The important contribution of glia to mechanisms of injury and repair of the nervous system is increasingly recognized. In stark contrast to the central nervous system (CNS), the peripheral nervous system (PNS) has a remarkable capacity for regeneration after injury. Schwann cells are recognized as key contributors to PNS regeneration, but the molecular underpinnings of the Schwann cell response to injury and how they interact with the inflammatory response remain incompletely understood.We completed bulk RNA-sequencing of Schwann cells purified acutely using immunopanning from the naïve and injured rodent sciatic nerve at 3, 5, and 7 days post-injury. We used qRT-PCR and in situ hybridization to assess cell purity and probe dataset integrity. Finally, we used bioinformatic analysis to probe Schwann cell-specific injury-induced modulation of cellular pathways.Our data confirm Schwann cell purity and validate RNAseq dataset integrity. Bioinformatic analysis identifies discrete modules of genes that follow distinct patterns of regulation in the 1st days after injury and their corresponding molecular pathways. These findings enable improved differentiation of myeloid and glial components of neuroinflammation after peripheral nerve injury and highlight novel molecular aspects of the Schwann cell injury response such as acute downregulation of the AGE/RAGE pathway and of secreted molecules Sparcl1 and Sema5a.We provide a helpful resource for further deciphering the Schwann cell injury response and a depth of transcriptional data that can complement the findings of recent single cell sequencing approaches. As more data become available on the response of CNS glia to injury, we anticipate that this dataset will provide a valuable platform for understanding key differences in the PNS and CNS glial responses to injury and for designing approaches to ameliorate CNS regeneration.The online version contains supplementary material available at 10.1186/s12974-022-02462-6. Like the CNS, the peripheral nerve is protected within several layers of connective tissue and behind a diffusion barrier, the blood–nerve barrier. Within the epineurium, the peripheral nerve is composed of endoneurial fibroblasts, endothelial cells, pericytes, resident macrophages, Schwann cells, and efferent and afferent axons . FollowiUnderstanding the mechanisms underlying the remarkable ability of the PNS to regenerate after injury has been a topic of intense interest for decades. The Schwann cell, a neural crest-derived glial cell type comprising 80% of the cells in the uninjured nerve has long been recognized as a key player in PNS regeneration, undergoing a remarkable cellular transformation after nerve injury and assuming multiple pro-regenerative functions during recovery of the injured nerve . While tUntil recently, knowledge of global changes in Schwann cell gene expression during the 1st week after injury was largely based on data obtained from whole sciatic nerve before and after crush or transection injury , 44, 49.www.BrainRNAseq.org) that provides a simple and searchable platform for browsing gene expression data. Our dataset provides a helpful resource for further deciphering the Schwann cell injury response. Furthermore, as additional data become available on the response of CNS glia to injury, we anticipate that this dataset will provide a valuable platform for understanding key differences in the PNS and CNS glial responses to injury and for designing approaches to ameliorate CNS regeneration.Here, we provide a high-resolution RNAseq transcriptome of Schwann cells acutely purified from the uninjured and injured rat sciatic nerve at 3, 5, and 7 days after nerve crush injury at postnatal day (P)18 . In addiAll procedures involving animals were conducted in conformity with Stanford University guidelines and are in compliance with national and state laws and policies according to APLAC protocol 10726.All surgical experiments were performed under 2.5% isoflurane on rats at postnatal day 18. Sciatic nerve crush injury was performed as previously described . Briefly10–20 sciatic nerves were used for the generation of each sample of purified cells. For injured nerves, sciatic nerve tissue was collected beginning proximally 1 mm distal to the graphite-marked crush site and extending distally along the tibial nerve branch as far as possible approaching the medial calcaneus. The proximal portion of the peroneal nerve branch was included when intact dissection was possible. Schwann cells were isolated from the entirety of the harvested nerve segment. For uninjured nerves, the anatomically equivalent nerve segment was isolated. Schwann cells were acutely purified using immunopanning from the injured or uninjured rat sciatic nerve according to Lutz . BrieflyCultured cells were fixed with 4% paraformaldehyde for 10 min at room temperature 24 h after immunopanning and then permeabilized and blocked with 10% goat serum, 0.2% TritonX-100. Cells were labeled using antibodies to goat Iba1 (Abcam) and mouse S100 (Sigma s2532) as well as Cellmask (ThermoFisher H34558) and DAPI.RNA from whole nerves was purified according to the RNeasy Fibrous Tissue kit. For injured nerves, only the nerve segment distal to the site of crush injury was included. For uninjured nerves the anatomically equivalent nerve segment was isolated. A minimum of 10 nerves was used for the generation of each RNA sample. RNA quality was verified through Bionalyzer analysis. All samples submitted for RNASeq had a RIN of 8 or above.RNA from purified Schwann cells was isolated at the end of the immunopanning procedure by scraping the O4-positive panning dish according to the instructions of the RNeasy MICRO kit. RNA quality was verified through Bionalyzer analysis. All samples submitted for RNASeq had a RIN of 8 or above.® RNA-seq system V2 (Nugen 7102). First and second-strand cDNA synthesis and SPIA amplification were performed following the manufacturer’s instructions, and cDNA was fragmented with a sonicator (Covaris S2) using the following parameters: duty cycle 10%, intensity 5, cycles/burst 100, time 5 min. We then used the Next Ultra RNA-seq library prep kit for Illumina (NEB E7530) and NEBNext® multiplex oligos for Illumina® (NEB E7335 E7500) to perform end repair, adaptor ligation, and 5–6 cycles of PCR enrichment according to manufacturer’s instructions. The quality of the libraries were then assessed by bioanalyzer and qPCR and high-quality libraries were sequenced by the Illumina NextSeq sequencer to obtain 150-bp pair-end reads to a minimum depth of 20 million reads per sample. All libraries passed standard QC metrics .Total RNA that passed QC standards  proceeded to library synthesis using the Ovationhttps://www.r-project.org/). We deposited all raw .fastq files from the RNA-seq data in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus . Unsupervised hierarchical clustering of FPKM data was performed in R using the top 2000 most variable genes with the following settings: Linkage; average, ColumnPdistance; Spearman, standardized across rows.FASTQ files were first groomed using FASTQ groomer and then mapped using TopHat2, which invokes Bowtie as an internal read mapper. The paired end option was selected and rat genome Rnor_6.0 was used as the reference genome. We then ran Cufflinks to assemble transcripts and estimate expression level as fragments per kilobase of transcript sequence per million mapped fragments (FPKM). All subsequent data analysis was performed using R software p = 21). The minimum WGCNA module size was 30 genes. Modules with < 0.25 similarity were merged.WGCNA analysis was performed using the R package ‘‘WGCNA’’ , 25. TheWe designed primers spanning exon–exon junctions to avoid amplification of genomic DNA. The primers used are listed in Additional file Full-length rat cDNA expression plasmids for the genes analyzed were linearized and transcribed with T7, Sp6, or T3 RNA polymerases to generate fluorescein (FITC)-labeled single-stranded anti-sense riboprobes according to manufacturer’s instructions  and purified over G-50 columns   or down-regulated (sevenfold or more decrease in FPKM) during the course of injury. First, we used qRT-PCR to validate a subset of these top differentially expressed Schwann cell genes in the week following nerve crush injury. We found consistent agreement between the qRT-PCR data and our RNAseq findings Fig. , selecteOur in situ hybridization confirmed the anticipated changes in gene expression in S100-positive Schwann cells, with putative upregulated transcripts exhibiting no signal co-localization with S100 immunostaining in uninjured nerves and co-localization with S100-positive Schwann cells post-injury. Similarly, putative down-regulated transcripts (SPARCL1) exhibited signal co-localized with S100 immunostaining prior to injury and were no longer detectable in immunolabeled Schwann cells after injury Fig. .For a subset of up- and down-regulated transcripts, we observed parallel trends in gene expression in S100-negative cells, in particular macrophages. Therefore, we wanted to further confirm that the transcriptomic changes we denoted originated in Schwann cells, and also differentiate immune responses due to Schwann cell expression of injury-related genes from those mediated by CD45-expressing myeloid cells. We sequenced CD45-positive myeloid cells that were depleted from our sciatic nerve cell suspension during CD45-based immunopanning post-injury. This data revealed that although CD45-positive macrophages exhibit occasional parallel trends in gene expression compared with purified Schwann cells, the level of the expression changes for these genes in this infiltrating population (absolute FPKM) was typically at least an order of magnitude less than in purified Schwann cells  in Schwann cells at each time post-crush stage. The number of significant differentially expressed genes (DEGs) was relatively consistent at each timepoint , although the composition of these genes differed throughout each stage. The full list of DEGs at each timepoint can be found in Additional file We next used volcano plots to visualize global changes in Schwann cell gene expression at each post-crush timepoint relative to the naïve Schwann cell transcriptome Fig. A–C   and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis of the top 2000 most upregulated and downregulated genes at each post-crush timepoint , each defined by a distinct pattern of gene expression over the examined timecourse Fig. A, B. TheOur WCGNA analysis also highlighted the temporal regulation of specific molecular pathways that are of interest for further study. The PI3K/Akt pathway, implicated in multiple studies involving the Schwann cell response to injury, Schwann cell proliferation, and in the initiation of Schwann cell myelination, was grouped into module 8, a cluster of genes for which expression increases sharply by day 3 after injury and then decreases in expression intensity by day 5 and further by day 7 after injury , 35, 47.The transformation of the Schwann cell in response to peripheral nerve injury has long been recognized as a key feature of the peripheral nerve’s remarkable ability to regenerate and yet the molecular underpinnings of this transformation have remained incompletely understood. Here, we provide an RNAseq dataset on Schwann cells acutely purified from rodent peripheral nerves at multiple timepoints during the 1st week after nerve crush to provide insight into the transcriptional changes underlying the Schwann cell response to injury.We used published immunopanning methods to purify Schwann cells away from other cell types in the nerve, including resident macrophages, hematogenous immune cells, and endoneurial fibroblasts at postnatal day 18. Several methodological points deserve brief discussion. First, O4 is highly expressed by all Schwann cells (both the myelinating and non-myelinating (Remak)) at rat postnatal day 18 . While iWhile recent single cell expression analyses contribute unprecedented granularity to our knowledge of Schwann cell gene expression, they lack the sequencing depth provided by bulk RNAseq to more accurately define and quantify the molecular changes occurring in Schwann cells after injury . Thus, both approaches are valuable tools for understanding the Schwann cell response to injury and should be viewed as complementary to one another. Our initial analysis of our bulk RNAseq results underlines several familiar themes of Schwann cell transdifferentiation, as well as potential avenues for future study. These are outlined below.There has long been consensus that Schwann cells distal to a site of peripheral nerve injury reorganize to form cellular columns known as Bungner’s bands. The importance of these cellular columns for successful nerve regeneration is also well-established, yet the mechanisms underlying the formation of these bands remains incompletely understood. Elegant studies using lineage tracing analysis have recently provided insight into the Schwann cell sub-type contributions and changes in Schwann cell structure that enable the formation of these columns . The molRecent work has highlighted that in parallel with macrophages, Schwann cells contribute to debris clearance within the injured peripheral nerve , 15. AccOur transcriptional profiling of the post-injury sciatic nerve also highlights extracellular matrix changes. On the side of infiltrating myeloid cells, we observed a strong upregulation of glycan breakdown enzymes after injury. Two Schwann cell transcripts, Sparcl1 and Sema5a, are among the most significantly downregulated genes within the first week after injury. Sparcl1, or Hevin, a secreted non-neuronal protein is implicated in synapse initiation and remodeling during development and after injury . Most woA complex interplay between the Mek/ERK1/2-MAPK and PI3K/Akt/mTOR pathways is suggested in Schwann cell myelination and myelin pathologies . A dual The AGE/RAGE pathway has to date been most heavily studied in age-related vascular dysfunction where it has been found to mediate age-related vascular stiffening. The accumulation of AGEs is known to be accelerated in diabetes , 38. AltThe ultimate value of findings in rodent models of nerve injury depends on their relevance to human disease. Data on the human Schwann cell response to injury are extremely scarce. Recent studies using degenerating nerve fascicles in vitro, however, demonstrate important parallels between rodent and human Schwann cells , 43. ImpA fascinating aspect of Schwann cell biology is the bifurcation of the Schwann cell lineage during development to form two distinct populations of mature Schwann cells, non-myelinating (Remak) cells and myelinating Schwann cells. Our understanding of the gene expression differences between Remak and myelinating Schwann cells has until very recently remained rudimentary, based on small handful of cell-subtype specific markers such as GFAP and myelin proteins . Using sThe transcriptomic data presented here provide a valuable resource to peripheral nerve biologists to fuel novel hypotheses and uncover new pathways underlying the Schwann cell injury response. In addition to the molecules highlighted above, we anticipate the discovery of to date un-appreciated pathways and proteins using our dataset that will enable mechanistic advances in our understanding of how these remarkable cells transform after nerve injury. Confirmation of our transcription-level changes at the protein level followed by knock-out or overexpression experiments has the potential to yield answers to outstanding questions in the field of Schwann cell biology. For example, to further our understanding of why the PNS regenerates so well, and to generate new molecular handles for both understanding CNS regeneration failure and designing strategies to overcome it.Additional file 1. PCA of all RNA-seq samples. (A) PCA of all samples colored by day post-crush. Circles indicate whole nerve samples and triangles represent Schwann cell purified samples. (B) PCA of Schwann cell samples alone, colored by time point.Additional file 2. Generation of in situ hybridization probes. RNA probes used for in situ hybridization validation. Expected probe lengths are listed beneath each column.Additional file 3. qRT-PCR primer sequences (rat) used to validate RNAseq findings.Additional file 4. All FPKM data for uncrushed (day 0) and crushed  samples. This includes whole nerve and purified Schwann cells. Also included is dissociated sciatic nerve to help identify genes that change from the dissociation process, itself.Additional file 5. All differentially expressed genes and their p-values (and p-adjusted) in Schwann cells at days 3, 5, and 7 post-crush, as compared to day 0 (uncrushed).Additional file 6. Table of enriched gene pathways in purified Schwann cells at days 3, 5, and 7. First two sheets show pathways across all timepoints and the subsequent sheets highlight statistics of each pathway separated by timepoints.Additional file 7. Table of downregulated gene pathways in purified Schwann cells at days 3, 5, and 7.Additional file 8. Module assignments (from the WGCNA) for each gene.Additional file 9. Analysis of markers of different S100-expressing cell populations.


Perivascular epithelioid cell tumors (PEComas) of the uterus is a rare type of mesenchymal tumors associated with myelomelanocytic differentiation and distinctive histological appearances. So far, the reported cases of uterine PEComas are usually benign. Documented malignant cases with aggressive behavior appear to be less common.We report a 37-year-old female who received abdominal hysterectomy for uterine tumor in a local hospital. She was diagnosed with uterine leiomyosarcoma and referred to Hubei Cancer Hospital. Her histological slides were reviewed and immunohistochemical staining for specific markers of epithelial, melanocytic, myoid and some others were analyzed. The pathologic diagnosis was malignant uterine PEComa. Systematic imaging of the patient further revealed an abdominal para-aortic mass. She received pelvic and para-aortic lymph node dissection. Postoperative histology revealed para-aortic lymph nodal metastasis of malignant uterine PEComa. She received 8 cycles of chemotherapy after surgery. The chemotherapy regiment was epirubicin plus ifosfamide The patient is free of recurrence and metastasis 6 years after surgical resection.Uterine PEComas are indistinguishable from other uterine tumors such as leiomyoma and leiomyosarcoma before pathologic diagnosis could be made. For patients with malignant uterine PEComas, removal of both primary lesions and metastatic foci, if any, needs to be attempted. Postoperative chemotherapy or radiotherapy should also be considered in patients with distant metastases or positive lymph nodes. The concept of perivascular epithelioid cell tumors (PEComas) was first proposed by Zamboni in 1996 to describe HMB-45-positive pancreatic tumors consisting of transparent cytoplasmic epithelial cells . PEComa PEComas may arise from various anatomic sites including the gynecological tract, in which the uterus is the most commonly affected organ. Fewer cases of gynecological PEComas are also reported in the cervix and even much rarer in the vagina, vulva, broad ligament, round ligament and ovary . During Currently, there is no standard treatment for neither primary nor recurrent and metastatic PEComa. Surgical resection is the first choice, and radical hysterectomy with bilateral salpingectomy is the best surgical method for the uterine PEComa .This article reports a case of malignant uterine PEComa with para-aortic lymph nodal metastasis in a 37-year-old woman.A 37-year-old female patient was found to have an 8 × 7 × 7 cm uterine tumor by ultrasonography during examination for heavy menstrual bleeding and prolonged bleeding in a local hospital. She was initially diagnosed with uterine leiomyoma and underwent an abdominal hysterectomy and bilateral salpingectomy on October 10, 2015. The pathological findings from the local hospital suggested a diagnosis of uterine leiomyosarcoma. The patient was then referred to Hubei Cancer Hospital on November 3, 2015.The pathological slides from the first surgery of the patient were reviewed with the Olympus BX-53 microscope. The pathological images were captured by the Olympus DP72 digital acquisition camera and no downstream processing was employed to enhance the resolution of the image. Microscopic examination revealed that the tumor was composed primarily of epithelioid cells and focal areas of spindle cells growing in a nested and alveolar pattern around small blood vessels Figs.  and 200×The tumor cells had abundant cytoplasm varying from clear to focally granular and eosinophilic, centrally located round or oval nuclei, and inconspicuous nucleoli. Infiltrative growth patterns and mitotic figures were easily identified, whereas calcification or lipomatous components were not evident Figs.  and 400×To further determine if there were local and/or distant metastases, we conducted a systematic imaging examination on the patient. The abdominal enhanced computed tomography (CT) scan revealed a solid, mixed-density mass of approximately 2.0 cm in diameter on the right side of the abdominal aorta which close to the right renal artery, indicating potential metastasis to the para-aortic lymph nodes Fig. . No obviTherefore on November 12, 2015, we performed the second surgery to dissect the bilateral pelvic lymph nodes and the abdominal aortic lymph nodes and bilateral ovary. Postoperative pathology showed metastasis to the abdominal aortic lymph nodes (3/11), whereas no metastatic lesions were found in the pelvic lymph nodes (0/34) and both ovary. Histologically, the para-aortic lymph nodal metastasis exhibited similar characteristics with the primary lesions described above, supporting the diagnosis of malignant uterine PEComa with para-aortic lymph nodal metastasis Fig.  and 40×.2,100–110 mg) on day 1 along with daily ifosfamide  from day 1 to day 3. Grade 1–2 drug-related toxic effects such as gastrointestinal reactions and myelosuppression were observed and closely monitored during the treatment. We also recommended everolimus, an mTOR inhibitor, to the patient for maintenance treatment after chemotherapy, but she failed to follow because of economic burden. Up to now, the patient has been free of recurrences or distant metastases for 6 years after surgery.The patient recovered smoothly and was discharged 18 days after the second surgery. Given the concerns of the large size of the tumor, the lymph nodal metastasis and other malignant features, the patient received 8 cycles of AI every three weeks starting from 1 week after the second surgery. The AI regiment prescribed was the intravenous administration of epirubicin (60 mg/mUterine PEComas were first documented in 2002 . IncreasTSC1) and/or TSC2 genes. The products of TSC1 and TSC2 gene form a heterodimer which negatively regulate mTOR pathway inhibiting cell proliferation and metabolism, and inactivation of these two genes results in increased cell growth and proliferation [The histologic and immunohistochemical features of PEComas suggest that these tumors might emerge from the vascular parietal structures even though neither a corresponding normal cellular counterpart nor a precursor lesion for PEComas has been identified , 7. Giveferation , 9. In uferation , 11.TSC1 and/or TSC2 mutations, a distinctive subset of PEComas which carry gene rearrangements involving transcription factor E3 (TFE3) translocation and fusion has also been identified [TFE3-rearranged PEComas are only a few and the significance of TFE3 rearrangements remains unclear. The clinicopathologic findings suggest that this subset of PEComas has a unique morphology and specific immunophenotype without TSC1/2 mutations [TFE3-rearranged PEComa has aggressive behavior and poor prognosis, further raising an concern regarding a potential association between TFE3 rearrangements and PEComas malignancy [Besides entified . Confirmutations . One doclignancy .Of interest, RAD51B-OPHN1 fusion, an even rarer gene rearrangement has been identified recently in 4 patients with uterine PEComas but not present at any other locations , 16. AltMoreover, the vast majority of PEComas have been described in females, with a female-male ratio as high as 9:1 noted in some case studies, suggesting a possible role of hormones in the pathogenesis of the disease. Indeed, increased expression of ER and PR has been shown in the patients with uterine PEComas –19. The Although most PEComas follow a benign course, malignant cases are being increasingly reported in different organs including the uterus. The original classification established by Folpe et al. categorizes PEComas as either benign , uncertain malignant potential (nuclear atypia/multinuclear giant cells or size > 5 cm), or malignant  . Later oTSC1 and/or TSC2 genes, targeted therapy using mTOR inhibitors seems to show encouraging results in a few studies [Due to the small number of reported cases and inconsistent results obtained from various therapeutic strategies, currently there is no consensus on the treatment of PEComas. Surgery is the main treatment of PEComas for primary lesions as well as for local recurrences and metastases. The extend of surgery should be individual regarding the health situation of the patients and the biological behavior of their tumors, with the aim to obtain tumor-free margins. Most PEComas appear to be curable with surgical resection alone, whereas the actual effect of adjuvant therapies including radio-, chemo-, and immunotherapy remains unclear. However, given the uncertain tumor biology of PEComas, postoperative radiotherapy and chemotherapy should be supplemented for patients with locally advanced or metastatic PEComas . Moreove studies –23. Neve studies . In our Notably in some cases, metastatic spread of PEComas may present many years after initial treatment , 25, whi


The 5-year survival rate for patients with oral squamous cell carcinomas (SCC), including tongue SCC, has not significantly improved over the last several decades. Oral potentially malignant disorders (OPMD), including oral dysplasias, are oral epithelial disorders that can develop into oral SCCs. To identify molecular characteristics that might predict conversion of OPMDs to SCCs and guide treatment plans, we performed global transcriptomic analysis of human tongue OPMD (n = 9) and tongue SCC (n = 11) samples with paired normal margin tissue from patients treated at Weill Cornell Medicine. Compared to margin tissue, SCCs showed more transcript changes than OPMDs. OPMDs and SCCs shared some altered transcripts, but these changes were generally greater in SCCs than OPMDs. Both OPMDs and SCCs showed altered signaling pathways related to cell migration, basement membrane disruption, and metastasis. We suggest that OPMDs are on the path toward malignant transformation. Based on patterns of gene expression, both OPMD and tongue SCC samples can be categorized into subclasses  similar to those seen in human head and neck SCC (HNSCC). These subclasses of OPMDs have the potential to be used to stratify patient prognoses and therapeutic options for tongue OPMDs. Lastly, we identified a gene set  whose transcript changes have the power to classify OPMDs and SCCs and developed a Firth logistic regression model using the changes in these transcripts relative to paired normal tissue to validate pathological diagnosis and potentially predict the likelihood of an OPMD developing into SCC, as data sets become available.The online version contains supplementary material available at 10.1007/s12672-023-00629-y. The American Cancer Society estimates 34,730 new oral cancer diagnoses in 2022, including 17,860 new tongue cancer cases within the United States . Oral poThe current clinical staging system of oral SCCs that determines the basic characteristics and prognosis for patients is based on assessments of primary tumors , regional lymph nodal metastases , and distant metastasis  . AlthougThere have been studies to identify markers to distinguish OPMD and malignant oral lesions , 14, 15,Because histopathological characteristics of OPMD do not always accurately predict the clinical outcomes of these lesions, we sought to develop a signature gene set in which transcript level changes in an individual patient with tongue OPMDs could be used to predict the risk of developing SCC. Comparative analysis of samples between OPMDs and SCC is a major challenge because of the variabilities among individual patient transcript changes in these lesions relative to normal tissue samples from the same patient . This vaIn this report we conducted genome-wide RNA-seq analyses of human tongue OPMDs and SCCs from individual patients compared to the normal healthy tongue epithelia in the same patient. We then developed a gene set that informs the risk of lesions advancing to SCCs. The results from this comparison provide novel molecular markers that may predict the risk of OPMDs becoming OSCCs and have the potential to improve early diagnosis and treatment decisions for OSCCs.Human patient tongue lesion samples and their corresponding margin samples (far away from the lesions) were surgically resected (See Supplementary Information).Histopathologic examination was performed for all lesions (See Supplementary Information).We prepared total RNA from the human margin, OPMD, and SCC samples. Subsequent steps for RNA-seq were carried out at the Genomics Resources Core Facility of WCMC (See Supplementary Information).We performed pathway and gene ontology analysis using Enrichr (See Supplementary Information).We carried out Pearson and Spearman correlation studies using GraphPad Prism software (See Supplementary Information).We built a logistic regression model to classify OPMD and SCC samples based on gene expression changes (See Supplementary Information).https://freshtuo.shinyapps.io/sccpred/ (See Supporting Information).The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database. A web application to predict the SCC risk of a tongue OPMD is available at We performed RNA-seq analysis on the OPMD and SCC samples and the corresponding margin  samples from the same patients. Our analysis revealed that 1439 transcript levels ) were altered significantly in the tongue OPMD samples (n = 9) compared to the corresponding margin samples (n = 9), including increases in 729 and decreases in 710 transcripts   shows that there were 146 transcripts that overlapped between SCC/margin and OPMD/margin (Table S2). These transcript overlaps were much more significant than expected by random chance, with a p-value = 3.245 e-73 using Fisher’s exact test . The log2 fold changes of these 146 transcripts are shown . Although 141 of these 146 transcripts showed same change directions in SCC/margin and OPMD/margin, with the average fold changes in SCC/margin greater than that in OPMD/margin , the transcript level changes of these 146 genes in individual patients exhibited variability . For the following studies below, we only used transcripts with fold changes of |Log2|> 1. To ascertain whether our patients’ lesions captured the characteristics of human oral SCC, we performed disease signature pathway analysis using “Disease Perturbations from GEO up/down” , a platfWe conducted a pathway enrichment analysis of the transcripts altered in the OPMD and SCC samples using the Enrichr web tool, based on the MsigDB (Molecular Signatures Database) . The topBy using the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis program we discovered that the top 15 pathways significantly enriched with transcripts increased in the SCC group included the cancer-related pathways “Cytokine-cytokine receptor interaction” and “ECM-receptor interaction,” while there were no cancer-related pathways with increased transcripts significantly (q < 0.05) enriched in the OPMD group . In the SCC group with transcripts decreased “Metabolism of xenobiotics by cytochrome P450” and “Fatty acid degradation” were among the top 8 pathways significantly (q < 0.05) enriched. The top pathways significantly (q < 0.05) enriched with transcripts decreased in the OPMD group, such as “Estrogen signaling pathway” and “Lipid and atherosclerosis,” showed no directly cancer-related pathways . Thus, our KEGG pathway analysis shows that cancer-related pathways are more highly activated in the SCC than in OPMD samples.e.g. increases in interferon responses and cytokine signaling  using the Gene Ontology (GO) Biological Process Database revealed that the top 15 pathways with transcripts increased in the SCC group included “Extracellular matrix organization GO:0030198)” and “Inflammatory response (GO:0006954)”, and the top pathways enriched with transcripts in the OPMD group showed “Skin development (GO:0043588)” and “Extracellular matrix organization (GO:0030198)” . Additionally, the top pathways with transcripts decreased in the SCC and OPMD groups included “Epidermis development (GO:0008544)” and “Cellular heat acclimation (GO:0070370)” . The KEGG and GO pathways enriched with transcripts increased in the OPMD and SCC samples suggest: (1) an increase in cell motility potential in SCC and OPMD [8” and “Iing Fig. B.A global heatmap analysis on the RNA-seq data, with each margin sample followed by the lesion sample from the same patient (OPMD or SCC)  demonstrated that there were differences in the basal gene expression in the margins in different patients. It was also clear that there were differences between the margin tissue and the OPMD or SCC samples. Furthermore, the changes in the average levels of individual transcripts in the tongue OPMD and SCC groups vs. the margin groups did not always reflect the changes in individual patients . These data indicate divergent gene expression in human tongue OPMD and SCC samples as well as variations in gene expression in normal tissues. Therefore, we next compared the changes in the transcriptomes in individual tongue lesion vs. margin tissue from the same patient. This comparison could mitigate the possible errors caused by comparing the group average of all margin samples with that of all lesion samples and reveal more consistent changes between OPMD and OSCC. We performed correlation studies (Spearman and Pearson) using the log2 fold changes of transcripts between tongue lesions (OPMD or SCC) vs. their margins in the same patients to examine the similarities in the entire transcriptome among patients. In the OPMD group, samples #5 and #2 were quite similar, and sample #9 showed no correlations with all other samples except a small similarity to #7 Fig. A, shown https://gdac.broadinstitute.org/). This study , IGSF10 (Immunoglobulin Superfamily Member 10), CRMP1 (Collapsin Response Mediator Protein 1), RPTN (Repetin), and HTR3A (5-Hydroxytryptamine Receptor 3A) (Table S6). ELF5 and IGSF10 mRNA levels are significantly decreased in human head and neck cancers , 37. CRMWe further tested all transcript combinations and their power to distinguish OPMD from SCC, out of which 6 combinations had the highest power (AUC = 1) with “Leave-One-Out” cross-validation (Table S7). The results for individual samples are shown  to validate the correlations between the changes of these 5 transcripts in SCC relative to margin. Neither CRMP1  nor IGSF10  were correlated with the other 4 markers. ELF5, HTR3A, and RPTN were weakly correlated with each other . Thus, changes in these transcripts can serve as independent predictors of outcome.Transcripts whose changes are well correlated are not considered independent predictors of outcome . We evalWe then built six SCC prediction models using the six transcript combinations (Table S7) on all samples to predict the risk of an individual patient’s tongue lesion to become SCC. The models are listed below:We finally chose the gene combination “RPTN, IGSF10” as a diagnostic signature to predict the probability of a OPMD becoming SCC because RPTN and IGSF10 were independent predictors of outcome and had statistically significant correlations with the sample groups (OPMD or SCC), with p-values equal to 1.4E-3 and 2.8E-3, respectively (Table S10). We then applied this model to all lesion samples and calculated their probabilities to become SCC  Get RNA-seq of the lesion vs. normal tissue nearby from biopsies; (B) Open the app, input the log2 fold changes in these two transcript levels ; then press “submit”; (C) See the SCC probability result (See an example in the Appendix). This logistic regression model shows proof of concept that the changes in the transcript levels (lesion vs. margin) has potential to verify pathological diagnosis and potentially predict the SCC risk of a tongue lesion. To validate this prediction, the algorithm should be tested on an independent data set, but currently we do not have such a dataset available.The logistic regression model not only distinguishes OPMD from SCC states and validate pathological diagnosis, but also has potential to predict the probability of a sample progressing towards SCC. Therefore, we propose the gene combinations “RPTN and IGSF10” as a diagnostic signature gene set because these transcript changes were independent predictors of outcome. We have prepared an app for physicians to validate pathological assessment and potentially predict SCC risk of a tongue OPMD: (1) Compared to the margin samples, OPMD samples show changes in much fewer transcripts compared to SCC samples; (2) The number of changed transcripts resembling human oral SCC molecular features in the OPMD samples is lower than that in the SCC samples. These data indicate that, as expected, OPMDs display early pathological changes that may lead to SCC development. The paired design allows us to remove variations among individuals so that we can focus on variations between OPMD/SCC and normal conditions.Tongue OPMDs are lesions with a high risk of progressing to SCC . Our geni.e., metastasis is initiated long before the primary tumor is well developed and diagnosed [Multiple overlapping pathway analyses suggest that EMT and cell migration start in OPMD and that higher signals in the SCC samples indicate that in EMT cell migration occurs more frequently. These data suggest that “quasi-normal” epithelial cells start to disseminate from pre-neoplastic lesions at an early carcinogenesis stage, such as OPMD. These results are in line with the “parallel progression model” of metastasis, iagnosed , 43. Notiagnosed , 45.We categorized these OPMD and SCC lesions into distinct classes and confirmed the heterogeneity among individual human tongue pre-cancerous and cancerous lesions. Importantly, to our knowledge this is the first report in which, based on the transcriptomic changes in OPMD/margin in individual patients, human OPMD samples are classified into different head and neck SCC subclasses . Previous studies , 46–48 sVarious prognostic molecular markers for OPMD, including p53, Ki67 and PCNA, cell cycle proteins, loss of heterozygosity (LOH), and some cell surface and stromal proteins, identified in prior studies, have failed to be used in clinical practice for OPMD prognosis , 49 becai.e., a tongue lesion versus the tongue margin tissue in the same patient. We think that this type of comparison is optimal for addressing the problem of variability in transcript changes among patients. Additionally, we screened candidate transcripts using the opposite directions of fold changes observed in OPMD and SCC groups. Therefore, our prediction model derived from the screened transcripts has the potential to distinguish OPMD from SCC and predict the risks of OPMDs progressing to SCCs. This will help physicians decide whether to surgically remove an OPMD.Here we focused on paired cases of OPMD and SCC, One limitation of our study is the relatively small sample size. Thus, validation with an independent data set is needed. Another point is that we have only analyzed mRNA, and not protein levels of these genes. In future work we plan to examine the levels of these proteins in OPMDs. However, we have shown proof of principle that changes in a set of transcripts between a tongue lesion and normal tongue epithelial tissue from the same patient have the potential to classify OPMDs and SCCs and provide insights to the probability of OPMDs becoming oral SCCs.We have built a Firth logistic regression model using the changes in these transcripts relative to paired normal tissue to validate pathological diagnosis and potentially predict the likelihood of an OPMD developing into SCC, as data sets become available.Supplementary file 1 (PDF 6828 KB)Supplementary file 2 (DOCX 33 KB)Supplementary file 3 (XLSX 7390 KB)Supplementary file 4 (XLSX 46 KB)Supplementary file 5 (XLSX 400 KB)Supplementary file 6 (XLSX 39 KB)Supplementary file 7 (XLSX 852 KB)Supplementary file 8 (XLSX 164 KB)Supplementary file 9 (XLSX 923 KB)Supplementary file 10 (XLSX 12 KB)Supplementary file 11 (XLSX 10 KB)Supplementary file 12 (XLSX 12 KB)


Human papillomavirus (HPV) negative cancers are associated with symptomatic detection, late-stage diagnosis, and worse prognosis. It is thus essential to investigate all possible infectious agents and biomarkers that could early identify these HPV negative cancers. We aimed to analyze and compare the metatranscriptome present in HPV positive and HPV negative cervical cancers. We analyzed the whole RNA sequencing files from 223 HPV negative cervical cancers , 223 HPV positive tumors and 11 blank paraffin block pools (used as controls) using Kraken2 software. Overall, 84 bacterial genera were detected, with 6/84 genera showing a positive median number of reads/sample and being present in both cervical tumor groups (HPV positive and negative). Viral reads belonged to 63 different viral genera, with 6/63 genera showing a positive median annotated read/sample value. No significant difference among genera was detected except for the presence of alpha-papillomaviruses. Metatranscriptome of bacteria and viruses present in HPV positive and HPV negative cervical cancers show no significant difference, except for HPV. Further studies are needed to early identify this biologically distinct group of cervical cancers. About 12 HPV types are nowadays classified as oncogenic, high-risk HPV genotypes, and persistent infection of these oncogenic HPV types is known to be a necessary cause for cervical cancer.Human papillomaviruses are a group of double-stranded DNA viruses which comprise up to 223 different types, with new types being continuously identified5. Cervical cancer is preventable, but it is still one of the most common cancers and causes of cancer-related death in women, worldwide.Although effective screening methods (cytology and HPV testing) exist and several effective prophylactic vaccines are available in the market to prevent cervical cancer, the annual number of new cases of cervical cancer is estimated to increase from 570,000 to 700,000 between 2018 and 2030, with the annual number of deaths rising from 311,000 to 400,0008. This percentage of HPV negative cancers may translate into women not being categorized correctly for the screening program and tumors being detected only when they had become symptomatic (very late). Of particular note, authors have shown that cervical cancer cases that appear to have lost HPV DNA seem to constitute a specific subgroup of cervical cancers with a different biological behavior (worse prognosis)10.Due to advances in molecular testing, the more sensitive method of HPV-based screening is now replacing cytology as the primary cervical cancer screening tool. This method translates into testing cervical samples for HPV DNA rather than morphological abnormalities and constitute a more automated and objective screening strategy. Women with a negative HPV test, have very low risk of subsequent cancer and may safely be re-called in three-seven years. Nevertheless, a significant proportion of cervical cancers (around 7%) appears to be HPV negative11.Efforts to find out and explain HPV negativity in cervical cancers have led scientists to reanalyze smears and retest these HPV negative tumors by using different approaches. Main explanations for HPV negativity have been sample inadequacy, false diagnosis, presence of cancers associated with types that were not targeted in the studies, and false negative results due to low sensitivity of the HPV detection methods13. However, these methods are biased to detect mostly known HPVs that bind specifically to designed primers and probes. Unknown HPV types, known HPV types that are distantly related as well as targeted HPV types presenting variations within probe/primer regions may escape amplification or hybridization17. To overcome this bottleneck, it is nowadays possible to perform unbiased metagenomic sequencing (not based on PCR) and detect all HPVs present in a sample, without prior knowledge on which types might be present19. Furthermore, if cDNA is sequenced, the data will reveal if there is viral transcriptional activity, essential for both initiation and maintenance of the malignant phenotype.Most HPV screening methods are focused on detecting high-risk HPV types and are based on PCR amplification of conserved regions in the L1 gene by consensus primers, followed by HPV genotyping identification by hybridization of amplicons to type-specific probesFollowing the three recommendations mentioned above, cervical tumors classified as HPV negative after (a) assuring sample adequacy, (b) reanalysis of corresponding slides/smears to confirm tumor diagnosis and (c) not revealing presence of HPV after performing an unbiased sequencing analysis, may be called truly HPV negative. Understanding the biology of truly HPV negative cervical cancers is urgently needed in the era of cervical cancer elimination.20. On the other hand, it is possible that another infectious agent may play a role in this specific subgroup of HPV negative cancers. Taking into consideration both possibilities, a search for an infectious cause/biomarker should focus on unbiased RNA sequencing of truly HPV negative cancers. We aimed to analyze and compare the metatranscriptome of HPV positive cervical cancers with the metatranscriptome of HPV negative cancers and assess whether there are differences that could act as biomarkers to early identify these HPV negative cancers.Cervical cancer is known to have an infection as cause, being HPV the primary predisposing factor. Truly HPV negative cervical cancers could have initially been caused by HPV but lost the virus as the tumor progressed. There is a very high proportion of cervical intraepithelial neoplasias (CIN) 3 that contains HPV and there are cases of HPV negative cancers that have been HPV positive in the earlier screening appointmentsE6/E7 genes instead of L1) was performed. Repeatedly negative samples decreased to 394/2850 . We then subjected all HPV negative carcinomas  to an unbiased RNA sequencing using Novaseq 6000 (Illumina Platform), generating high quality sequencing data with a median of 30 million reads per sample. RNA positivity was detected in 169/392, decreasing the percentage of cervical cancers negative for HPV from 18.5% to 7.8% (223/2850)15.In a previous study, we systematically genotyped all cervical cancer cases occurring in Sweden from 2002–2011 (n = 2850), and for those that were HPV negative , sample adequacy and correct diagnosis were analyzed and reanalyzed, respectively. After excluding samples that were not adequate (beta-globin negativity) and those whose diagnosis was not confirmed, a different PCR approach . A total of 11 blank paraffin blocks pools were used as negative controls. While sectioning each cervical tumor formalin-fixed paraffin-embedded (FFPE) block, blank paraffin blocks had been sectioned in between as control for contamination. These paraffin blocks were extracted in the same manner as the cervical tumors, pooled (each pool containing about 45 blank blocks) and sequenced together with the corresponding tumor blocks.21, which was run against a reference database containing all RefSeq bacterial and viral genomes (built in December 2020) with a 0.1 confidence threshold. A cut-off of 10 classified unique reads was used to discriminate positive genera for bacteria and viruses, and results reported all genera which comprised more than 1% of total bacterial or viral reads, respectively.High-quality non-human reads were classified using Kraken2 v. 2.1.121, with a 0.1 confidence threshold. A cut-off of 10 classified unique reads was used to discriminate positive species. Reads that corresponded to HPV genomes were subjected to visual inspection using Integrative Genomics Viewer to confirm mapping.As HPV was the key difference between the cervical tumors (and the only significant difference detected between the 2 groups), we also queried all high-quality non-human reads to a reference database containing all human papillomavirus nucleotide sequences deposited in GenBank until July 8th 2022  using Kraken2 v. 2.1.1Differences in bacterial/viral presence across the two groups (HPV positive and HPV negative cancers) were evaluated by comparing median unique reads with nonparametric Wilcoxon rank-sum test, and relative proportions of bacteria/viral communities among the HPV positive and HPV negative cervical cancers were analyzed using a two proportion Z-test and its associated p-value. Bonferroni correction was applied considering a 0.05 error rate  and statistical significance was then obtained if p < 0.0002.Ethical approval was granted by the Regional Ethical Review Board of Stockholm, Sweden (EPN-Dnr: 2012/1028/32). The Regional Ethical Review Board determined that, due to the population-based nature of the study, informed consent from study participants was not required (EPN-Dnr: 2011/1026–31/4). All methods were performed in accordance with the relevant guidelines and regulations.15. A total of 223 HPV negative cervical cancers were reported. The present study aimed to compare the metatranscriptome present in these 223 HPV negative cervical cancer specimens with the corresponding metatranscriptome present in 223 HPV positive cervical tumors.In a previous study, all cervical cancers diagnosed from 2002 to 2011 in the whole Sweden were collected and genotyped. HPV negativity was determined after sample adequacy confirmation, diagnose reanalysis and confirmation of cervical cancer, and not detecting HPV after both PCR and unbiased RNA sequencingThe median age at cancer diagnosis of the HPV negative cervical cases (n = 223) was 68 years (range 30–93 years) and the median age at cancer diagnosis of the HPV positive cervical cases (n = 223) was 56 years (range 24–95 years).The metatranscriptome analysis from RNA sequencing had a total number of 63.80 M annotated bacterial reads for the HPV positive specimens and 62.81 M annotated bacterial reads for the HPV negative specimens.A total of 84 different bacterial genera showing at least 10 reads and 1% of total bacterial annotated reads were detected within the 223 HPV positive and 223 HPV negative cervical tumors. Only 6/84 genera showed a positive median number of reads/sample for both specimen groups (HPV positive and HPV negative cancers) , Staphylococcus , Pasteurella , Burkholderia (5633 and 5643 reads), Paracoccus (4817 and 4955 median reads) and Bacillus (2898 and 3260 reads) were the genera detected in HPV positive and HPV negative specimens, respectively. All 6 genera were present in at least 87.89% of specimens. No statistical difference (p < 0.001) in median read level nor in proportion was detected when comparing any bacteria genera within HPV positive and HPV negative specimens , and the corresponding proportions were similar. From higher to lower number of median annotated reads/sample: Klebsiella, Staphylococcus, Pasteurella and Paracoccus being identified in 1/11 blank controls and Burkholderia and Bacillus in 5 and 3 blank controls, respectively.Analysis of controls (blank paraffin specimens) showed a total of 31 different genera with 28/31 genera being also detected in cervical cancer specimens. All 6 genera that showed positive median read values in cervical tumors, were also present in at least one of the blank controls, with The metatranscriptome analysis from RNA sequencing had a total number of 613,606 annotated viral reads for the HPV positive specimens and 575,734 annotated viral reads for the HPV negative specimens.Overall, 63 different viral genera were detected when analyzing the RNA metatranscriptome within HPV positive and HPV negative tumors, with 6/28 genera showing a positive median annotated read/sample value  was only detected for the Alphapapillomavirus genus . Alphapapillomavirus genus was detected in 142/223 HPV positive specimens and in 3/223 HPV negative specimens according to established cutoffs and reporting parameters .Three out of these 6 generaFurther analysis of HPV presence was performed by subjecting high-quality non-human reads to a more “complete” database containing thousands of human papillomavirus sequences deposited in GenBank using Kraken. The analysis revealed HPV positivity (considered when samples showed more than 10 reads/species) in all 223 HPV positive cervical tumors, 7/10 controls blank pools and 30/223 HPV negative cervical tumors. Visual inspection of reads using Integrative Genomics Viewer showed that the blank controls and HPV negative cervical tumors that turned out to be HPV positive with this analysis did in fact show more than 10 reads, but reads were very short (< 50 bp) and viral coverage was below 10%. All 223 HPV positive cervical tumors showed more than 10 reads and HPV coverage was above 10% for all specimens.Locarnavirus, Chlorovirus, Gemycircularvirus, Orthobunyavirus and Betabaculovirus, were present in 6/11, 9/11, 9/11, 10/11 and 11/11 blank pools, respectively.Analysis of controls (blank paraffin specimen pools) revealed presence of 74 different viral genera, with 30/74 genera being also present in HPV negative and/or HPV positive cervical cancer samples. Most of the genera (25/30) were present in less than half of the blank controls, while Cervical HPV negative cancers exist and have unique properties such as late stage diagnosed cancers with poor prognosis. In this paper, we compared the metatrascriptome from 223 HPV negative cervical cancers with the metatranscriptome corresponding to 223 HPV positive cancers to inspect if there was any difference between them.L1, E6/E7 as well as unbiased RNA sequencing. Furthermore, blank paraffin controls were used to assess possible contamination as well as presence of environmental communities, and Bonferroni correction was applied to prevent data from incorrectly appearing to be statistically significant (a common event when performing multiple comparisons).Strengths of this study include the use of HPV negative cancers whose diagnosis, sample adequacy and HPV result had been confirmed and analyzed with different methods in order to dismiss false negativity. FFPE specimens were reanalyzed by an expert pathologist and confirmed to indeed contain invasive cervical cancer tissue. Beta-globin was present in all specimens included in the study and HPV detection was performed by using PCR targeting 22. Literature agrees on reporting that “healthy” women show a vaginal microbiome with low diversity, dominated by Lactobacillus, and that one of the most prominent features of “disease” is an increase of pH (due to a decrease of lactate concentration), reduction of lactobacilli and a great diversity of bacterial vaginosis-related bacteria, which are primarily anaerobic bacteria23. Studies on HPV positive CIN and cervical cancers show higher diversity in vaginal microbiota, with depletion of Lactobacillus crispatus and increase abundance of anaerobic bacteria25. While our results from cervical cancer cases agree with the literature , microbiome differences between HPV negative and HPV positive cervical cancers have not been studied yet. No significant difference among present genera was detected except for the presence of alpha-papillomaviruses, which is in line with the fact that HPV is the only reported infectious agent so far associated with cervical cancer, supporting the hypothesis that loss of HPV may occur as the tumor progresses and may be the reason for the occurrence of such cancer which is almost 7% of all cervical cancers diagnosed. A weakness from the present study may comprise not having previous screening specimens from the corresponding women to see if HPV presence was detectable prior cancer diagnosis.Bacterial and viral communities have been already analyzed and compared between health and disease, specially thanks to the effort of the Human Microbiome Project (HMP), the first large study to address the diversity of microorganisms present in the different organs of the human bodygammapapillomavirus) and 3/223 HPV negative cervical cancers when querying sequencing reads to the RefSeq database. Genera were reported when they presented more than 10 reads and comprised at least 1% of total bacterial/viral reads/sample. A further investigation of HPV presence using a broader HPV database identified HPV in all HPV positive tumors, as well as in 7/10 controls blank pools and 30/223 HPV negative cervical tumors. Visual inspection of these reads revealed questionable calling for both the blank pools as well as the 30/223 HPV negative cervical tumors, where the viral coverage was below 10%.Human papillomaviruses were initially reported in 143/223 HPV positive cervical cancers  as well as the genome coverage to achieve accurate identification (e.g. at least 10%). Accepting number of reads/k-mers as only parameter is not enough, as artefacts/background/noise may produce false positivity27.There is no consensus about which cut-offs to use for HPV “calling” when performing sequencing analysis and there is an urgent need to establish thoseAlphapapillomavirus). Further studies are needed to possibly find differences and/or biomarkers to early identify this biologically distinct subgroup of HPV negative cervical cancers. As the study is clear regarding that the metatranscriptome does not differ much between HPV negative and HPV positive cancers, we suggest that it may be more rewarding to look for differences in the human genome and transcriptome between the HPV positive and HPV negative cervical cancers.In conclusion, the present study reports the metatransciptome analysis of both HPV negative and HPV positive cervical tumors and does not detect any statistical difference in bacterial/viral communities´ expression when comparing HPV positive and HPV negative cervical cancers (except for


Scientific Reports, 10.1038/s41598-021-94413-z, published online 20 July 2021Correction to: In the original version of this Article, the Supplementary Information 2 and 3 were omitted.The Supplementary Information files now accompany the original Article.As a result, the Data availability section was revised.“The datasets generated and analyzed during the current study are publicly available and shared along the article submission. First name initials of study participants were coded numerically to anonymize them.”now reads:“The datasets generated and analyzed during the current study are publicly available and shared along the article submission (Supplementary Information 2 and 3). Personal identifying variables were removed to protect anonymity of study participants. The capture recapture codes rely on these variables and therefore won’t work but are made publicly available for transparency. Feel free to reach out to the corresponding authors for additional information.”The original Article has been corrected.


To demonstrate and validate the improvement of current risk stratification for bronchopulmonary dysplasia (BPD) early after birth by plasma protein markers , basal cell adhesion molecule (BCAM), angiopoietin-like 3 protein (ANGPTL-3)) in extremely premature infants.n = 52 preterm infants <32 weeks gestational age (GA) on two proteomic platforms  confirmed three biomarkers with significant predictive power: BCAM, SIGLEC-14, and ANGPTL-3. We demonstrate high sensitivity (0.92) and specificity (0.86) under consideration of GA, show the proteins’ critical contribution to the predictive power of known clinical risk factors, e.g., birth weight and GA, and predicted the duration of mechanical ventilation, oxygen supplementation, as well as neonatal intensive care stay. We confirmed significant predictive power for BPD cases when switching to a clinically applicable method (enzyme-linked immunosorbent assay) in an independent sample set  and demonstrated disease specificity in different cohorts of neonatal and adult lung disease.Proteome screening in first-week-of-life plasma samples of While successfully addressing typical challenges of clinical biomarker studies, we demonstrated the potential of BCAM, SIGLEC-14, and ANGPTL-3 to inform future clinical decision making in the preterm infant at risk for BPD.https://www.drks.de.Deutsches Register Klinische Studien (DRKS) No. 00004600; The urgent need for biomarkers that enable early decision making and personalized monitoring strategies in preterm infants with BPD is challenged by targeted marker analyses, cohort size, and disease heterogeneity.We demonstrate the potential of the plasma proteins BCAM, SIGLEC-14, and ANGPTL-3 to identify infants with BPD early after birth while improving the predictive power of clinical variables, confirming the robustness toward proteome assays and proving disease specificity.Our comprehensive analysis enables a phase-III clinical trial that allows full implementation of the biomarkers into clinical routine to enable early risk stratification in preterms with BPD. As of now, physicians have to rely on the diagnosis at 36 weeks gestational age (GA), solely based on clinical criteria.In order to now evaluate the potential of these proteins to serve as biomarkers in clinical routine as early as in the first week of life and thereby improve current risk stratification for BPD, we designed a study approach validating the biomarkers’ expression in a larger patient cohort as well as an independent sample set while rigorously assessing different, clinically relevant performance criteria. These included the evaluation of the biomarkers’ added value for disease detection in comparison to the sole use of clinical risk factors, the transfer of the measurement technique to a clinically applicable assay as well as the assessment of the biomarkers’ disease specificity by the use of neonatal and adult cohorts suffering from CLD of different origin. The comprehensive analysis was designed to enable a phase II clinical trial for implementation of the identified biomarkers into routine care for preterm infants.Sample sets analyzed comprise a training (preterm training cohort) and a validation  cohort of preterm infants, as well as additional samples from a small group of preterm infants recruited at a different study site. Disease specificity was addressed in a cohort of neonatal CLD (CLD-CDH cohort) and a cohort of adult CLD patients (adult CLD cohort).n = 55, median GA 27.2 weeks, range: 23.2–30.6; 45.5% males). All infants were born at the Perinatal Center in Munich and prospectively enrolled into the AIRR study (Attention to Infants @ Respiratory Risks) after written informed parental consent. The approval was assigned by the Ethics Committee of the Medical Faculty of Ludwig-Maximilians University in Munich . The study was registered at the German Registry for Clinical Trials . Preterm infants were prospectively included following the in- and exclusion criteria published previously:6 inclusion of preterm infants born <32 weeks GA with the exception of severe congenital malformations ), chromosomal abnormalities , inborn errors of metabolism, and decision for palliative therapy directly after birth). Clinical variables were comprehensively monitored from birth to discharge  or signs of maternal and fetal signs of infection;8 presence of early postnatal systemic infections (early-onset infection (eoi)): one or more clinical and laboratory signs of infection according to Sherman et al.9We analyzed a total of 55 plasma samples obtained in the first week of life  from preterm infants born <32 weeks GA  for at least 28 days, followed by a final assessment at 36 weeks postmenstrual age (PMA) or at discharge, whichever came first in preterm infants born <32 weeks GA. Disease grading accordingly assigned infants to having mild BPD  or moderate BPD (oxygen supplementation <FiO2 0.30 at 36 weeks PMA), and severe BPD (oxygen supplementation >FiO2 0.30 at 36 weeks PMA and/or positive pressure ventilation/continuous positive pressure) with each treatment referring to its continuous application and oxygen supplementation >12 h equaling one day of treatment.1 The infants’ oxygen saturation was assessed by standardized pulse oximetry. No infant was discharged from hospital before 36 weeks’ gestation.BPD was defined according to the NICHD/NHLBI/ORD workshopn = 25, median GA 27.0 weeks, range 23.6–31.0; 52% males). In this cohort, n = 10 infants did not develop BPD (no BPD (40%)), n = 5 infants developed mild BPD (20%), and a total of 10 infants were diagnosed with either moderate BPD (n = 2 (8%)) or severe BPD (n = 8 (32%)) with no infant being discharged before 36 weeks’ gestation  by the use of commercially available enzyme-linked immunosorbent assay (ELISA). Preterm infants were included following the same in- and exclusion criteria as outlined above , 75% males, n = 4 no BPD, n = 4 moderate/severe BPD). The approval was assigned by the Ethics Committee of the University of Schleswig-Holstein .In an additional step, a small group of preterm infants was recruited after written informed parental consent at a different study site following the same in- and exclusion criteria in order to mimic sampling conditions of a clinical trial, i.e., ongoing recruitment of small sample sets with random distribution of clinical characteristics; total number of patients: 1 The infants were part of the VICI-trial10 and prospectively included after informed parental consent at the ErasmusMC Sophia Children’s Hospital in Rotterdam. The approval was assigned by the Ethics Committee of the University of Rotterdam, the Netherlands . The cohort included 21 neonates in total with a median GA 38.0 weeks (range 33.6–41.3), 33.3% males. Six infants did not develop CLD (no CLD-CDH) and nine infants developed CLD (CLD-CDH (survivors)). Six infants deceased (CLD-CDH (deceased)) , 76.7% males), chronic obstructive pulmonary disease , 58% males), and subjects free of lung disease according to clinical history from the KORA cohort11 , 52% males). KORA  is a regional research platform for population-based surveys and subsequent follow-up studies with a focus on diabetes, cardiovascular, and lung diseases, including the impact of environmental factors.Disease specificity of the biomarkers was furthermore assessed in a cohort of adult CLD patients after informed consent ) comprising samples from patients with idiopathic pulmonary fibrosis  obtained during routine laboratory blood drawings were collected using ethylenediaminetetraacetic acid neonatal collection tubes. After pseudonymization samples were processed for proteomic screening by centrifugation  before supernatants were aliquoted and stored at −80 °C. Time from sample collection to sample processing was standardized according to the study protocol (DRKS No. 00004600).Ⓡ assays: 1st batch n = 16, 2nd batch n = 20; Proximity Extension Assay (PEA): n = 19; collected at day 4 of life (median), range: 0–7) followed by validation in a sample set of independent patients  that used a clinically applicable analysis technique, i.e., ELISA. Samples from an additional group of preterm infants (n = 8) were analyzed by PEA. Analysis for disease specificity comprised samples from cohorts with neonatal and adult CLD of different origin, i.e., n = 21 neonates in the CLD-CDH cohort (PEA (Olink-Proteomics®), n = 21) and n = 72 in the adult CLD cohort (SOMAscan® assay (SomaLogic®)).Samples from the preterm training cohort were analyzed on two analysis platforms in three subsequent batches  uses 1129 individual high-affinity molecules (SOMAmer®—slow off-rate modified DNA aptamer—reagents) quantified on a custom Agilent hybridization array.12 The PEA  employs a matched pair of antibodies linked to unique oligonucleotides detected in multiplexed fashion in a high throughput fluidic chip system measuring 630 unique proteins.13 Both techniques are designed for the accurate quantification of human plasma proteins present in concentrations below picogram per milliliter using even low-amount samples. For PEA measurements, SIGLEC5/14 was detected in the identical aliquot by ELISA .The SOMAscann = 25 infants  in an independent sample set obtained from 2-transformed, and pareto scaled data) and removed from further analysis and summary statistics. Preterm training cohort: protein expression obtained from two different sample sets analyzed by SOMAscan® (SomaLogic®)  and one sample set analyzed by PEA (Olink-Proteomics®) (n = 19) were batch corrected using the combat function from the sva package (version 3.36) in R , n = 21) and n = 72 in the adult CLD cohort (SOMAscan® assay (SomaLogic®)). Area under the curve (AUCs) were calculated using a leave-one-out cross-validation with a generalized linear model that compared no BPD infants to (a) only moderate and severe BPD cases or (b) all BPD cases as binary outcome and log2 transformed, pareto scaled protein expression data together with the clinical variables GA, birth weight, eoi, and sex as covariates, which are the most important risk factors associated with BPD development.14 These variables were analyzed with and without protein expression levels as covariates in a total of 32 different combinations including a null model. The model with the highest AUC and lowest Akaike’s Information Criterion (AIC) was the one used to perform the prediction of BPD and the continuous variables of duration of mechanical ventilation [days], oxygen supplementation [days], and neonatal intensive care unit (NICU) duration [days]. All prediction models for the preterm training cohort were calculated using a leave-1-out cross-validation with a linear regression model using the covariates from the best model comprising protein expression levels and GA. The models applied for the CLD-CDH cohort and adult CLD cohort, i.e., IPF and COPD, were corrected for GA (neonates) or age (adult patients). Three tests ) were used to check for equal variances before ANOVA testing. All protein concentrations were log2 transformed and pareto scaled prior to statistical analysis.Three outliers were detected by principal component analysis , n = 15 mild BPD (28.9%), n = 5 moderate BPD (9.6%) and n = 8 severe BPD (15.4%)) and 25 samples in the preterm validation cohort  for the separation of moderate and severe BPD cases from no BPD while explaining the data only by the most important variables (lowest AIC). We show that the combination of the three protein markers together with GA (BPD~BCAM+SIGLEC-14+ANGPTL-3+GA) best predicted BPD in the first week of life with an optimized AUC (0.87) and AIC (23.40), thereby being superior to the other models tested, e.g., “GA alone” , and the null model . These results were confirmed when restricting the analysis to very immature infants, i.e., <28 weeks GA, again demonstrating superiority with increased AUC (0.86) and decreased AIC (10.0) when compared to other models tested  when compared to the model with GA alone (51.0) and the null model ; no BPD vs. mild BPD ; no BPD vs. moderate and severe BPD , and oxygen supplementation  as well as the duration of NICU stay [ showed no discrimination between neonates with CDH and age-matched infants with CLD-CDH when analyzing survivors  and good power to separate cases in the CLD-CDH cohort (no CLD-CDH vs. CLD-CDH (survivors): 60%; CLD-CDH  vs. no CLD-CDH: 72%).14 The diagnostic gap with regard to timeliness and accuracy is reflected by the limitations of clinical trials aiming at the implementation of new therapeutic strategies19 and underscores the need for new markers enabling today’s clinicians to early and sensitively identify CLD in the preterm infant.BPD is a multifactorial disease and remains the most serious lung condition in neonates born premature due to its significant mortality and morbidity. Despite the clinical significance, the diagnostic process solely relies on clinical criteria assessed at 36 weeks PMA. The relatively late diagnosis only inadequately addresses both the need for early risk stratification as well as the diseases’ multifaceted pathology that includes the rarefication of the gas exchange area, interstitial remodeling, and airway pathology, now clustered at a late stage in a non-discriminatory diagnosis.6 Based on these findings, we first applied a generalized linear model to identify the best combination of proteins and clinical risk factors for BPD prediction and demonstrated the significant impact of the protein expression levels on improving BPD prediction when compared to clinical markers only. Second, we addressed the markers’ ability to successfully predict BPD grades as well as the duration of mechanical ventilation, oxygen supplementation, and intensive care treatment. Third, we successfully validated the biomarker results in an independent cohort of preterm infants using a clinically applicable measurement technique, i.e., ELISA and in a final step defined disease specificity in neonatal and adult patients suffering from CLD of different origins.We therefore followed a rigorous approach to evaluate protein markers with significant potential to serve as future biomarkers for BPD prediction as early as in the first week of life, previously identified by us using unbiased proteome screening.With the assessment of critical factors that can affect the biomarkers’ clinical performance including their validation in different cohorts and their resilience toward different measurement techniques, we overcame significant limitations of previous studies.14 Nonetheless, the model demonstrates good sensitivity and specificity to identify all BPD cases. Confirmation of the results in infants <28 weeks GA further supports the clinical value of the model by demonstrating superiority in a subcohort of very immature infants, in which GA alone is assumed to predict BPD development with significant power.21The assessment of 32 models identified the combination of GA together with the protein expression levels as the best model for BPD prediction while demonstrating the reproducible impact of the proteins on disease stratification when compared to known clinical covariates. The comparison between infants with higher disease grades to infants that did not develop BPD unequivocally identifies infants at risk, whereas the comparison of all BPD cases to patients without BPD is limited by the clinical heterogeneity of cases with mild BPD.The demonstration of the model’s potential to separate infants with different BPD grades from infants without the disease in the first week of life, and the validation of the results in an independent sample set with a clinically applicable protein measurement technique, i.e., ELISA, not only underscores the independence of the results from the measurement technique applied but adds significant insight into the performance of the potential biomarkers that is unmet by previous studies.22 low GA or birth weight, male sex,23 RDS, and invasive mechanical ventilation,24 sepsis, asphyxia, and chorioamnionitis,25 as well as race or ethnicity,27 and mode of delivery29 show only moderate predictive value.21 The limitations might be enhanced by significant changes in the diagnostic or therapeutic process applied to very low birth weight infants over time.31Reviewing and discussing the identification of reliable markers to predict BPD by previous studies, the use of clinical disease indicators including intrauterine growth restriction,3 Here, even the development of multivariate logistic regression models for the outcome of BPD or death at PMA of 36 weeks using protein expression levels of 25 cytokines as suggested by the Neonatal Research Network of the National Institute of Child Health and Human Development (NICHD NRN)32 did not improve disease prediction significantly, potentially due to the use of a pre-selected set of markers as opposed to their identification by unbiased screening. At the same time, markers derived from metabolomic analysis that showcased a cluster of 53 interesting metabolites associated with BPD development33 are most likely limited by the markers sensitivity toward sample collection and processing as well as the analytical platform used. Furthermore, marker detection in tracheal aspirates requires intratracheal intubation for sample acquisition, which becomes rarer with current postnatal treatment strategies.34 Despite significantly informing other fields of lung disease36 genetic BPD markers, although informing pathophysiologic understanding,38 have not been shown to significantly contribute to risk stratification until now.42 In contrast, the use of miRNAs already shows promising potential for BPD diagnosis and treatment.44Addressing the need for additional markers, a variety of studies aimed at identifying protein-based biomarkers, with the majority of studies centered around the detection of inflammation.® for the identification of biomarkers that enable the detection of vascular disease in the preterm infants.45 The approach succeeded showing early postnatal changes in circulating angiogenic peptides in association with disease, only limited by the lack of a validation cohort.In conclusion, the use of protein-based biomarkers may thus be today’s method of choice when generated by unbiased screening such as in the study by Arjaans et al. that used the SOMAmer technology47 has been previously associated with invasive infections in human newborns48 and the host response to viral airway infections,49 it holds promising potential to reflect the degree of pulmonary inflammation characterizing the BPD lung.53 The laminin receptor BCAM likely mirrors the process of tissue remodeling and its associated cellular cross-talk,54 thereby potentially reflecting BPD “activity”.55 In line with the findings of Arjaans et al.,45 ANGPTL-3 also is associated with angiogenic signaling, playing a role in endothelial development and survival, whereas BCAM holds functions in the basal membrane thereby reflecting significant changes to the surrounding niche of the developing alveoli.Likely, the reflection of the three most important processes of BPD pathophysiology by the three proteins supports their strength for early risk stratification: As the presence of the immune-activating SIGLEC-146 supports their potential as indicators of BPD pathophysiology and demonstrates that the circulating proteins largely originate from lung tissue in contrast to other studies.45Pulmonary expression of the three markers demonstrated in our previous study by the use of paraformaldehyde tissue sections from autopsy preterm BPD lungsIn order to further add to previous studies and to showcase clinical applicability of the biomarker, we simulated the circumstances of a clinical trial by the use of a small dataset characterized by random differences in the patient’s characteristics and successfully demonstrated the fit of the data obtained in the distribution of the expression levels of the larger sample sets. Furthermore, we showed disease specificity of the biomarkers for BPD prediction by their application in a neonatal (CLD-CDH cohort) and adult CLD cohort (adult CLD cohort). Here, the proteins in combination with the confounder GA did not allow the stratification of CDH neonates that developed CLD but only separated infants with fatal outcome, whereas adult patients suffering from emphysema (COPD) or lung fibrosis (IPF) could not be separated from pulmonary healthy individuals at all when using the biomarker combination.® or the PEA assay allows for the extensive study of a large number of peptides, peptides playing a role in the pathogenesis of BPD, however, might be missed. These candidates, when identified and validated by other studies, could be added to the model for further improvement.While successfully demonstrating the clinical value of the three biomarkers, limitations of our study include (i) cohort size, which we, however, consider adequate for a study in ELGAN’s (extremely low GA newborns) and (ii) the fact that despite the prospective study design, patients cannot be considered randomly assigned to disease groups. To partially compensate for these limitations, we underscored clinical relevance when demonstrating the proteins’ ability to predict main risk factors associated with BPD, i.e., need for mechanical ventilation, oxygen supplementation and NICU stay in the context of GA, as well as by their validation in an independent sample set, resulting in comparable results despite differences in cohort characteristics Table . The lim56 The currently prepared clinical trial aims to prove the biomarkers’ benefit for guiding clinical care. Future studies will have to address the potential of the biomarkers to inform disease monitoring, supported by our previous findings demonstrating stable protein expression levels at day 28 of life6 or to identify disease subtypes dominated by inflammation, matrix remodeling, or vascular pathology.58In conclusion, we demonstrated the promising potential of the identified proteins to inform clinical decision making while considering critical clinical variables. The study significantly adds to previous biomarker studies in the field as it addresses disease specificity and the biomarkers’ robustness toward changes in cohort characteristics or measurement technique.Supplementary tableSupplementary information figure


DIS3 events to have more differentially spliced events than those without. Splicing levels were correlated with expression of splicing factors. Moreover, the non-homologous end joining pathway was an independent factor that was highly associated with splicing frequency as well as an increased number of structural variants. We therefore identify an axis of high-risk disease encompassing expression of the non-homologous end joining pathway, increase structural variants, and increased alternative splicing that are linked together. This indicates a joint pathogenic role for DNA damage response and alternative RNA processing in myeloma.Alternative splicing plays a pivotal role in tumorigenesis and proliferation. However, its pattern and pathogenic role has not been systematically analyzed in multiple myeloma or its subtypes. Alternative splicing profiles for 598 newly diagnosed myeloma patients with comprehensive genomic annotation identified primary translocations, 1q amplification, and  KRAS (21%), NRAS (19%), BRAF (7%), DIS3 (9%), TENT5C (6%), and TP53 (3%) are amongst the most frequently mutated genes, some of which contribute to high-risk disease. Clinical factors including the International Staging System (ISS) . Ho. HoTP53 TP53 and TENT5C, increased activity in the NHEJ pathway, which in turn affects spliceosome component expression and AS of RNA transcripts.Here we bring together the association of DNA instability, an increased number of SVs, enrichment of abnormalities in DIS3 comparison offered the highest number (N = 481), followed by t (N = 218) and t (N = 120) in univariate analysis for PFS while DIS3 mutation reflected the highest number (N = 114) in multivariate analysis in PFS. Biallelic TP53  and TP53 mutation  reflected the highest number of significant events in univariate analysis in OS and the highest proportion in PFS.We subsequently conducted a survival analysis and evaluated the effect on outcome associated with each AS event both within their genomic subgroup and among all NDMM samples, Supplementary Table RHOQ within the t comparison. RHOQ encodes a GTPase that binds to a variety of effector proteins to regulate cellular responses. The mean PSI of this event was used to divide all t samples into two subgroups, and although there was no gene level expression difference between the two t groups  reflecting a significantly worse median PFS and OS compared to the low-risk group (N = 207) , we tested whether patients with an increasing number of markers would result in decreased outcome. The samples were divided into three subgroups based on the number of poor prognosis markers they carried: low risk (0–2 markers), medium risk (3–4 markers), and high risk (5–7 markers) groups (no samples contained more than 7 markers). As the samples contained more markers, an additive risk was observed with the high-risk group  was set to preserve high correlations, from which three correlation networks were constructed: t Fig. ZFAS1, whose splicing level was consistently lower in the three translocation comparisons compared to non-translocated samples. For example, the correlation between AL_ZFAS1 splicing and HNRNPH1 expression remained moderately high in all three translocations  patients [NSD2, a histone methyltransferase, affecting chromatin structure and function. In AML there is evidence of a coordinated alterations in IDH2 (epigenetic regulator) and SRSF2 (splicing factor), resulting in distinct splicing changes and increased stalling of RNA polymerase II [NSD2 and DIS3, and indeed the splicing landscape presented here aligns with previous reports of alternative active promoters with altered chromatin marks in MM [in yeast , This ef samples , 50. DISpatients , so we aerase II . A similks in MM .HNRNPH1 among three translocation groups), making these factors potential common targets.Splicing factors are fundamental components of the spliceosome that control the splicing process and small molecules have been developed to target them in diseases with aberrant splicing . We haveCollectively, our study not only revealed the AS landscapes and their functional impact, but also their associated risks and clinical impact. The driving force of AS frequency in NDMM was also unveiled. This brought novel understanding of how AS is regulated and involved as part of the MM machinery, but also novel events and pathways that can be potentially targeted.Supplementary MethodsSupplementary FiguresSupplementary Tables