Epiretinal membrane (ERM) is a pathologic tissue that develops at the vitreoretinal interface. ERM is responsible for pathological changes of vision with varying degrees of clinical significance. It is either idiopathic or secondary to a wide variety of diseases such as proliferative diabetic retinopathy (PDR) and proliferative vitreoretinopathy (PVR). A great variation in the prevalence of idiopathic ERM among different ethnic groups proposed that genetic and lifestyle factors may play a role in ERM occurrence. Histopathological studies demonstrate that various cell types including retinal pigment epithelium (RPE) cells, fibrocytes, fibrous astrocytes, myofibroblast-like cells, glial cells, endothelial cells (ECs) and macrophages, as well as trophic and transcription factors, including transforming growth factor (TGF), vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) etc., are directly or indirectly involved in the pathogenesis of idiopathic or secondary ERMs. These processes are driven (on the last count) by more than 50 genes, such as Tumor Necrosis Factor (TNF), CCL2 (chemokine (C-C motif) ligand )), Metastasis Associated Lung Adenocarcinoma Transcript 1 )MALAT1-β1, TGF-β2, Interleukin-6 (IL-6), IL-10, VEGF and glial fibrillary acidic protein (GFAP), some of which have been studied more intensely than others. The present paper tried to summarize, highlight and cross-correlate the major findings made in the last decade on the function of these genes and their association with different types of cells, genes and gene expression products in the ERM formation. Epiretinal membrane (ERM) is a pathologic layer, located at the vitreoretinal interface, which grows on the inner surface of the retina , 2. It iERMs can be either idiopathic or secondary -13. In iSecondary ERMs are the result of an already existing ocular pathology, including vitreoretinal vascular disorders. Common causes include PVR, PDR, hypertensive retinopathy as well as diseases like intraocular inflammation , occlusion of a central or branch retinal vein, retinal detachment (RD) and ocular trauma -12. SecoStructurally, ERM includes an inner and an outer extracellular matrix (ECM) layer. The first one includes one or multiple cell layers while the second one contains randomly oriented extracellular fibrils, in most cases , 18, 19.Nonangiogenic fibroglial tissue characterizes idiopathic ERMs, in contrast to PDR ERMs which are mainly characterized by neovascularization , 21. ERMFibrocellular proliferation of ILM and cellular contraction are also ERM features. PVD can injure the ILM, allowing movement of glial cells to the retinal surface and favorable conditions for fibrocellular proliferation, between the vitreous and the retina. This might be provided by an incomplete PVD as well -7. The present paper tried to summarize , 6, highBased on the review literature, this article focuses and attempts to summarize, highlight and cross-correlate the major findings about the role of cells, genes and gene expression products implicated in ERM formation, idiopathic and secondary.This was a literature research on PubMed and the Scholar databases for articles related to the pathogenesis of ERM, idiopathic or secondary. “Epiretinal membrane”, “genes”, “genetic associations”, “proteins”, “growth factors” and “pathogenesis” are the keyword combinations used. The research represents a critical appraisal focused on articles dated from January 2008 until December 2019. A total of 60 articles were identified, and the analysis of their references revealed an additional 78 articles related to ERM pathogenesis, which were also reviewed. General aspects of ERM pathogenesisMigration of retinal-tissue-derived glial cells from the optic disc together with microdefects in the ILM , which occur during PVD and move towards the surface of the retina may contribute to the pathogenesis of ERM , 24, 25.Clinical SignsERMs impact negatively the quality of life leading to significant visual impairment , 28. TheAlthough, the early form of thin and translucent ERM is usually non-symptomatic or may provoke minor changes in visual acuity, which is seldom below 20/200, the more severe forms characterized by a semi translucent, thick, and contractile membrane can cause significant loss of visual acuity and a number of visual symptoms , 31, 32.Epidemiology and Risk FactorsDespite extensive observations about ERM, its pathogenesis and pathophysiology are still poorly understood , 37. The most consistently identified risk factors for ERM are age and PVD , 19, 38,ERM prevalence does not differ significantly between men and women , 31, 39.There are almost 30 million people in the USA having ERM in at least one of their eyes. Moreover, the prevalence seems to be lower in Asia, markedly lower in China , 28 but The role of race and ethnicity in the prevalence of ERM has remained unclear , 14, 40.Increased prevalence of idiopathic ERM accompanies diabetes, hypercholesterolemia and vascular narrowing or occlusion. These associations suggest a strong role for metabolic factors , 19 in tThe Genetic and Proteomic Basis of ERM PathogenesisDespite our incomplete knowledge of the pathogenesis of ERM , gene exCells Involved in ERM PathogenesisERMs are characterized by cellular migration and proliferation on the inner retinal surface , ECM forRPE cells is apparently the major cell type contributing to ERMs and intravitreal membranes in eyes with rhegmatogenous RD, which is complicated by massive periretinal proliferation . RPE celOther cell types whose origin is not fully elucidated, especially in diabetic ERM, are fibroblasts , 58. TheMüller cells are other type of cells with central role in ERM contraction due to their ability to express a-smooth muscle actin (a-SMA), which is involved in membrane contraction and produce various collagens , 59, 60.TGF-β contributes to ERM contraction by upregulating the expression of a-SMA in the epiretinal cells. Non-expressing a-SMA retinal Müller cells apparently produce collagens that form the ECM network in idiopathic ERM, while it is the retinal Müller cells that express a-SMA responsible for the membrane contraction . The rolGenes Involved in ERM PathogenesisSeveral changes take place in gene expression during the onset as well as later in progress of various diseases , 71. GenCOL1A1, COL1A2, COL3A1, POSTN, THBS1, LGALS1, SPARC, FN1, TIMP3 and DCN. These findings are supported by morphological studies that enhance the positive correlation between the disease process with the amount of ECM in ERMs, especially in PVR-ERMs [, several other genes and proteins, among them 60 genes not detected in PVR-ERM, may also be involved [The expression of genes in idiopathic or secondary ERM has been extensively studied . There aPVR-ERMs , 72. Geninvolved . Of genes mentioned above, MALAT1 is associated with ERM formation, with a crucial role in the progression of secondary, especially PVR, ERM. This gene is a large, non-coding RNA, highly conserved among mammals, which appears to be the most actively expressed PVR-ERM gene. It is involved in the expression of genes associated with metastasis and motility at the level of transcription and/or post-transcription regulation , 73. As The expression of genes like RELA , tenascin C (TNC), glial fibrillary acidic protein (GFAP) and other cytokine-encoding genes such as TGF-β2, IL-6, VEGFA and CXCL1 (chemokine C-X-C motif ligand 1) is intensified irrespective of sex, especially in idiopathic ERM eyes . RELA isAnother gene that possibly plays an important role in ERM formation is the PPM1D gene. This gene encodes Wild-type p53-induced phosphatase 1 (Wip1), which has a key role in stress signaling , 77, infSP1 mRNA (specificity protein 1) is also highly represented in PDR-ERM. SP1 regulates expression of genes that encode angiogenesis-related factors and has a vital role in PDR angiogenesis . SP1 regTGF-β2, IL-6, RELA, GFAP, VEGFA, TNC and CXCL1 are slightly more expressed in eyes with idiopathic ERM [Genes associated with wound healing and angiogenesis along with the pro-inflammatory genes, thic ERM , while tthic ERM , 66, 67 thic ERM , 57, 67.Glial cells like astrocytes or MGCs, which express GFAP, are associated with idiopathic ERM. Retinal glial cells produce VEGF , 47, whe. Finally, the expression of several genes that encode cytokines in ERM eyes is enhanced, while it remains stable for other cytokine encoding genes. As mentioned above, genes like RELA, VEGFA, IL-6, TGF-β2, CXCL1, GFAP and TNC are significantly upregulated in the idiopathic ERM eyes, while ACTA1 and IL17A are not detected in idiopathic ERMs. In addition, the levels of CCL2 (chemokine (C-C motif) ligand 2), CCL5, CXCL10, TGF-β1, TNF, STAT3, RORC, TBX21, IFNG and POSTN does not change in idiopathic ERM . Collagens and ERMGiven the increased collagen deposition and membrane contraction, the ERM progression is a fibrotic process. Collagen plays a crucial role in ERM construction and collagen types I-VI are important components of idiopathic ERM , 82, 83.On the basis of their collagenous fibrillar ECM, two major types of idiopathic ERM can be identified. One of them is characterized by high amounts of fibrils consisting of collagen type VI; the other by high amounts collagen types I and II , 83. TheCollagen types I, III and IV are freshly produced during idiopathic ERM formation. They promote the fibrotic process and contribute to the formation of hard collagen scaffold, where the myofibroblast precursor cells, when anchored there, become more susceptible to fibrogenic factors . CollageNewly formed ERM collagens along with the density of the associated cells apparently enhance vitreoretinal adhesion. Collagens types I, III and V enhance mechanical functions, are major constituents of skin, muscles, blood vessels and fibrotic scar tissues, and can affect the mechanical properties of ERM. However, collagens types I, III and V do not appear in the normal vitreoretinal interface. Recently formed collagens not only provide strong adhesion to various tissues, but also influence the rigidity and digestibility of ERM . Changes of Cytokines and Growth Factors in ERM FormationCytokines and growth factors are present in both idiopathic and secondary ERMs, and in VF. These peptide factors, which are mediators of angiogenesis, may cause or aggravate diabetic neovascularization and, given that the growth and contraction of the ERM are regulated by growth factors , they maCytokines contribute significantly to various inflammatory processes, to wound healing and fibrotic scarification. Fibrosis is associated with the activation of fibroblasts, which triggers collagen and glycosaminoglycan production. Release of soluble factors such as TGF-β and profibrotic mediators, like PDGF, IL-4, IL-6, IL-13 in the retina, triggers the fibrotic process via glial cells and macrophages , 88-91. Specific cytokines that participate in ERM formation and are specifically expressed in idiopathic ERMs include TGF-β , 92, intreceptor kinase A), trkB (Tropomyosin receptor kinase B) and trkC (Tropomyosin receptor kinase C), which are involved in neurite growth [In addition to cytokines, growth factors are expressed in idiopathic ERMs contributing to ERM pathogenesis. These factors include IL-6, TGF-β, nerve growth factor (NGF), CTGF, glial cell-line-derived growth factor (GDNF), PDGF, basic fibroblast growth factor (bFGF), pigment epithelium-derived factor (PEDF) and VEGF, which promote the migration and/or proliferation of a heterogeneous variety of cells , 39, 94.e growth . The Transforming Growth Factor-β FamilyThe TGF-β family, in particular, has a crucial role in idiopathic and secondary ERM formation. It is involved in normal tissue repair and development of fibrosis. TGF-β members upregulate ECM production, inhibit protease synthesis and mediate both inflammatory response and myofibroblast differentiation, while retinal Müller cells and hyalocytes produce and activate TGF-β . TGF-β mGiven that the ECM network including various collagen types, enhances the migration and proliferation of ERM cells, the TGF-β involvement in pathological ECM networks (that are responsible for idiopathic ERM) may explain its increase in the VF of patients with idiopathic ERM .The TGF-β1 and TGF-β2 are upregulated in both the vitreous humor and the ERMs  which meTGF-β2 is, therefore, associated with development of idiopathic and secondary ERM and its levels are correlated with intraocular fibrosis , 15. MorNerve Growth FactorNGF can also contribute to the myofibroblast differentiation. In addition, NGF receptors may have a crucial role in this process. Hence, NGF may support the development of idiopathic ERM via the fine tuning of bound versus free NGF levels. Both increased expression and utilization of NGF may be involved in intracellular and intercellular signals leading to ERM. Therefore, both TGF-β2 and NGF contribute to idiopathic ERM pathogenesis, while NGF can induce functional changes in ERM cells and be a basis for evaluation of ERM pathology . Vascular Endothelial Growth Factor -Agene is overexpressed in ERMs [VEGF is an EC- mitogen and angiogenic inducer, which is greatly expressed in various retinal cell types in case of hypoxia . It cont in ERMs , 17. VEG in ERMs .Insulin-like Growth Factor Insulin-like growth factor (IGF) apparently contributes to the progression of ERMs , 23, 66,Furthermore, protein array analysis indicates that cytokines and growth factors in the aqueous fluid (AF) and VF of eyes with idiopathic ERM do not appear to differ significantly in their concentrations. Thus, only the homodimer form of PDGF-A is overexpressed in the VF compared to the AF .The Platelet-Derived Growth Factor ReceptorThe PDGF receptor and its subunits have been investigated in patients with secondary (PVR) ERM, whereby the PDGFRa subunit is more frequently encountered than the PDGFR𝛽 subunit -101. PDGAfter RPE cells migration to the ERM or the vitreous cavity, they undertake the role of secreting stimulatory factors . RPE celGlial Cell-Line-Derived Growth Factor GDNF and its receptors seem to be involved in several proliferative vitreoretinal disorders, however, their role in ERM formation, especially idiopathic one, has not been widely studied. Given that GDNF levels are far below the sensitivity threshold in idiopathic ERM samples , there aNeurotrophin ReceptorsFinally, expression of neurotrophin receptor mRNAs  in idiopathic and secondary (PDR) ERMs are similar. However, NGF in idiopathic ERM is significantly higher. In contrast, GFR𝛼1  receptor mRNA is higher in idiopathic ERMs compared to secondary (PDR) ERMs, while GFR𝛼2  expression levels are extremely increased in secondary (PDR) ERMs . Changes of Other Proteins Specific to Idiopathic ERMGiven the anatomically close relationship between the vitreous and the retina , the patOther studies of idiopathic ERM vitreous proteome identified  233 proteins that are expressed at lower levels, including ESR1 protein, and 179 proteins that are expressed at higher levels. The obvious conclusion that there are severe level changes in several proteins in patients with idiopathic ERMs was also accompanied by the finding that vitreous protein concentration is, on the average, significantly higher in idiopathic ERM . In idioThe UBE2O (Ubiquitin-conjugating enzyme E2 O) protein is upregulated in the idiopathic ERM proteome and the vitreous. Ubiquitination is a significant enzymatic post-translational modification of all eukaryotic organisms. Post-translational modification of proteins by ubiquitin (Ub) determines several functions including DNA repair, procedures of endocytosis and cellular signaling and control of protein quality. Idiopathic ERM pathogenesis is associated with protein misfolding and subsequent aggregation. These procedures influence negatively proteins while they also decrease production of new proteins. However, validation of UBE2O as a protein biomarker of idiopathic ERMs needs to be evaluated on larger samples .A long part of LR11  is thought to play a key role in the pathogenesis of idiopathic ERM and its VF levels of eyes with idiopathic ERM, especially of the proliferative subtype, are apparently upregulated. LR11 is considered a risk factor for the progression of idiopathic ERM given that it provokes migration and proliferation of several cells, and as its possible association with the TGF-β2 signals. LR11 is thought to act independent from clinical characteristics such as age, hypertension and diabetes mellitus .Finally, removed idiopathic ERM specimens were immunostain positive for GFAP, vimentin, CD45, CD68, CD163 and cellular retinaldehyde binding protein (CRALBP), which indicates the role of hyalocytes and glial cells . GFAP, iChanges of Proteins That Occur in Both Idiopathic and Secondary ERMsApart from proteins specific to idiopathic ERM, there is a great variety of proteins involved in both idiopathic and secondary ERMs. Proteins like FN, laminin and VTN promote cell adhesion and contribute to idiopathic and secondary (PDR) ERM formation. FN, in particular, is a multifunctional glycoprotein that its association with ERM was described more than 10 years ago . It promA gene with 50 exons encodes FN mRNA. It codes extra domains A (EDA) and B (EDB), and type III homology connecting segment (IIICS), each identified by alternative splicing. Differential pre-mRNA processing potentially leads to 21 different FN isoforms. The EDB FN variant, in particular, is highly expressed in proliferating tissues , but is not detected in normal adult tissues, the main reason it has been proposed as an angiogenic marker .Ιn both idiopathic and secondary (PDR) ERMs, collagen type IV, FN and EDBFN are some ECM proteins that are overexpressed. Their overexpression is associated with elevated TGF-β and endothelin-1 (ET-1), which is more pronounced in idiopathic ERMs . Neovascularization is considered as the first step in ERM formation. Neovascularization is a process where many angiogenic factors like apelin have a significant role , 87, 108Apelin is also present in the vascular and glial components of ERMs. Thus, elevated apelin expression is associated with a characteristic microenvironment around new vessels, triggered by glial cell proliferation. Furthermore, it upregulates expression of adhesion molecules and promotes cell aggregation independently of cell expansion .The expression of SP1 in secondary (PDR) ERMs has not been determined. Nevertheless, the mRNA of SPI is highly expressed in secondary ERMs, while the SP1 protein is mainly colocalized with VEGF which is significant for ERM formation. SP1-like proteins bind to GC-rich motifs of many promoters and are involved in several cellular activities. The expression of various genes like TGF-β, VEGF fibrogenic factors and matrix genes is regulated by SP1, thus these cytokines may be detected in the VF and secondary ERMs. Moreover, SP1 may be crucial for overexpression of VEGF, given its central role in angiogenesis, cancer and ERM pathogenesis , 80, 109ET with its 3 isoforms ET-1, ET-2, and ET-3. ET-1, is a potent vasoconstrictor molecule involved in several types of ERM. ET is recognized by ET receptors A (ETA) and B (ETB) . ET-1 isLittle is known about the ET-1 expression and its association to fibroblast proliferation in secondary, especially PDR, ERMs. There are reports about its essential role in mediating fibrosis in secondary ERMs, although it is not clear if the membrane cells produce ET-1 , 110-112Nonetheless, glial cells secrete ET-1 in idiopathic as well as secondary (PVR) ERMs, whereas RPE cells secrete ET-1 only in secondary (PVR) ERMs. Therefore, ET-1 may play a central role in formation and contraction of PVR ERMs, given that glial and RPE cells contribute to this process. In addition, ETA and ETB are detected in both idiopathic and secondary (PVR) ERMs , while EET-1 and S100A4 are possibly upregulated in PDR ERMs, while staining techniques show that ET-1 and S100A4 are colocalized. This indicates an association between ET-1 and fibroblastic proliferation. In addition, it indicates that idiopathic and secondary ERMs are mediated by different mechanisms, especially as ET-1 seems to be a crucial factor in advanced PDR and fibroblastic transition. Therefore, ET potentially contributes to idiopathic and secondary ERM .Epithelial membrane protein-2 (EMP2) is significantly expressed in many intraocular membranes. This suggests that RPE-derived cells are a crucial or predominant cell type in idiopathic and secondary (PVR) ERMs, given that EMP2 is a regulator of integrins, and is highly expressed in the RPE. EMP2 is also expressed in many secondary (PVR) ERMs, but additional research is needed to establish the association between EMP2 and secondary ERM . Older sAnother protein expressed in ERM is aquaporin (AQP). AQP1 has been described to be active in several cell types of the retina during both pathological and non-pathological conditions -119. AQPANP  also has a crucial role for ERM formation. The ANP protein, is a natural inhibitor of angiogenesis. ANP possibly regulates the growth of secondary (PVR) ERMs and is expressed by several cells of ERM tissue, including vascular EC, RPE cells, glial cells, macrophages, vascular smooth muscle cells (VSMC) and fibroblasts. Thus, most cells that contribute to ERM formation express ANP. Because ANP is implicated in increased glial proliferation, it might contribute to secondary (PDR) ERMs formation , 128. Periostin (POSTN) is another protein that plays a significant role mainly in secondary (PVR) ERMs. Periostin mRNA is detected in secondary ERMs, while it is low in normal retinas. It interacts with integrins αvβ1, αvβ3 and αvβ5 under conditions of tissue development and remodeling and thus enhances cell motility. POSTN is also implicated with development of several tissues and organs, including teeth, heart valves, bone and in tumor metastasis . Periostin is found in pathologically fibrotic areas, and it may be essential for the development of PDR-ERM. It is suggested that periostin is significantly increased in the vitreous of eyes with secondary ERMs, which indicates its association with proliferation of secondary ERMs . Finally, Snail protein may have a role in secondary (PVR) ERMs. Snail protein is primarily expressed in the nucleus and is detected via immunofluorescent staining. Several signaling pathways are involved in its regulation. Factors like Glycogen synthase kinase-3b (GSK-3b), Phosphatidylinositol 3-kinase (PI3K), Ras, Smad and MAP kinase enhance Snail regulation by TGF-β1. The expression of Snail in secondary (PVR) ERMs implicates its role in PVR pathogenesis. Moreover, Snail transcription factor has a significant role in epithelial to mesenchymal transition in human RPE cells, induced by TGF-β1, indicating that this protein may regulate the formation of secondary (PVR) ERMs  Table 5Table 5. Inflammation Molecules in Idiopathic and Secondary ERM As already mentioned, idiopathic ERM pathophysiology involves a large number of inflammation proteins, immune reactions and cytoskeleton remolding. Therefore, classical, alternative and lectin-induced pathways of complement activation, have all significant roles in the idiopathic ERM process. Five proteins including C4A (Complement C4-A), C3 (Complement C3) and complement factor B (CFB), are increased in idiopathic ERM with C4A being significantly upregulated. In addition, inflammation occurring in idiopathic ERMs may implicate the complement and coagulation cascade pathway as contributing factors .Cellular adhesion molecules also have an essential role for the binding of circulating inflammatory cells to specific sites, while they play a crucial role in cell proliferation. There are 25 cell adhesion proteins detected in AF and VF compartments of eyes affected by idiopathic ERM. The protein intracellular adhesion molecule 1 (ICAM-1) is detectable in the VF of eyes with idiopathic ERM, in contrast to AF from the same eyes, while it is frequently detected in the ECM of idiopathic ERM and in the endothelium of secondary ERMs .Development of ERMs strongly correlates with the presence of CD44 and VCAM-1. CD44, a cell-surface glycoprotein, is involved in a wide variety of functions, including cell migration and adhesion, tumor growth and progression, also it is receptor for hyaluronic acid , 130. InFinally, development of fibrotic and inflammatory processes is related to increased levels of IL-1b, IL-2, IL- 4, IL-6, IL-10, TNF-a and INF-c , 132-134As mentioned above, the main theories proposed for the pathogenesis of ERM, involve either cellular migration or cellular proliferation. The present mini review of the past decade developments tried to summarize, highlight and cross-correlate the major findings on the function of genes involved in ERM and their association with different types of cells, genes and gene expression products implicated in ERM formation. Although the issue of ERM pathogenesis remains poorly resolved, certain features of ERM are already apparent;1. With the exception of TGFb2 gene, there is apparently no overlap between the genes identified so far as being involved in idiopathic ERM, PVR-ERM and PDR-ERM . This ma2. MGCs and fibroblasts or collagen cells are involved both in idiopathic ERM and PDR-ERM. However, based on our present knowledge, it is rather evident that in each case they use different sets of cytokines. 3. The pathogenesis of secondary ERM ,5 or its4. Growth factors, cytokines and ECM have a central role in ERM pathogenesis , as they are implicated in several functions, including cellular signal transmission and tissue modification .Finally, the great number of genes involved in ERM should serve as a warning for reaching premature conclusions regarding our expectations for potential gene manipulation-based treatments of ERM. The experience, so far, from the field of oncology argues excessive reliance on genetic data for combating cancer -138. PerEthical issues have been completely observed by the authors. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship of this manuscript, take responsibility for the integrity of the work as a whole, and have given final approval for the version to be published. No conflict of interest has been presented. Funding/Support: None.


Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase. Intracellular molecular pathways are specific networks of interacting molecules that are involved in certain molecular functions . KnowledSeveral popular algorithms and software like gene ontology (GO) analysis tools ,b, MetacOn the other hand, knowledge of the individual gene product roles within a pathway can make it readable in terms of finding its activation profiles. Indeed, several techniques had been proposed, e.g., Oncofinder , iPANDA We proposed an algorithm that identifies functional roles of the pathway components based on the pathway topology and applied it here to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, NCI, and HumanCYC databases, collectively covering 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data.biopax format. In the case of Qiagen Pathway Central database, no machine-readable format of data was available, and we manually curated data from the available graphical pathway representations  parameter can be formulated as follows: pathway activator (ARR = 1), rather activator (ARR = 0.5), repressor (ARR = −1), rather repressor (ARR = −0.5), and gene product with uncertain or inconsistent role (ARR = 0). In the previous studies, ARR values were obtained by manually curating pathway graphs. This is however not feasible for annotating thousands of molecular pathways. We developed an original algorithm that automatically assigns ARR score values to gene products that participate in a molecular pathway.The ARR annotation algorithm is based on the machine reading of gene product interaction graph within each pathway. Nodes correspond to gene products, and the ribs between every pair of nodes represent molecular interaction between the corresponding gene products. Each rib on the graph has a direction and is characterized by an activator or inhibitory nature of the molecular interaction it represents. For the correct calculation of ARR values, the pathway graph must be connected, wherein a weak connectivity is acceptable.If the pathway molecular interaction graph meets these criteria, then ARR coefficients can be algorithmically assigned to the participating gene products. For the biochemical pathways, we put enzyme gene names on the pathway nodes, and the interaction ribs represented directions of the catalyzed reactions.The algorithm used consisted of the following major steps.max for which N+M reaches the maximum value. The central node identified is then assigned with ARR = 1 value. It serves as the starting point for further recursive assignment of ARR values to the other nodes. If multiple nodes have the same maximal N+M, then V-node for a pathway is selected randomly among those “maximal” nodes. Therefore, the algorithm is suitable also for circular-organized pathways, where all nodes will have equal N+M.Initialization. At this stage, a major node is algorithmically identified to be the “central” node of the pathway graph . The maji under ARR annotation may have ribs either directed toward V (Pi → V) or outward V (Pi ← V) on the graph. During recursion, each rib can be considered only once in order to prevent endless recursion in case of cyclic interactions on the graph. If the rib has an “activator” characteristic, temporary ARRtemp = 1 is assigned to the node Pi. In contrast, if the rib has an “inhibitor” characteristic, Pi is assigned with ARRtemp = −1. Conversely, all the gene products included in the node Pi receive the same ARRtemp characteristics.Recursion. For every node V, all connected nodes Pi belongs to node Pi. If GPi was never previously considered in the graph traversal, ARR = ARRtemp(Pi) for the node Pi would be assigned for GPi. In the case when GPi was previously considered in the graph traversal and the previously assigned ARR of it node is equal to the current ARRtemp(Pi) then ARR = ARRtemp would be assigned to the node Pi. If GPi was previously considered in the graph traversal but its previously assigned ARR is not equal to ARRtemp(Pi), then ARR is assigned to the gene product GPi according to the following conflict resolution rule.Let gene product GPi with previously specified ARR or ARRs is currently considered in the graph traversal but its previously assigned ARR(s) contradict(s) with the ARRtemp(Pi), then the conflict(s) should be resolved as follows:If a gene product GPtemp(Pi) are different, then the resulting ARRfinal(Pi) = 0;If the signs of the previous ARR coefficient(s) and ARRtemp(Pi) and any of the previous ARRs(GPi) does not exceed 0.5 and at least one of the ARRs is positive, the resulting ARRfinal(Pi) = 0.5;If the difference between ARRtemp(Pi) and any of the previous ARRs(GPi) does not exceed 0.5 and at least one of the ARRs is negative, the resulting ARRfinal(Pi) = −0.5.If the difference between ARRi all of its gene products starting from the nodes proximate to the central node V. As a result, the algorithm will assign ARR values to all the connected the graph nodes and the enclosed gene products.Then the recursion R is initiated for every node PAfter the recursion finalization pathway activators will have ARR = 1, rather activators – ARR = 0.5, inhibitors – ARR = −1, rather inhibitors – ARR = −0.5, and genes with inconsistent role – ARR = 0. The recursion is stopped when a vertex with 0, 0.5, or −0.5 ARR is encountered during the traversal of the graph. This rule is needed because otherwise all vertices will have ARR 0, 0.5, or −0.5 in case of the only one ARR inconsistency found. However, this rule also may lead to exclusion of some genes described in the original source.Therefore, the gene products included in the molecular pathway database will have the assigned ARR values representing their functional significances in the given molecular pathway. These values can be used for further calculations of the PALs according to any algorithm of PAL calculation.genes include gene names according to HUGO Gene Nomenclature Committee (HGNC) nomenclature and the corresponding ARRs for the gene products participating in the pathway under consideration. The worksheet (ii) edges include information about molecular interactions between every pair of the interacting pathway nodes. Every node is defined by the names of gene products or physiological outcome(s) that form this node. The interaction type is specified as “activation,” “inhibition,” or “undefined,” where appropriate. The worksheet (iii) nodes include node names and gene names corresponding to every node on the pathway graph.We report here an ARR-curated database of 3,044 molecular pathways including 2,018 core pathways and 1,026 micropathways . The curIt should be noted that annotation of similar pathways may be different between the source databases. For example, EGFR signaling pathway is presented in Qiagen database as “EGF_Pathway,” in Reactome as “reactome_Signaling_by_EGFR_Main_Pathway” and in Biocarta as “biocarta_egf_signaling_Main_Pathway.” Yet conceptually similar, all three pathways have different gene and edge compositions. In this study, we did not aim to identify inconsistences between different source databases and annotated all the pathways under their original names.https://pypi.org/project/oncoboxlib/. Algorithm is implemented as a Python library. It takes normalized  gene expression data as an input. Gene symbols should be provided in HGNC format accessible through the web-site genenames.org. At least two groups of samples are required: cases and controls, each group represented by at least one sample. Sample names should contain “Norm_” (for controls) or “Tumour_” (for cases). Output will contain PAL values for each pathway in each sample. All annotated pathway datasets mentioned in this paper alternatively can be downloaded and used for PAL calculation using the same link.We made freely accessible software for PAL calculation using the annotated pathway database accessible following the link: n = 16; n = 7) samples of patients who died in road accidents (We also provide here an example of PAL calculation for real-world data. We extracted gene expression data for gastric cancer samples n = 16;  togetherccidents , that weccidents .We propose here the recursive algorithm for functional annotation of the molecular pathway nodes, and its application to annotation of 3,044 human molecular pathways, including signaling, metabolic, and DNA repair pathways extracted from six major pathway hubs . The ARRThe proposed algorithm is suitable for the analysis of pathways with already established gene content and known topology of its molecular components. The algorithm can be used for agnostic objective characterization of interacting gene networks. The underlying rationale allows reducing operator’s errors and subjectivity in annotating the molecular roles of pathway components, which are inevitable in case of manual curation of the pathway graphs including hundreds of nodes. Another advantage is the pathway-centric approach during annotation, when gene product role in one pathway can be different from its role in another pathway.The major limitations deal with the algorithm applicability only for the tasks of further calculations of pathway activation scores/ranks. Such an approach also does not address the issue of crosstalk between different molecular pathways, because all pathways are analyzed separately.In this study, we annotated a collection of previously published human molecular pathways . We planThe original contributions presented in the study are included in the article/MS, NB, DK, and AB contributed to conception and design of the study. MS developed recursive pathway annotation algorithm. DK, AGu, MZ, and AGa manually curated the pathways and performed recursive algorithm implementations. AB, MZ, and MS wrote the manuscript. All authors contributed to the article and approved the submitted version.MS, AGa, and AB have a financial relationship with OmicsWay Corp.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The handling editor declared a shared affiliation with the authors AB, MS, and AGu at the time of review.


Currently, it appears that there is a lack of understanding related to the role of SSF, in the two-phase behavior of the deceleration history, which is an issue discussed recently in the impact dynamics field. This paper analytically and numerically focuses on the effect of SSF on the projectile deceleration characteristic of concrete-like targets. Firstly, the penetration process according to the two-phase feature of the projectile deceleration is revised, where analytical results indicate that the SSF has a phased feature corresponding to the two-phase behavior of the deceleration history. Furthermore, a series of numerical simulations are conducted to understand the role of SSF more clearly. Simulation results show a similar conclusion to the analyses of the two-phase penetration process; at the range below a certain critical striking velocity, adding friction can reproduce the experimental data; when exceeding the critical striking velocity, the simulated results without considering friction are closest to the experimental data. Hence, it could be gained that the role exchange between the SSF and the dynamic term contributes to the two-phase penetration behavior for concrete-like materials. This indicates that the sensitivity of SSF to the penetration process is one of the factors driving the two-phase feature.  Nondeforming projectile penetration into concrete-like materials has been studied for a wide range of applications with regard to civil and military shelters. Many studies related to the penetration of concrete-like materials dealt with the question of modeling and quantifying the force that resists the penetration . HoweverIn several theoretical analyses and semiempirical equations, the SSF is usually neglected or a 0.02–0.2 static friction coefficient is used directly to gain good agreement with the experimental data ,5. ForreOn the other hand, Chen  firstly Meanwhile, some nonconstant friction algorithms were developed and applied in the analyses of force resisting the projectile penetration. Jones et al.  and DaviAs discussed above, in a certain range of the initial striking velocity, consideration of the SSF between the projectile and the target is reasonable, where the coefficient of SSF has a monotonic decreasing correlation with the penetration velocity of the nondeforming projectile ,16,18,19The two-phase behavior of the deceleration history during rigid penetration was first introduced by Hill in his pioneering work during World War II . He indiSimilarly, Yarin et al.  and RubiFurthermore, rosenberg and Dekel  performeAfterward, in the arguments between Warren’s and Rosenberg and Dekel's work ,22,24, WRecently, Fan and Li  obtainedIt is found that the above studies mainly focused on metal targets. However, the two-phase feature of the deceleration history during rigid projectile penetration also exists in the concrete-like brittle targets. This was shown directly in deceleration–time measurements performed by Forrestal et al. . These eZhang and Mu  adopted It is also noted that the discussion held by Kong et al. and Rosenberg and Dekel ,27 suppoThe aforementioned results substantiate the two-phase behavior of the deceleration history of a penetrating rigid projectile. It is evident from the above dynamic spherical cavity expansion model and velocity field theories, as well as from numerical and experimental studies and from semi-empirical equations, that the projectile deceleration history in the rigid penetration mode can be delineated by two distinct phases. When the initial striking velocity is below a certain critical striking velocity, the projectile axial resistant force shows an approximate peak plateau response; however, when the initial striking velocity exceeds this value, the projectile axial resistance force presents a well-defined peak. Furthermore, the event is not only found in metal-like materials, but also in concrete-like materials. This proves that the two-phase phenomenon is not an accidental event, whereby it does exist in the rigid penetration process, which is the analytical premise for the present paper.Note that the discussions above mainly focused on the two-phase feature, while the velocity-independent characteristic was not discussed, as mentioned in the research and development (R&D) approach .As stated above, it is acceptable that projectile deceleration exhibits two-phase behavior in rigid penetration for concrete-like materials, as demarcated by a critical striking velocity. The two corresponding phases were identified as the “squeeze and push” (SP) penetration phase and the Dynamic Spherical Cavity Expansion (DSCE) penetration phase , accordiFr is the resisting force, acting along the trajectory of the penetrating projectile, and V is the instantaneous penetration velocity.The SP phase mainly involves the quasi-constant behavior of the deceleration history, which can be expressed by the following equation:A is a dimensionless coefficient, and According to the force analysis of the projectile nose in Note that, in the analysis process, the surface sliding friction coefficient μ is assumed to be a function of penetration velocity. Consequently, the following equation is gained by integrating Equation (3):d is projectile diameter, and β1 and μs are the model coefficient related to the projectile shape and the static friction coefficient. The detailed analytical process was described in , where dEquation (4) indicates that the axial resisting force includes two parts: the quasi-static term Apparently, Equation (4) indicates that the friction has an effect on this SP phase. The driving mechanism behind the quasi-constant behavior can be attributed to two actions: one is the “push” action since the existence of the penetration velocity; the other is the “squeeze” action. The premise of Equation (4) is that comments  stating Furthermore, with the striking velocity increasing, the DSCE mechanism gradually dominates the penetration process since the cavity diameter finally exceeds the projectile diameter in the finite penetration time in Eventually, it can be concluded that the role exchange between the SSF and the DSCE contributes to the two-phase behavior. SSF plays a major role in characterizing the SP phase, which can be denoted as an “external mechanism” since it can be changed by enhancing the projectile lubrication ability. Moreover, in this phase, the DSCE mechanism still exists, but the effect is relatively weak such that it can be neglected, while the quasi-static term and SSF term together contribute to the quasi-constant behavior of the deceleration history, where, according to the definition of the SSF, the quasi-static term can be denoted as an “internal mechanism”. In the DSCE phase, a well-defined peak performance of the deceleration history is found, accompanying the disappearance of the quasi-constant phenomenon; thus, Equation (4) considering the friction effect is not available to predict the penetration depth. The other internal mechanism DSCE is activated totally to govern the penetration process, and the corresponding cavity beyond the projectile diameter is generated since in the finite penetration time the cavity expansion size exceeds the projectile diameter. Consequently, this results in the disappearance of the “squeeze” effect.P/d is also dimensionless as a function the unit of the penetration depth and the projectile diameter. Note that The feasibility of two-phase penetration analysis was validated by comparisons with 12 penetration experimental cases . Two typThe two-phase analyses in All simulations in the study were performed with the Autodyn-2D code (version 11.0) , using tTo our knowledge, in order to count on the simulation results, the rigorous material models that properly represent the material behavior is required. The current paper aims to clearly study the SSF effect to the penetration process, the piece-wise linear Drucker‐Prager concrete material model  and the The SSF between projectile and target is a complicated problem, and there is still no feasible method for in situ measurements during the penetration process. Normally, a specific and accurate algorithm is requested to reproduce the friction physics for the numerical method; however, this is beyond the scope of this paper. Our main purpose was to qualitatively and numerically investigate the role of SSF in rigid penetration into concrete-like material targets.Firstly, residual velocity comparisons were conducted between perforation experimental data and simulated results without including the friction, as given in Aiming to investigate the role of SSF, the simulation results above were analyzed on the basis of the following error comparisons of residual velocity.E. According to the assumption of the existence of friction, the following equation was obtained:At the beginning, it was postulated that the friction always existed in all experimental cases. The simulated cases including the SSF and without regarding the SSF are denoted as Sf≠0 and Sf=0, respectively. The corresponding experimental results are denoted as Furthermore, since the SSF mainly retards the penetration of a rigid projectile into the target, it could be gained that Sf ≠ 0 < Sf = 0. Therefore, this indicates that the simulated results for the cases below the demarcation point were consistent with the postulation of SSF existence. On the other hand, for the shots beyond the demarcation point, it was found that Sf = 0 was smaller than the experimental data. According to the SSF retarding feature with respect to the penetration, the following logic relation was obtained:The simulations above adopted an indirect method, which was not strong enough to highlight the SSF effect. Therefore, to further directly elucidate the role of SSF, the corresponding cases considering friction were simulated, as detailed in this section.As mentioned before, for the SSF coefficient, it is difficult to give a precise algorithm due to the complexity and the measured difficulty during the penetration process; however, a proper static friction coefficient can be identified as the average of SSF . Thus, tMeanwhile, as a function of the fit between the static friction coefficient and the initial striking velocity, an exponential function was gained as shown in Equation (9) and Subsequently, for the penetration experiments, the static friction coefficient could be derived using Equation (9), as listed in Note that the analyses above assumed that the main assistant force was from the projectile nose, whereas the effect of projectile shank could be neglected, which is commonly accepted by most researchers ,5,15,18.In this section, the shank friction effect was revised numerically. On the other hand, aiming to further prove the shank friction effect, the surface stress of the projectile was investigated. Three gauges were selected as given in Eventually, according to the numerical simulations of the perforation and penetration cases, it could be summarized that the role of SSF is phased. At the range below a certain critical striking velocity, adding the friction could reproduce the experimental data; when exceeding the critical striking velocity, the simulated results without considering the friction were closest to the experimental data. Furthermore, the critical striking velocity for the penetration experiment was above 651 m/s, which is consistent with the 703 m/s gained according to the two-phase prediction models in The comparisons of penetration depth, energy loss, and surface contact force between the projectile shank and the target indicate that the effect of the shank friction could be deemed negligible, which further proves the reasonability of the present analytical work. Combined with the two-phase analytical results in There are two points to be taken into consideration for these simulations. One is that, in the perforation simulations, the reinforcing bar was neglected. Sliter  indicateThe study of penetration process analyses and numerical simulations illustrated the role of SSF in the two-phase feature of the deceleration history. These results provide insight into the major role of SSF in characterizing the SP phase of two-phase penetration. SSF and the quasi-static term both contribute to the quasi-constant behavior of the deceleration history for nondeforming projectile penetration into concrete targets. The two-phase penetration analyses and numerical simulations both showed that considering the SSF could allow an accurate prediction of the penetration depth below a certain critical striking velocity. With the rise in initial striking velocity, the penetration would enter into the DSCE phase, where the SSF effect is weak such that it can be ignored, while the DSCE is totally activated. Meanwhile, the numerical simulation results indicated that a friction coefficient of zero fit best with the experiments in the DSCE phase, further supporting the role exchange between SSF and DSCE during the penetration process. Last but not least, the variation of penetration depth, energy loss of the projectile, and the stress features of the surface gauge in the shank part and the nose part all indicated that the shank friction has a very slight effect such that it can be deemed negligible, where it was found that the addition of the shank friction only resulted in an extra 2.7% energy loss for the 545 m/s shot. This further validates the reasonability of the present study. It is necessary to emphasize that, in two-phase penetration analyses, the axial resistant force of the projectile nose is not needed to deduce the relative complex sliding friction, and only the static friction coefficient is required. More importantly, this point is obtained from the analysis itself, independent of any assumption. Furthermore, the analytical process considers the SSF feature depending on the striking velocity. The increase in “push” effect resulting from the increase in striking velocity contributes to a decrease in the “squeeze” effect due to the decrease in SSF sensitivity. This was consistent with the SSF feature depending on the striking velocity. The simulation results in This paper analytically and numerically investigated the role of SSF in the two-phase behavior of the deceleration history. This phased SSF feature improves the understanding of the corresponding two-phase penetration response. The study in this paper is beneficial for accurately predicting the penetration depth and the residual velocity in real engineering applications.


Cell division cycle-associated 7 (CDCA7), as a member of the cell division cycle associated family, was reported to be aberrantly expressed in both solid tumors and hematological tumors, suggesting its essential role in promoting tumorigenesis. Hence, we aimed to explore its comprehensive roles of overall survival (OS) in clear cell renal cell carcinoma (ccRCC) and emphasize its associations with immunity.The RNA sequencing data and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was adopted to explore CDCA7 associated signaling pathways. Univariate and multivariate Cox regression analyses were carried out to assess independent prognostic factors. Furthermore, roles of CDCA7 in human immunity were also investigated.Our results suggested that CDCA7 was overexpressed in ccRCC and its elevated expression was related to shorter OS (P < 0.01). Univariate and multivariate Cox regression analyses identified CDCA7 as an independent prognostic factor (both P < 0.05). The prognostic nomogram integrating CDCA7 expression level and clinicopathologic variables was constructed to predict 1-, 3- and 5-year OS. GSEA indicated that high CDCA7 expression was related to the apoptosis pathway, cell cycle pathway, JAK-STAT pathway, NOD like receptor pathway, P53 pathway, T cell receptor pathway and toll like receptor pathway, etc. Moreover, CDCA7 was significantly related to microsatellite instability  and tumor mutational burden . As for immunity, CDCA7 was remarkably associated with immune infiltration, tumor microenvironment, immune checkpoint molecules and immune pathways.CDCA7 could serve as an independent prognostic factor for ccRCC and it was closely related to MSI, TMB, and immunity. According to Cancer Statistics 2020 in the United States, renal cell carcinoma (RCC) is the 6th and 8th most common cancer in males and females, respectively . It is eCell division cycle-associated 7 (CDCA7) gene, also called JPO1, located on chromosome 2q31, is a member of the cell division cycle associated family of genes and encodes a 47 kDa nuclear protein consisting of 371 amino acids . JPO1 waSo far, however, there has been no detailed investigation of the role of CDCA7 in RCC. In the present study, we found CDCA7 was upregulated in ccRCC and significantly associated with some clinicopathologic parameters of patients via comprehensive and systematic bioinformatic analysis of RNA sequence data downloaded from The Cancer Genome Atlas (TCGA) database. CDCA7 also could serve as an independent prognostic factor of ccRCC patients. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to explore related enrichment genes and signaling pathways. Furthermore, we performed immune-related analysis of CDCA7 in ccRCC to reveal its potential function.http://cancergenome.nih.gov/), containing 531 ccRCC tissues and 72 matched adjacent normal kidney tissues. International Cancer Genome Consortium (ICGC) dataset  and ArrayExpress dataset  were utilized as the external validation cohorts. Data were standardized by log2 transformation and we set |log2 fold change (FC)|≥ 1 and false discovery rate (FDR) < 0.05 as the cut-off value.The RNA sequence data and related clinical information of kidney renal clear cell carcinoma (KIRC) patients were searched from The Cancer Genome Atlas (TCGA) Data Portal  were generated by means of the “survivalROC” package, and the area under the curve (AUC) values were calculated to assess the specificity and sensitivity of CDCA7 and associated clinicopathologic parameters including age, gender, race, grade, stage, and T, N, M.In order to identify significantly OS-related independent factors, univariate and multivariate Cox regression analyses were conducted to exclude clinical characteristics with little prognostic values from age, gender, ethnicity, grade, stage, T, N, M and CDCA7 expression level by R package.In order to predict the possibility of OS and visualize the correlation between individual predictors  and survival outcomes, we applied a nomogram model to help clinicians observe and predict the prognosis of ccRCC patients by using the R “rms” package. Points were divided to every parameter by performing the point scale in the nomogram, and we calculated the whole points by summing up the points of all factors.To analyze the signaling pathways of CDCA7, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed with the help of “clusterProfiler” R package. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method used to interpret gene expression data and analyze statistically significant and consistent differences between different groups with different biological states . The perhttp://pgrc.ipk-gatersleben.de/misa/misa.html), we screened out all the autosomal microsatellite tracts containing five or more repeating subunits of 1–5 bp in length as described previously [With the help of MISA  and GraphPad Prism 6.0 . Pearson’s correlation method was used to analyze the correlation between two different genes. We estimated the survival predictive performance of CDCA7 with the help of Kaplan–Meier curve and log-rank test. And univariate and multivariate Cox regression analyses were utilized to assess the correlation between different variables and OS. The nomogram was generated by using the rms package of R software. ROC and AUC were performed to evaluate the prognostic ability of RS by means of the package of “survivalROC” in R. It was considered to be statistically significant that nominal P value < 0.05.We analyzed all of the statistical data and the figures by mean of SPSS 24.0 , R3.3.1  of ccRCC patients Table . As showThen we carried out ROC curve analysis to assess the predictive ability of CDCA7 and other clinicopathologic parameters Fig. c. The AUThough we discovered CDCA7 could act as an independent prognostic factor in ccRCC, how CDCA7 is involved in the ccRCC pathogenesis still remained unclear. We performed GSEA analysis to explore possible mechanisms and signaling pathways through which CDCA7 functioned to regulate ccRCC. On the basis of the normalized enrichment score (NES) and FDR q-val (FDR < 0.01), the most significantly enriched biological pathways were exhibited Table ; Fig. 4,To explore the potential functional interaction of CDCA7, we constructed the PPI network by applying the STRING database and the Cytoscape software. As showed in Fig. + T cell infiltration, CD8+ T cell infiltration, neutrophil infiltration, macrophage infiltration, and dendritic cell infiltration  is a computational method utilized to evaluate whether a prior defined set of genes display statistically significant, consistent differences between two biological states . To explThen we constructed the PPI network of CDCA7 to explore potential protein–protein interaction. It showed that ten genes were significantly functional associated with CDCA7, including SLBP, GINS2, HELLS, UHRF1, MCM2, MCM4, MCM5, NASP, TYMS, and CDC6. Researchers have discovered the important roles of these genes in the physiological process and tumor progression. For instance, SLBP (Stem-loop-binding protein) is evolutionarily conserved and involved in the processing, translation, and degradation of canonical histones mRNAs including H1, H2A, H2B, H3, and H4 . With re+ T cell infiltration, CD8+ T cell infiltration, neutrophil infiltration, macrophage infiltration, and dendritic cell infiltration. Moreover, CDCA7 was significantly correlated with tumor-related immunosuppressive molecules including PDCD1, CD274, CTLA4, BTLA and LAG3. PDCD1 (programmed cell death 1), CTLA-4 (cytotoxic T-lymphocyte-associated antigen 4) and BTLA (B and T lymphocyte attenuator) are members of immunoglobulin-related receptors family associated with various aspects of T cell immune regulation [We also discovered the association of CDCA7 with immunity. CDCA7 was in close connection with the immune infiltration including B cell infiltration, CD4gulation . PDCD1  in TCGA and this might lead to some biased in our conclusions. Thirdly, we currently could not tell the possible mechanism of CDCA7. Is it tumor intrinsic  or extrinsic (related to immune suppression?) or both? Future studies with larger sample sizes and sufficient clinical information were required to correct our results.To conclude, our study elucidated that CDCA7 could act as a favorable prognostic factor for ccRCC. Moreover, apoptosis pathway, cell cycle pathway, JAK-STAT pathway, NOD like receptor pathway, P53 pathway, T cell receptor pathway and toll like receptor pathway might be the primary pathways regulated by CDCA7. Furthermore, CDCA7 could serve as an independent prognostic factor for ccRCC and it was closely related to MSI, TMB, and immunity. Further advanced researches in vivo and in vitro were required to verify our findings.Additional file 1: Table S1: Clinical information of the 16 ccRCC patients.Additional file 2: Figure S1: The expressions of the significantly associated immune checkpoint molecules with CDCA7 in the TCGA dataset.


Renal cell carcinoma (RCC) is a complex disease and is comprised of several histological subtypes, the most frequent of which are clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC) and chromophobe renal cell carcinoma (ChRCC). While lots of studies have been performed to investigate the molecular characterizations of different subtypes of RCC, our knowledge regarding the underlying mechanisms are still incomplete. As molecular alterations are eventually reflected on the pathway level to execute certain biological functions, characterizing the pathway perturbations is crucial for understanding tumorigenesis and development of RCC.In this study, we investigated the pathway perturbations of various RCC subtype against normal tissue based on differential expressed genes within a certain pathway. We explored the potential upstream regulators of subtype-specific pathways with Ingenuity Pathway Analysis (IPA). We also evaluated the relationships between subtype-specific pathways and clinical outcome with survival analysis.In this study, we carried out a pathway-based analysis to explore the mechanisms of various RCC subtypes with TCGA RNA-seq data. Both commonly altered pathways and subtype-specific pathways were detected. To identify the distinctive characteristics of each subtype, we focused on subtype-specific perturbed pathways. Specifically, we observed that some of the altered pathways were regulated by several recurrent upstream regulators which presenting different expression patterns among distinct RCC subtypes. We also noticed that a large number of perturbed pathways were controlled by the subtype-specific upstream regulators. Moreover, we also evaluated the relationships between perturbed pathways and clinical outcome. Prognostic pathways were identified and their roles in tumor development and progression were inferred.In summary, we evaluated the relationships among pathway perturbations, upstream regulators and clinical outcome for differential subtypes in RCC. We hypothesized that the alterations of common upstream regulators as well as subtype-specific upstream regulators work together to affect the downstream pathway perturbations and drive cancer initialization and prognosis. Our findings not only increase our understanding of the mechanisms of various RCC subtypes, but also provide targets for personalized therapeutic intervention. Kidney cancer or renal cell carcinoma (RCC) is comprised of distinct histological subtypes which present different genetic characteristics, biological functions and clinical outcome . RCC conGenomic alterations lead to differential expression of genes and subsequently induce dysregulation of biological functions to promote tumorigenesis and development. To date, numerous studies have been carried out to investigate the molecular basis of each subtype and elucidate potential mechanisms of tumorigenesis –7. The CIn this study, we systematically investigated the mechanisms of various subtypes of RCC at the pathway level, identifying pathway perturbations as well as the potential upstream regulators and exploring the associations with clinical outcome. Previously, Ricketts et al.  re-evaluThe main objective of this study was to investigate the pathway perturbations of various RCC subtypes against normal tissue, identify potential upstream regulators and explore the relationships associated with clinical outcome. The whole workflow was shown in Fig. https://gdac.broadinstitute.org). The histological subtype of each sample were re-divided based on the original pathology reports or re-evaluated by urologic pathologists in Ricketts et al. [http://software.broadinstitute.org/gsea/downloads.jsp) and included 5266 genes and 186 pathways. Demographic and clinical characteristics of the patients are described in Table The RNAseqV2 level 3 data including raw counts data, normalized scaled estimate data, and corresponding clinical data for TCGA Pan-kidney cohort were accessed from the Broad GDAC Firehose  . In partAs genes associated with multiple pathways can be a confounding factor when linking pathway alternations to a specific function, such kind of genes were subjected to a promiscuity correction . Here, wp-value less than 0.05.Furthermore, pathway enrichment analyses were also performed based on corrected Wald t-values of DEGs using the R package ‘piano’ (version 2.0.2) . Here, tp-value < 0.01; (3) The predicted upstream regulators regulated more than 10% DEGs of a pathway in order to identify the drivers most likely affecting this pathway; (4) The predicted upstream regulator itself was also a DEG.To identify the potential upstream regulators of a particular pathway, we used the Ingenuity Pathway Analysis (IPA) software and performed the “Core” and “Upstream Regulator” analyses on IPA  based onp values across each subtype and pathways with p-values less than 0.05 were considered as prognostic pathways.To assess the association between subtype-specific enriched pathways and patient survival information, overall survival (OS) analysis was performed based on pathway expression within each subtype. Here, we calculated the pathway expression based on the DEGs within each pathway. TCGA normalized scaled estimate data were firstly converted into TPM (multiplied by 1e6) value and then subjected to log2(TPM + 1) transformation for subsequent analysis. The expression of each pathway was measured by the average expression of all DEGs inside the pathway based on log2-transformed values. For each subtype-specific enriched pathway, the patient cohort for the corresponding subtype was divided into two groups (i.e. pathway high expression group and pathway low expression group) by average expression of the pathway. Then the Kaplan-Meier estimator was used for patient stratification and the log-rank test was applied to compare the survival difference between two groups with R package ‘survminer’ . A multiExcept where noted above, all statistical analyses were performed in R version 3.5.1.To investigate the pathway perturbations of various RCC subtypes against normal tissues, we analyzed the expression of pathway genes of kidney cancer from TCGA, encompassing 485 ccRCC, 273 PRCC, 81 ChRCC and 129 normal samples   and PRCC  subtype, especially for ccRCC. TNF was also a predominant regulator and controlled multiple altered pathways in PRCC  and ChRCC  subtype, especially for ChRCC. In addition, subtype-specific upstream regulators which affected a large number of perturbed pathways were also observed. IFNG and TGFB1 for ccRCC , SPP1, HDAC1 and CUX1  for PRCC, and STAT3, RB1, NUPR1 and MECP2  for ChRCC. Specifically, the recurrent upstream regulators presented distinct expression patterns among different subtypes of RCC  Upstream Regulator Analysis based on DEGs within a pathway and the potential upstream regulators for each subtype-specific altered pathway were identified  Fig. . TP53 waP < 0.05). ccRCC, PRCC and ChRCC contained 17, 3 and 3 prognostic pathways, respectively   and is cTP63, TP53, TGFB1, IFNG, IL1B and TNF. Furthermore, a few of them are shared among ccRCC, PRCC and ChRCC, while presenting heterogeneous expression patterns among subtypes. In our study, TP63 has been observed to play crucial roles in various RCC subtype and regulated multiply pathways. Tuna et al. have highlighted that TP63 has a close relationship with tumor stage, grade and survival time of RCC [TP53 as a critical tumor suppressor regulated various pathways in ccRCC and PRCC, and is overexpressed within each subtype compared against normal tissue. TP53 protein overexpression is particularly prevalent in RCC and is associated with higher tumor grades and poor survival [TGFB1, IFNG, IL1B and TNF are important proinflammatory cytokines, and have a close relationship with PD-L1 [. TGFB1 is a key cytokine produced by proximal tubular and renal cancer cells, and regulates various vital processes and contributes to tumor progression and aggression in ccRCC [TGFB1 was one of the most active drivers, regulating about 36% perturbed pathways within ccRCC and showing higher expression compared with normal tissue. Ruan et al. have also shown that TGFB1 is overexpressed in ccRCC against normal tissue [IFNG is a major effector of immune therapy of cancer [, and its high expression is correlated with poor prognosis in ccRCC [. In addition, we have also observed that IL1B is upregulated in ccRCC. IL1B is produced by high malignancy ccRCC cells and its high expression has been shown to promote tumor aggressiveness [When we further investigate the potential upstream regulators of subtype-specific altered pathways, several recurrent regulators controlling multiple pathways were identified, such as e of RCC . Besidessurvival . Specifith PD-L1 –30. TGFBin ccRCC , 32 . Inl tissue . IFNG isf cancer , and itsin ccRCC . In addisiveness , 35. TakSince pathway perturbation plays vital roles for cancer progression, we also identified prognostically disturbed pathways. Among these pathway predictors, we found that pathways with lower expression linked to poor outcome were presented in ccRCC. Meanwhile, we also observed that these prognostic pathways exhibited downregulation in ccRCC. Thus, we hypothesize that the alteration of upstream regulators induced the downregulation of such kind of prognostic pathways, thus contributing to the tumorigenesis and progression of ccRCC. Instead, pathways with higher expression linked to poor outcome were present in PRCC and ChRCC. Among these pathway predictors, we noticed that spliceosome and protein export were upregulated in PRCC and ChRCC compared to normal tissue, respectively. Given that frequent splicing and export of protein are necessary events for tumor proliferation, we hypothesize that the overexpression of spliceosome and protein export functions promote tumorigenesis and progression of PRCC and ChRCC, respectively. As for vascular smooth muscle contraction and arrhythmogenic right ventricular cardiomyopathy both of them were downregulated in PRCC compared to normal tissue. Considering the highly vascularized nature of the kidney tissue, we hypothesize that these perturbations reflect a disruption in the biological and physiological functions of the kidney.Despite the extensive observations and results generated from our analysis, some limitations of this study should be noticed as well. First, the mRNA levels of pathway may not represent all the biological function due to lack of large-scale downstream information, such as protein expression levels and modulation  and metabolite abundance. Secondly, there are complex interactions between pathways and they often function together to contribute tumorigenesis and progression. Therefore, pathway perturbation analysis based on individual pathways may only estimate a portion of its activity. Thirdly, while the significantly altered pathways of various subtypes have been defined, further research is needed to explain the underly mechanistic meaning and implications of the altered pathway in the tumorigenic process of RCCs. Last but not least, even though the potential upstream regulators of a certain pathway have been identified and their roles in various subtypes have been confirmed in many recent studies, further confirmation of how changes in upstream regulators affect pathway alterations still requires experimental validation.In summary, we performed a pathway-based analysis among different RCC subtypes, and both the commonly altered pathways and subtype-specific altered pathways were identified. We found that some of the recurrent upstream regulators controlled a few of the altered pathways and exhibited different expression patterns among various RCC subtypes. Each subtype also has its own specific upstream regulators for a large number of perturbed pathways. In addition, prognostic pathways were also identified. We hypothesized that the dysregulation of recurrent upstream regulators as well as subtype-specific upstream regulators work together to affect pathway perturbations and further influence cancer prognosis among various RCC subtypes. Our findings can catalyze a better understanding of the mechanisms of various RCC subtypes and provide promising potential targets for personalized therapeutic intervention.Additional file 1: Table S1. Differentially expressed genes within pathways.Additional file 2: Table S2. Significantly altered pathways of various subtypes.Additional file 3: Table S3. Commonly altered pathways and subtype-specific altered pathways.Additional file 4: Table S4. Overlapped genes in common altered pathways among ccRCC, PRCC and ChRCC.Additional file 5: Table S5. Potential upstream regulators.Additional file 6: Figure S1. An example of the correlations between upstream regulators with the target genes.


A total of 1,059 differentially expressed circRNAs (477 upregulated and 582 downregulated) in autism group was identified by RNA sequencing. The expression of novel_circ_015779 and novel_circ_035247 were detected by real-time PCR. A ceRNA network based on altered circRNAs was established, with 9,715 nodes and 150,408 edges. Module analysis was conducted followed by GO and KEGG pathway enrichment analysis. The top three modules were all correlated with autism-related pathways involving “TGF-beta signaling pathway,” “Notch signaling pathway,” “MAPK signaling pathway,” “long term depression,” “thyroid hormone signaling pathway,” etc. The present study reveals a novel circRNA involved mechanisms in the pathogenesis of autism.Autism is a common disease that seriously affects the quality of life. The role of circular RNAs (circRNAs) in autism remains largely unexplored. We aimed to detect the circRNA expression profile and construct a circRNA-based competing endogenous RNA (ceRNA) network in autism. Valproate acid was used to establish an  Early screening and diagnosis are very important to improve the outcome of autism patients. Unfortunately, primary health care professionals are usually unaware of the early manifestations of autism, and the gold standard diagnostic tools, autism Diagnostic Interview Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS), are time-consuming and expensive. The pathogenesis of autism is associated with genetic and epigenetic alterations  is a multifactorial neurodevelopmental disorder diagnosed mainly during early life onset, which is often combined with attention deficit/hyperactivity disorder, mental disorder, and intellectual disability  are a kind of non-coding RNAs with circular structure, which are rapidly becoming considered as critical regulators of gene expression networks . Exposure to some neurotoxic drugs may induce fetal nervous system development disorders. Valproate acid (VPA) is a widely used drug to induce autism  Adaptation stage: Gently put the tested mice into the device , and let them move freely inside 10 min. (2) Test stage: Take out the test mice that have just adapted, and put them in the test mice and interactive mice (the same strains of mice that have the same sex and age as the test mice that have not been raised in the same cage). The number of social behaviors  and non-social behaviors (investigation) of the mice were recorded within 10 min.−ΔΔCT method.TRIzol reagent  was used to extract total RNA. A reverse transcriptase kit  was used to synthesize first-strand cDNA. Reverse transcription was performed using HiScript® II Q RT SuperMixfor qPCR . Real-time PCR analysis was performed on an ABI step one plus system . Primer sequences were as follows: novel_circ_000430, 5′-ACCCGTCTTCAGTCTCCGT-3′ (forward), 5′-AATATCACCCACACCCTCAGC-3′ (reverse); novel_circ_015779, 5′-CTCTGCCTGGTGCTGGTATTG-3′ (forward), 5′-ATGTAACTCTCTCCCTCCCCTG-3′ (reverse); novel_circ_063340, 5′-GCTTACCGTGGAGATGTTTGAC-3′ (forward), 5′-CGCCTTCTCCAACACCTCA-3′ (reverse); β-actin, 5′-ACCCATTCTCTGTCTCGCAC-3′ (forward), 5′-ATCGTCACCCCCAAAACCTG-3′ (reverse). β-actin was used as an internal control. Target gene expression was analyzed using the 2http://www.rproject.org/) was used to identify differentially expressed circRNAs. CircRNAs with P < 0.05 and |log2FoldChange| > 1 in a comparison between control and autism groups was identified as differentially expressed.RNA sequencing was conducted by Gene Denovo Biotechnology Co. . The circRNA were identified using find_circ   terms in source genes comparing to the genome background were defined by hypergeometric test , Miranda (v3.3a), TargetScan (v7.0), and miRTarBase (v6.1). Then, a ceRNA network was constructed using differentially expressed circRNAs and bioinformatic predication results. The established circRNAs-miRNA-mRNA ceRNA network contains 9,715 nodes  and 150,408 edges. A top 5 miRNA-based circRNA-miRNA-mRNA network is shown in Modules were screened using plug-in MCODE. GO and KEGG pathway enrichments were performed in the top three modules, respectively. Module 1 consists of 393 nodes  and 1,508 edges, which are mainly associated with “HIF-1 signaling pathway,” “arginine and proline metabolism,” “TGF-beta signaling pathway,” “IL-17 signaling pathway,” etc. . Module The incidence of autism is still increasing, which seriously affects the quality of human life. Despite the research progress that has been made in recent years, the pathogenesis mechanisms of autism are still not fully clarified. The development of bioinformatics facilitated the investigation of disease mechanisms and therapeutic strategies , circRNA–protein interaction, or regulate translation (Du et al., RNA sequencing identified a total of 1,059 differentially expressed circRNAs in the autism group. The expression level of novel_circ_015779 and novel_circ_035247 was detected by real-time PCR assay. The RNA sequencing and real-time PCR assay achieved similar results.CircRNAs could act as a ceRNA and thus exert biological functions. Using bioinformatic methods, we predicted the interaction between circRNAs and miRNA and constructed an autism-related ceRNA network. The ceRNA network contains 150,408 edges and 9,715 nodes, including 1,059 circRNAs, 1,926 miRNA, and 6,730 mRNA.We then performed GO and KEGG analysis. The autism-related ceRNA network was signaling pathways including “axon guidance,” “MAPK signaling,” “Hippo signaling pathway,” and “ErbB signaling pathway.” Among these signaling pathways, the role of “axon guidance” and “MAPK signaling” in autism has been proved (Suda et al., The hub nodes, take miR-142-3p, miR-142a-5p, and miR-142b for example, may play an important role in autism. Mor et al.  found thSubsequently, network modules analysis was conducted. The top three modules were all correlated with autism-related pathways including “TGF-beta signaling pathway,” “Notch signaling pathway,” “MAPK signaling pathway,” “long-term depression,” etc. Take “TGF-beta signaling pathway” as an example; TGF-β1 was upregulated in the brain of autistic patients (Vargas et al., Circulating miRNAs were aberrantly expressed in autism individuals compared with that in controls (Mundalil Vasu et al., In conclusion, our study provides the expression profile of circRNAs in a mouse model of autism, which promotes our understanding in the pathogenesis mechanism of autism.GSE163904.The RNA sequencing results for this study could be found in the GEO repository with the accession number of The animal study was reviewed and approved by Harbin children's hospital Animal Care and Use Committee [JJ2017ZR0484].JW, YJ, and WZ designed the study. HW and BL performed the animal experiment. ZY, CC, and YX analyzed the data. JW and YJ wrote the manuscript. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pulmonary neuroendocrine tumors (NETs) are a heterogeneous family of malignancies whose classification relies on morphology and mitotic rate, unlike extrapulmonary neuroendocrine tumors that require both mitotic rate and Ki-67. As mitotic count is proportional to Ki-67, it is crucial to understand if Ki-67 can complement the existing diagnostic guidelines, as well as discover the benefit of these two markers to unravel the biological heterogeneity. In this study, we investigated the association of mitotic rate and Ki-67 at gene- and pathway-level using transcriptomic data in lung NET malignancies. Lung resection tumor specimens obtained from 28 patients diagnosed with NETs were selected. Mitotic rate, Ki-67 and transcriptomic data were obtained for all samples. The concordance between mitotic rate and Ki-67 was evaluated at gene-level and pathway-level using gene expression data. Our analysis revealed a strong association between mitotic rate and Ki-67 across all samples and cell cycle genes were found to be differentially ranked between them. Pathway analysis indicated that a greater number of pathways overlapped between these markers. Analyses based on lung NET subtypes revealed that mitotic rate in carcinoids and Ki-67 in large cell neuroendocrine carcinomas provided comprehensive characterization of pathways among these malignancies. Among the two subtypes, we found distinct leading-edge gene sets that drive the enrichment signal of commonly enriched pathways between mitotic index and Ki-67. Overall, our findings delineated the degree of benefit of the two proliferation markers, and offers new layer to predict the biological behavior and identify high-risk patients using a more comprehensive diagnostic workup. On the other hand, SCLC and LCNEC neoplasms constitute poorly differentiated tumors showing high grade cytologic features, extensive necrosis and mitotic count higher than 10 mitoses per 2 mm2 [Neuroendocrine tumors (NET) are a heterogeneous group of malignancies, which represent around 23% of all lung cancers . Based oer 2 mm2 .In pulmonary NETs, the Ki-67 antigen has been evaluated for its diagnostic, prognostic and grading implications –14. Ki-6Many studies in the literature have shown that the two proliferation markers, mitotic rate and Ki-67 index are strongly correlated , 21–23, The clinical characteristics of patients in the cohort are presented in r = 0.83 . We first examined the association between the two proliferation markers, using the Spearman correlation coefficient, and found a moderate correlation between them (r = 0.55)  . Next, w = 0.55) . The maxFinally, we compared the biological pathways that were associated with the mitotic index and Ki-67. For an FDR < 10%, 311 molecular pathways were enriched using mitotic index, out of which 97 and 214 pathways were positively and negatively correlated with mitotic index, respectively . Similarr = 0.52, between them . Secondly, we found gene-level differences between both the markers, and specifically, the cell-cycle related genes tended to exhibit large variations in the ranking of their correlation strength.For this study, we selected 28 lung NET patients consisting of carcinoids and large cell carcinomas. The proliferation capacity of cancer cells was assessed through two markers, namely, mitotic index and Ki-67 antigen for all the samples. Firstly, we found a strong Spearman correlation between the two proliferation markers  and LCNEC (n = 8). We found moderate correlation  between mitotic index and Ki-67 in carcinoids and in LCNEC, respectively. We observed larger variations in terms of the ranking of cell cycle-related genes in both the subtypes, while the variations were found be larger in the LCNEC group. We hypothesize that these set of proliferation-related genes could potentially be used to characterize the differences or similarities between the two proliferation markers using gene expression data. Using pathway analysis, we were able to show that there is considerable variation in the molecular pathways that were associated with mitotic rate and Ki-67 index across carcinoids and LCNEC. While mitotic index provided a more comprehensive characterization of molecular pathways compared to Ki-67 in carcinoids, Ki-67 elicited a broad characterization of biological pathways compared to mitotic index in the LCNEC group. This reflects that specific pathway properties can explain the inherent biological complexity of these subtypes. Furthermore, to better understand which genes tend to be drive the commonly enriched pathways between mitotic index and Ki-67, we capitalized on the leading-edge genes of a pathway. We found distinct sets of leading-edge genes that drive the biological pathways underpinning the cell cycle kinetics defined by mitotic index and Ki-67 in both the subtypes. Altogether these observations suggest that depending on the histological subtype, either mitotic index or Ki-67 can be used to discover the inherent biological complexity and diversity of these malignancies.Given the disease heterogeneity of lung NET malignancies, it may be difficult to delineate the true extent of benefit of mitotic index and Ki-67 within the histological groups. To investigate this, we examined the association of mitotic index and Ki-67 with the expression profiles across two distinct lung NET subtypes – carcinoids (i) Sample size: due to small sample size of the LCNEC group, the gene-level and pathway-level associations with mitotic rate and Ki-67 index is less robust; ii) Inter-observer variability: there is a considerable interobserver variation in the histopathological characterization of lung NET’s, i.e., in samples with low to medium values of mitotic index [Despite the relative merits of mitotic index and Ki-67, and the degree of correlation between these two biomarkers in different cancer types, less attention has been paid to integrate these two biomarkers in lung NETs. To summarize our findings, we found that mitotic index and Ki-67 markers govern similar set of biological mechanisms (at the pathway-level). Therefore, we hypothesize that integrating both these proliferation indices into the classification framework could potentially be redundant in stratifying lung NET subtypes more effectively. In order to translate these findings into a clinical setting, we will require further validation involving larger cohorts to determine their additive or independent value in a classification setting, which is the focus of future investigations. With regards to the histology-based findings, we hypothesize that either mitotic index and Ki-67 could potentially offer a novel characterization of the biological behavior and identify patients who will benefit from a multi-modal therapeutic regimen. The rational integration of these mutually inclusive proliferation variables within the lung NET subtypes could strengthen the current practices to improve clinical decision making for biologically diverse malignancies. Although our findings are of significant interest from a biological point of view, there were some limitations, which include, ic index . The curhttps://rsr-qc.ca/biobanque/) at the Institut universitaire de cardiologie et de pneumologie de Québec (IUCPQ). The cohort included a total of 28 lung neuroendocrine cases. Among these patients, 10 samples were typical carcinoids, 7 samples were atypical carcinoids and 11 cases were LCNEC based on the 2015 WHO classification. The availability of frozen tissue along with the quality and the amount of extracted RNA were assessed. For carcinoids, we selected the cases such that the spectrum of their mitotic index, i.e., of 0–10 mitoses per 2 mm² is covered. All the original H&E slides were reviewed by two thoracic pathologists to confirm the diagnostic, including assessment of the mitotic count and the Ki-67 index. We used the WHO guidelines to classify the tumors, except for cases with 10–20 mitoses per 2 mm2, as we and other studies have shown that they usually or may exhibit carcinoid-like morphology [The study was performed on the cohort from a single pulmonary pathology reference center with the approval of the institutional ethics committee (#21045). Pulmonary resected cases of lung neuroendocrine tumors diagnosed between 2000 and 2016 were reviewed. These samples were part of the Quebec Respiratory Health Network Tissue Bank  using Spearman correlation. This analysis was done to compare and highlight significant genes that are positively and negatively associated with proliferation markers in carcinoids, LCNEC and in all the samples. This analysis would enable us to identify top genes that changed their ranking in terms of the correlation strength with the two proliferation markers.R package [p-values obtained for each molecular pathway were corrected for multiple testing using the false discovery approach (FDR) [R package.The pathway enrichment analysis on the gene expression data was carried out using the gene set enrichment analysis (GSEA) methodology  with tra package . Nominalch (FDR) , with p.To analyze if the same set of genes were driving a pathway enriched by both mitotic index and Ki-67, we performed the leading-edge gene analysis. GSEA method returns a subset of genes, termed as the leading-edge genes, which drives the enrichment statistic in the pathway analysis. The leading-edge genes were obtained from the enrichment score that is defined by the maximum deviation from zero. This set of leading-edge genes are considered to be of high biological interest due to appearing at higher frequencies among the pathway subsets , which c