncar et al. determined the expression of 24 proteins by immunohistochemistry, mutations of 48 genes by sequencing, and copy number alterations for six genes by in situ hybridization for cohorts of vulvar, anal, cervical, and oropharyngeal tumors, reporting the HPV-positive tumors to have similar molecular profiles . HoweverPromising specific, sensitive, and clinically significant biomarkers are miRNAs, small single-stranded non-coding RNAs that regulate gene expression and play an important role in cell development, growth, and differentiation. Their expression has been reported to be deregulated in tumors. MicroRNAs play a role in stimulating tumor growth by negative regulation of tumor suppressor genes or by positive regulation of oncogenes. Specific miRNA profiles have been reported in many studies of various tumors, for example, of leukemia, breast cancer, lung cancer, or prostate cancer ,16,17,18In our study, we analyzed the miRNA expression profiles in a set of anogenital tumors associated with HPV, namely, cervical, vulvar, and anal tumors, and in a set of tonsillar tumors by next-generation sequencing (NGS) in an attempt to identify commonly deregulated miRNAs exclusive for HPV-dependent SCCs. To our knowledge, this is the first study to analyze miRNA profiles in four HPV-driven types of tumors which employs a unified methodological approach. Furthermore, this study provides miRNA expression analysis of insufficiently researched anal and vulvar carcinomas.Samples of SCCs were obtained from the Department of Pathology and Molecular Medicine, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, and from the Institute of Pathology, 3rd Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague. All tissue samples originated from unique patients. Formalin-fixed paraffin-embedded (FFPE) samples prepared from tumor tissues were stained and macrodissected . The HPV® DNA/RNA FFPE . RNA concentration and quality were measured by a Nanodrop™ spectrophotometer  and Experion chip electrophoresis . From purified RNA, sequencing libraries were prepared using the TruSeq Small RNA Library prep kit . The libraries were sequenced on NextSeq 500  at the Division of Molecular Medicine, Ruđer Bošković Institute , or the EMBL’s Genomics Core Facility (GeneCore)  using the NextSeq 500/550 High Output Kit . Sequencing reads were deposited in the SRA, GenBank (BioProject ID: PRJNA718204).Total RNA was isolated from macrodissected sections of FFPE samples using AllPreppadj  ≤ 0.1 were extracted and further analyzed. The Adonis–Bray test [Sequencing reads were trimmed off adapter sequences using the FastQ toolkit Basespace App (Illumina). The trimmed reads were mapped onto human reference genome assembly GRCh38.p13 using the Geneious mapper  , of the corresponding HPV-negative tumors (if available), and of healthy tissues were collected from three patients per sample type and processed for NGS. Sequencing yields ranged from 338,068 to 4,273,955 total reads per sample; the percentage of miRNA-derived reads varied from 6.3% to 82.9% . The expp = 0.0001) and tissue type  of samples. This is in accordance with miRNA expression being long recognized to exhibit tissue-specific and tumor-specific differences [p = 0.074).The similarity of tissue samples based on their miRNA expression profiles is visualized in ferences ,38,39,40We compared miRNA expression between tumor and normal tissues of the same locations, identifying miRNAs which exhibited differential expression (DE). This type of comparison is particularly suitable for pointing out the miRNAs whose deregulation is linked with carcinogenesis. The cross-tumor comparison revealed that the largest proportion of deregulated miRNAs was specific to a single tumor type (149/239 and 94/210 upregulated and downregulated miRNAs, respectively; Since infection with high-risk HPV16 is a key factor which delineates tumors between and within different anatomical locations, we investigated whether infection with HPV is functionally translated into a common set of deregulated miRNAs. Core HPV miRNAs were focused on in two previous studies ,32; howeIn our set of seven SCC types, the highest similarity in miRNA deregulation was detected between cervical and HPV-negative vulvar tumors. These tumors exhibited the largest total numbers of deregulated miRNAs a. Of allUnless diagnosed early, cervical cancers are notorious for their poor prognosis; the otherwise high mortality is kept in check only by strenuous screening and HPV vaccination. In our set of SCCs, cervical carcinomas exhibited the most extensive miRNA deregulation a. Among In conclusion, this study demonstrates that miRNA deregulation in various SCC types is largely tumor-specific and that the HPV status of SCCs is not a determinant of either the extent or the composition of the deregulated miRNA pool. Our findings may provide useful directions for further studies on the pathogenesis of individual types of SCCs with respect to their differences in anatomical location and HPV status.


Proc. R. Soc. B288, 20211769 (Published 20 October 2021). (doi:10.1098/rspb.2021.1769)e of our original paper, the middle pie chart had the colour schemes for the different career stage groups mixed up. In that paper, 17% was dark blue, 31% was blue and 52% was light blue. It should be 17% blue, 31% light blue and 52% dark blue, as shown in the corrected version below.In figure 3


Papillomaviridae family, constitute a group of small nonenveloped double-stranded DNA viruses. HPV has a small genome that only encodes a few proteins, and it is also responsible for 5% of all human cancers, including cervical, vaginal, vulvar, penile, anal, and oropharyngeal cancers. HPV types may be classified as high- and low-risk genotypes  according to their oncogenic potential. HR-HPV 16 and 18 are the most common types worldwide and are the primary types that are responsible for most HPV-related cancers. The activity of the viral E6 and E7 oncoproteins, which interfere with critical cell cycle points such as suppressive tumor protein p53 (p53) and retinoblastoma protein (pRB), is the major contributor to HPV-induced neoplastic initiation and progression of carcinogenesis. In addition, the E5 protein might also play a significant role in tumorigenesis. The role of HPV in the pathogenesis of gynecological cancers is still not fully understood, which indicates a wide spectrum of potential research areas. This review focuses on HPV biology, the distribution of HPVs in gynecological cancers, the properties of viral oncoproteins, and the molecular mechanisms of carcinogenesis.Human papillomaviruses (HPVs), which belong to the  Helicobacter pylori. Among the 690,000 HPV-attributable cancer cases, 570,000 (83%) are cervical cancer cases, of which 500,000 (72%) can be attributed to the high-risk HPV (HR-HPV) types 16 and 18 and 120,000 (17%)—to HPV types 31, 33, 45, 52, and 58. HPV16 and HPV18 are responsible for almost all HPV-related cancers in men [Viral infections are recognized as strong risk factors for some types of cancer. Human papillomavirus (HPV) is the most common sexually transmitted infection and occurs via direct skin-to-skin or mucosa-to-mucosa contact. Benign cutaneous manifestations of HPV usually include papillomas, which are commonly called warts. HPV can act as a direct carcinogen by infecting cells that subsequently undergo neoplastic transformation. It exhibits specific tropism for the squamous epithelium. Specific types of the virus grow in the skin, while others grow in mucous membranes such as the vagina. The clinical effect of infection is skin and mucosal lesions in the form of warts and condylomas and in the form of both low-grade and high-grade dysplasia, the latter being a premalignant lesion. Worldwide, approximately 690,000 of the new cancer cases that are diagnosed every year can be attributed to HPV infection (age-standardized incidence rate (ASIR) of 8.0 cases per 100,000 person-years) [500,000 7% can be 570,000 8% are cerThe review provides new insight into the pathogenesis of HPV-related gynecological cancers and the possible HPV participation in the development of ovarian cancer. This review also discusses morphological and genetic aspects of HPV biology, oncogenic properties of viral proteins, and their effect on signaling pathways. To fully understand the HPV role in gynecological neoplasms, it seems appropriate to begin our review by discussing the biology of HPV and the function of its oncoproteins.Papillomaviridae family. The HPV genome is a circular double-stranded DNA molecule approximately 8000 bp in length and associated with histones to create chromatin-like structures [L1 gene sequence. More than 200 different HPV genotypes have been identified that are categorized according to their epidemiologic association with cancer. Isolates of a virus type, whose L1 genes differ from an established type by 2–10%, as well as intermediates between types and variants are considered subtypes [E4 and E5 genes are also hypervariable [Human papillomaviruses are small nonenveloped viruses that belong to the ructures . HPVs haructures . The genructures . In the ructures . The E aructures . HPV typsubtypes . Genomessubtypes ,15. In tvariable . With revariable , octamervariable . The nexvariable . HPV genotypes are divided into two groups: oncogenic HR-HPV and non-cancerous low-risk (LR) types. The oncogenic group includes 14 HR-HPV types: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68; and 23 LR-HPV types: HPV 6, 11, 26, 40, 42, 53, 54, 55, 61, 62, 64, 67, 69, 70, 71, 72, 73, 81, 82, 83, 84, IS39, and CP6108 . HPV16 iE6 and E7 oncogenes through its specific binding to DNA recognition sites located within the promoter sequences. Loss of the E2 repressive functions may result in deregulated expression of both oncogenes and initiation of the transformation process [E1 and E2 genes, and the possibility of partial or total deletion of these genes [E6 and E7 genes and contributes to oncogenesis. The E4 ORF lies within the E2 ORF, but E4 gene products are expressed from the E1∧E4 spliced mRNA [Early proteins E1 and E2 are encoded by the E1 and E2 ORFs and are known to regulate the replication of the viral genome, as well as transcription of early proteins. These viral proteins tightly control and maintain the expression of the E6 and E7 oncoproteins at low levels. In particular, the E2 protein represses transcription of the  process . The E1 se genes . The losced mRNA . The E4 ced mRNA . It is aced mRNA ,28. ProtThe two late genes encode L1 (major) and L2 (minor) proteins, which form an icosahedral capsid around the HPV genome during the generation of progeny virions. The L1 protein has DNA-binding activity, while the L2 protein has domains capable of interacting with L1 capsomeres. During infectious entry, the nonenveloped virion uncoats in the endosome, whereupon conformational changes result in a dissociation of L1 from L2, which remains in complex with the HPV DNA. Capsid proteins L1 and L2 are critical for virion assembly . The L1 Three early HPV proteins, E6, E7, and E5, play an important role in the process of oncogenesis. The E6 and E7 proteins are related to functional inactivation of the main regulators of the cell cycle, tumor transformation suppressors, and activation of telomerases, while E5 enables keratinocyte differentiation and immune evasion ,66. HR-Hhect  domain E3 ligases [The E6 oncoprotein in some types of cancer is involved in carcinogenesis. It may participate in transcriptional activation, transformation, and immortalization or associate with cellular proteins. The E6 protein acts as a repressor of apoptosis and promotes the survival of severely damaged cells. The E6 protein consists of approximately 150 amino acid residues coding an 18 kDa protein . It cont ligases . E6AP co ligases .The E7 oncoprotein is a phosphoprotein of approximately 100 amino acids that contains three conserved regions 1/2/3 (CR1/2/3). The CR3 region at the carboxyl terminus encodes a zinc finger domain containing two CXXC motifs . The CR2The E6 and E7 oncoproteins are essential components for cellular immortalization and transformation, as well as carcinogenesis induced by HPV. The interactions of the HPV oncoproteins with host cellular proteins are involved in the activation or repression of cell cycle progression in carcinogenesis. The oncoproteins’ common function gives rise to a complementary and synergistic effect, inducing an increase in transforming activity . The intE5 gene encoding the E5 protein is expressed early during the lytic cycle of HPV. This gene is frequently deleted when the viral genome is integrated into the DNA of a host cell during malignant progression [The gression ,79. The gression . The molgression . This prgression . What isgression . It is igression ,83. Expression of the E5 protein disrupts the synthesis and function of the major histocompatibility complex (MHC) class I and II proteins. MHC class I downregulation can promote immune cell evasion by precluding cytotoxic T lymphocytes from recognizing infected cells . E5 may The E5, E6, and E7 oncoproteins alter multiple signaling pathways in the initiation and maintenance of HPV-associated cancers. We distinguish a variety of mechanisms through which HPV may impinge cellular pathways for its own needs, such as p53, pRB, epidermal growth factor receptor (EGFR), PI3K/Akt/mTOR, JNK/ERK/AP-1, ERK, E-cadherin, Wnt/β-catenin, NF-kB, JAK/STAT, TGF-β/TNF-α, Hippo, MAPK HIF1/VHL/VEGF, EMT, and YY1 ,88. HPV The p53 protein is a well-characterized tumor suppressor protein often called the “guardian of the genome”. This protein is a key regulator of cell fate under stress conditions and acts as a transcription factor for the genes needed for apoptosis or cell cycle arrest . The HPVThe pRB protein is a tumor suppressor that is involved in the negative control of the cell cycle and in tumor progression . This prHer-1 protooncogene located on chromosome 7p12. It is activated by the binding of some ligands, including the epithelial growth factor (EGF), giving rise to the formation of homodimers. EGFR functions through dimerization that activates a tyrosine kinase domain to regulate multiple functions such as cell growth, differentiation, gene expression, and development [The epidermal growth factor receptor (EGFR) is a 170 kDa transmembrane glycoprotein receptor that is encoded by the elopment . High EGThe HPV E5 oncoprotein is involved in the activation of and increase in the EGFR pathway. It interacts with the EGFR, the PDGF, and the colony-stimulating factor (CSF) and might promote angiogenesis in cancer through the EGFR/VEGFA pathway and the metastasis of HPV-containing malignancies. E5 creates complexes with the EGFR in cells overexpressing the receptor. This promotes prolonged activation of ERK1/2 and protein kinase B (Akt) in response to the EGF. The activation of EGFR-dependent pathways such as phosphoinositide 3-kinase (PI3K)/Akt increases the expression of the VEGF, leading to increased angiogenesis  Figure . DisturbMET transcripts and the hepatocyte growth factor receptor (HGFR) [E5 can decrease the autophagy process by downregulating keratinocyte growth factor receptor/fibroblast growth factor receptor 2b (KGFR/FGFR2b) signaling activation and plays a role in the regulation of programmed cell death . Throughr (HGFR) ,112. ThiThe Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway plays a key role in immune responses, cell proliferation, differentiation, and survival . The oveThe E6 and E7 oncoproteins are related to the PI3K/Akt/mTOR pathway, a major cancer survival pathway . PI3K reAnother target of Akt is the mammalian target of rapamycin (mTOR) kinase . It has Akt activation may produce a cascade of changes in later targets. Akt phosphorylates E6 to promote its ability to interact with the protein 14-3-3σ, which is a key step in carcinogenesis. HR-HPV E6 comprise C-terminal PBMs, which are subjected to phosphorylation events that are prone to modulating their interaction with PDZ domains and the 14-3-3 proteins . SeveralE6 gene in HPV-positive cells reduced nuclear β-catenin substantially. These results suggest that E6 plays a critical role in the activation of the Wnt pathway. Studies have also shown that HPV16 E6 activates the Wnt/β-catenin pathway. The mechanism is independent of the ability of E6 to target p53 for degradation or bind to the PDZ-containing E6 targets but requires E6AP [The Wnt signaling pathway is another HPV-related cancer pathway. Wnt ligands and the associated pathway regulate cellular proliferation and differentiation processes and play critical roles in normal tissue homeostasis , as wellres E6AP . In vivores E6AP . PI3K/Akres E6AP . HPV has a prolonged replication period and must persist in the host epithelium without being detected for an extended period. Therefore, the virus develops complex mechanisms to escape host immunosurveillance and interfere with the host’s virus eradication mechanism ,161. HPV+ T cells [The E6 oncoprotein may inhibit phosphorylation of tyrosine kinase 2 (TYK2) through the signal transducer and activator of transcription (STAT/TYK2) pathway and therefore prevents the association between IFN type α (IFNα) and its receptor . Upon E6 T cells ,190. TheHPV infection is one of the main causative factors in female genital tract cancers. Epidemiological studies show that HPV is detected in almost all cervical cancers and in between 40–85% of all vaginal and vulvar carcinomas. Considering that HPV is the most common sexually transmitted disease worldwide, the role of this oncovirus in the development of HPV-driven cancers is exaggerated. Prophylactic HPV vaccines against HR-HPVs are expected to offer protection against precursor lesions and genital carcinomas.Cervical cancer comprises the following histologic subtypes: squamous carcinoma (SCC), adenocarcinoma, and adenosquamous carcinoma. The majority (75%) of cervical cancers are of the SCC type. The old term for cervical dysplasia, “cervical intraepithelial neoplasia” (CIN), was changed to “squamous intraepithelial lesion” (SIL) following the 2001 revision of the Bethesda classification . CIN1 isE1 and E2 is launched. These genes activate viral replication by expropriating cellular DNA replication factors [E5, E6, and E7 are expressed, stimulating cells to propagate and grow. After cellular differentiation in the suprabasal layer, E4, L1, and L2 expression is activated. Subsequently, a capsid around the virus’ genomic material is formed by the L1 and L2 proteins, and the mature viral particles are released from the epithelial cells. The virions are sloughed off with the dead squamous cells of the epithelium.HPV infection may induce changes from a normal to dysplastic cellular architecture in the transformation zone. Most neoplastic lesions develop from cells in the transition zone, which is defined as the border between the cervical surface squamous epithelium and the glandular epithelium of the cervical canal. Small injuries expose the basal layer cells to HPV-infected cells from the cervical epithelium, enabling its penetration, which is a receptor-mediated process. The molecules that are involved in this process may be the heparin sulfate proteoglycans and integrins  that are present in the basal cell epithelium ,63. When factors ,196. To c-Myc expression is often altered in HPV-infected cervical cancer cells. Integration frequently leads to the disruption of the E2 gene site and the expression of the E6 and E7 oncogenes. However, HPV integration per se does not necessarily lead to increased oncogene expression or a cell growth advantage [HR-HPVs often integrate their genome into the human genome in cervical SSC tissue samples ,200,201.dvantage . The E6 dvantage ,204,205.dvantage . Dereguldvantage . Neitherdvantage . Biomarkdvantage . Howeverdvantage ,211.Histologically, about 90% of vaginal cancers are squamous cell carcinomas (VaSCC) . VaginalSeveral studies examined the prevalence of HPV infection in VaIN and vaginal cancer ,215,216.When HR-HPV DNA is integrated into host cell DNA, its carcinogenic effect on vaginal epithelial cells is exerted through the viral oncoproteins E6 and E7, which are actively transcribed. The E7 oncoprotein binds to and inactivates pRB, affecting cell cycle control, overturning the control and repair system in the cell, and leading to an overexpression of the tumor suppressor protein p16, a cyclin-dependent kinase-4 inhibitor. A meta-analysis of the prevalence of p16 revealed that the vast majority of HPV-positive vaginal cancers show p16 overexpression, suggesting active involvement of the virus in the malignant transformation process . HPV is also known to cause some vulvar squamous cell cancers (VSCCs). The precursor lesion for VSCC is high-grade vulvar intraepithelial neoplasia (VIN). Approximately 25–42% of VSCCs are induced by HR-HPVs ,223,224.Endometrial cancer is one of the most common gynecological malignancies affecting more than 300,000 women worldwide. Its incidence is much higher in more developed countries. The involvement of HPV infection in the pathogenesis of endometrial carcinoma is controversial. A meta-analysis examining the prevalence of HPV DNA in tumor tissue from endometrial cancer revealed that the pooled prevalence of HPV DNA in endometrial cancer was 10.0% ,232,233.Ovarian cancer (OC) is the eighth most common cancer in women around the world. OC is a heterogeneous disease with different histological types. Serous tumors account for approximately 70% of all epithelial ovarian cancer (EOC) tumors and are responsible for the majority of deaths from ovarian cancer. More than 90% of serous carcinomas are very aggressive and defined as high-grade serous ovarian carcinomas (HGSOCs). The remaining 10% are low-grade serous ovarian carcinomas (LGSOCs) that generally have a better prognosis. Asymptomatic early stages of the disease and failure to identify precursor lesions delay tumor detection, which makes diagnosis difficult until the OC is in advanced stages ,235,236.BRCA1, BRCA2, RAD51C, RAD51D, and TP53 genes [E6 and/or E7 mRNA was also detected [Three hypotheses have been formulated to explain how both the fallopian tube and the ovary might contribute to tumorigenesis of EOC. The first hypothesis suggests that the border between the fallopian tube and the ovary is an area of epithelial transition that is rich in stem cells and therefore vulnerable to malignant transformation . The sec53 genes . The ris53 genes ,245. The53 genes ,253,254 53 genes . In cont53 genes ,256,257.53 genes ,259,260.53 genes ,253,258.53 genes ,262. The53 genes ,262, resdetected ,263. HPVdetected . It is hypothesized that the inactivation of p53 and pRB by the HR-HPV E6 and E7 oncoproteins can lead to the development of ovarian cancer. However, the mechanisms that can induce the development of disease in patients remain unknown. The neoplastic processes in EOC may be similar to those found in other types of cancer, e.g., cervical cancer, and research is still ongoing to confirm or refute this possibility and create a completely new model of EOC development. Taking into consideration the fatal course of ovarian cancer and the lack of very effective treatment in advanced and recurrent cases, it is important to understand the mechanisms of its formation. Based on the proven role of HPV in many cancers, it seems reasonable to check a possible role of HPV in ovarian carcinogenesis. This speculation is supported by observations that inflammation plays an important role in triggering and maintaining EOC progression. Moreover, it was observed that tubal closure reduces the risk of EOC (probably by cutting off the route of ascending infection) . HPV can cause multiple types of gynecological cancers, although HPV is the greatest risk factor for cervical cancer. A subset of cancers, including vulvar and vaginal cancers, have been attributed to HR-HPV infection. The role of HPV in ovarian and endometrial cancers is still undefined. The most widely accepted hypothesis points to occasional HPV infection in combination with other risk factors, including chronic inflammation and genetic predisposition. HPV infection plays an important role in the risk of precancerous lesions and therefore in the dysplastic and malignant transformation of lesions. The main factor influencing carcinogenesis is the presence of HPV oncoproteins. These viral proteins have a remarkable capacity to impair multiple key regulatory pathways and induce all of the known characteristic features associated with cancers. The E6 oncoprotein can inactivate p53 and PDZ while stimulating the PI3K/Akt and Wnt pathways. E7 can inhibit pRB and stimulate the PI3K/Akt pathway. E5 can augment their function and contribute to tumor progression. Altered signaling pathways in turn promote cell proliferation, decrease in cell apoptosis, increase in angiogenesis and cell migration. The combined expression of the HPV E6 and E7 oncoproteins leads to a complementary and synergistic effect that induces cell immortalization and transformation. Understanding the mechanism of HPV involvement in cancer development and the role of viral oncoproteins is essential for the effective prevention of the neoplastic process and the development of screening tests and therapies.


Predictions of cervical cancer burden and the impact of measures taken to control this cancer are usually data-demanding and based on complex assumptions. We propose a predictive method  based on human papillomavirus (HPV)prevalence, measured 1993–2008, and cervical cancer incidence (CCI), measured 1993–2012, in the same birth cohorts from different worldwide locations, informed by data on age at detection of high-risk HPV and sexual debut. The model can predict CCI among high-risk HPV–positive women and predict CCI up to 14 years following high-risk HPV detection. We found CCI to increase during the 14 years following high-risk HPV detection in unscreened women aged <35 years but to remain mainly constant among women ≥35 years. Age at sexual debut was a significant modifier of CCI. Using our model, we accurately reproduced CCI among high-risk HPV–positive women as observed in cohort studies and in the general population of multiple countries. We also predicted the annual number of cervical cancer cases and CCI in locations with HPV prevalence data but no cancer registry. These findings could inform cervical cancer control programs in settings without cancer registries, as they can be used to predict future cervical cancer burden from population-based surveys of HPV prevalence. CIconfidence intervalCI5Cancer Incidence in Five ContinentsHPVhuman papillomavirusIARCInternational Agency for Research on CancerPIprediction intervalhttps://gco.iarc.fr/), where sex- and age-specific estimates of incidence and mortality for 38 cancer sites in 185 countries or territories are calculated, relying upon the best available data on cancer incidence and mortality nationally  vaccination, cervical cancer screening, and the management of detected disease . An accuMathematical  and statOur cervical cancer incidence predictions in the general population are based on estimates of cervical cancer incidence rates in high-risk HPV–positive women, obtained by assessing at an ecological level the association between high-risk HPV prevalence and cervical cancer incidence rates in the same birth cohorts. To estimate our predictions, we also accounted for the effect of the time interval between high-risk HPV prevalence measurement and cervical cancer detection (hereafter “time lag”), of average age at sexual debut within the birth cohort, and of cervical cancer screening. Below we describe data sources and the statistical methods used to parameterize and validate the PANDORA predictive model.https://doi.org/10.1093/aje/kwab254). All cross-sectional surveys (n = 28) used a standardized protocol for population-based recruitment and detection of HPV in cervical cell samples using a general primer (GP) 5+/6+-based polymerase chain reaction assay detecting at least 36 types , Volumes VIII–XI, a series of monographs published every 5 years by IARC and the International Association of Cancer Registries, including age-stratified data from high-quality cancer registries worldwide , 8–10.As a preliminary analysis, we quantified the association between high-risk HPV prevalence and cervical cancer incidence in the same birth cohorts, as reported in CI5, Volume XI 2008–2012) 008–2012 . High-riR2 to assess the goodness of fit of the models . Subsequently, we assessed the association between age-specific high-risk HPV prevalence and cervical cancer incidence for the same 10-year birth cohort fitting a Poisson regression model, with a linear link function, separately for locations with a time lag <10 and ≥10 years between assessment of high-risk HPV prevalence and cancer incidence. Since HPV prevalence cannot be negative, the intercept of the regression models, which can be interpreted as the cumulated risk of cervical cancer in the time elapsed between the assessment of high-risk HPV prevalence and cancer incidence, was not allowed to be negative. In the Poisson models, we also included age at HPV detection as a modifier of the effect of HPV prevalence on cervical cancer incidence rates. Since cervical cancer screening is an important confounding factor at a population level, we also conducted a sensitivity analysis excluding data from the locations with organized screening programs with reported coverage above 50% . The ote models . See WebWe went on to estimate cervical cancer incidence among high-risk HPV–positive women. In this analysis, we included a subset of 16 locations (Web Table 1) for which cervical cancer incidence data from CI5 Volumes VIII–XI , 8–10 coTo estimate cohort-specific incidence of cervical cancer in women with prevalent high-risk HPV infection, we fitted, for each age-group at HPV detection, a mixed-effect linear regression model with years since HPV detection, average age at sexual debut, their interaction, and screening implementation status as covariates, location as grouping variable, and the risk of cervical cancer  as an outcome. See Web Appendix 1 for more details.To be able to include at least 3 locations in each analysis, we limited the time interval for cancer incidence estimates to 14 years. For the same 14 locations without screening, average age at sexual debut (Web Table 2) was estimated from the IARC HPV Prevalence Surveys database , whereasR2 using a random 10-fold cross-validation procedure .R2 was 0.61, ranging between 0.49 and 0.81 . Similar results were obtained analyzing the data set restricted to locations with at least ten HPV infections in each age group .Results of the mixed-effect regression model are shown in The annual incidence of cervical cancer  over a period of 14 years following HPV detection, by age group at HPV detection, is shown in Estimated cervical cancer risk in the 14-year period following high-risk HPV detection, restricted to locations without screening, according to average age at sexual debut is shown in To provide external validation of our estimates, we adapted our predictive model to reproduce the cervical cancer incidence measured in 2 cohort studies among high-risk HPV–positive and cytology-negative women. In one study, nested in a cervical cancer screening trial conducted in the Netherlands, estimated cervical cancer incidence was 1.9  per 1,000 women aged 29–61 years . In the According to our model-based estimates, cervical cancer incidence among high-risk HPV–positive women aged 30–59 years was 2.0  and 4.7  per 1,000 women in locations with screening and without screening, respectively (data not shown).For Bhutan and Nigeria, our model-based estimates were capable of accurately predicting the incidence reported by the local cancer registry for the same birth cohorts . For GeoWe have developed, parameterized, and validated PANDORA, a parsimonious model to predict age-specific cervical cancer incidence in a population in which we know age-specific high-risk HPV prevalence, average age at sexual debut, and screening implementation status. To do that, we characterized and quantified the relationship between age-specific HPV prevalence and cervical cancer incidence in the same female birth cohorts as a function of time lag between high-risk HPV prevalence and cancer incidence assessment. We found a strong positive effect of high-risk HPV prevalence on cervical cancer incidence, which increased with age at HPV detection and with time lag. This finding is consistent with the observation that prevalent HPV infections in older women are more likely to be persistent and have a higher risk of causing cervical cancer . In a miOur internal validation assessment suggests that model-based estimates are robust to data perturbation and fairly accurate to predict cervical cancer incidence in a new location. The external validity of our findings is supported by the comparison of our model-based estimate of cervical cancer incidence among HPV-positive women with empirical measurements from cohorts of HPV-positive and cytology-negative women from Amsterdam and Taiwan. PANDORA’s estimate for the screened population  per 1,000 women), matches well data from Amsterdam , where the organized screening program was fully implemented , whereasEcologic inference has been previously used by Maucort-Boulch et al.  and SharOur observations—1) that cervical cancer incidence among women below 35 years with prevalent high-risk HPV infection is modulated by age at first sex, as a proxy of early exposure to HPV infection, and 2) that cervical cancer incidence increases until approximately 45 years of age and subsequently remains constant—are consistent with data from unscreened populations  and withWe have adapted our study design to account for some typical limitations of ecological studies. First, to account for the latency between exposure and outcome , we assessed the 2 measurements in the same birth cohorts and incorporated the time lag between the 2 measurements. Second, we matched HPV and cervical cancer data from the same (or a similar) location, to geographically match, as far as possible, exposure and outcome measurements. Third, we accounted for age at first sex and cervical cancer screening as a possible confounder of the ecologic relationship in the same locations between HPV prevalence and cervical cancer incidence.The effect of other location-related determinants of cervical cancer risk given high-risk HPV detection, such as human immunodeficiency virus status, reproductive behavior factors, hormonal contraceptive use, and smoking, for which we were unable to explicitly account, may also have been captured by the between-location variance. Finally, high-risk HPV prevalence survey data and incidence data from the cancer registries for the same geographic areas were not available for 4 out of 16 locations. Hence, for these settings, we selected cancer registries with comparable characteristics, whenever possible in the same region or country (see Web Table 1 for more details).Consequently, our predictions of the annual incidence of cervical cancer in countries not represented in CI5 are based on average age at sexual debut, time elapsed since HPV assessment, and the age-specific high-risk HPV prevalence assessed in each survey. The accuracy of the reported predictions relies on the assumption that the age-specific high-risk HPV prevalence assessed in the survey is representative of prevalence in the whole country. Our estimates of the risk of cervical cancer among high-risk HPV–positive women are based upon the systematic use of the GP5+/6+ test, which is validated for clinical purposes. Hence our findings should be applicable to other population-based data sets obtained using HPV tests with a similar sensitivity but might be less applicable to prevalence data generated with more sensitive methods.In conclusion, using worldwide, high-quality, standardized data on age-specific HPV prevalence and cervical cancer incidence from the same location, we have built a model to estimate age-specific cervical cancer incidence over time elapsed since high-risk HPV detection, as a function of average age at first sex in the female population. PANDORA could be particularly useful in designing and planning cancer control programs in settings without cancer registries as a tool to predict the expected burden of cervical cancer from high-quality data collected through population-based cross-sectional HPV prevalence surveys, which are relatively easy to implement.Web_Material_kwab254Click here for additional data file.