BackgroundIn order to facilitate normal gait, toes require to be in a rectus position during the propulsive phase. This requires a correct balance and sequence of activity of the intrinsic musculature of the feet. Alteration of this balance and sequence may lead to the development of claw toes. Atrophy of the lumbricals occurs in the development of claw toes, but it is not known if changes occur in any other intrinsic muscles, including flexor digitorum brevis. This study set out to investigate whether hypertrophic changes were evident in flexor digitorum brevis in feet with claw toes.MethodsFour cadaver feet were investigated, two with rectus toes and two with claw toes. Flexor digitorum brevis was removed from each, and seven anatomically significant tissue sections from each muscle were routinely processed, cut and stained. One hundred and sixty muscle fibre cross sectional areas were measured from each section.ResultsThe mean age of the donors was 81.5 years, and three of the four were female. Results showed that the cross sectional area of fibres from feet with claw toes was 417 μg2 significantly greater (p < 0.01) than the cross sectional area of fibres from feet with rectus toes, which was 263 μg2.ConclusionsAlthough this study has several limitations, preliminary observations reveal that flexor digitorum brevis muscle fibre cross sectional area is significantly reduced in feet with claw toes. This would indicate a relationship between muscle fibre atrophy of flexor digitorum brevis and clawing of the lesser toes.
The extensive tumor microenvironment (TME) in pancreatic adenocarcinoma (PAD) modulates cancer progression and impact prognosis. Although gene analysis has enhanced understanding of cancer biology, few models exist to model prognosis in association with mRNA expression in the TME. Clinical outcomes data and mRNA-seq of 156 and 64 patients (pts) were obtained from TCGA and Bailey at el. [1] for testing and validation, respectively. Expressivity of 191 genes enriched in cellular and structural elements of TME and clinical data were analyzed by multivariate nonlinear regression aided by machine learning for confined optimization with model-data error minimization. Statistics including Kaplan-Meier (KM), Cox Hazard (CH), and correlation analysis was used. Most pts (85.89% and 85.94%, respectively) were in stage II, and pts in stage I/III/IV were excluded. Prognostication was modeled with higher risk score (RS) representing worse prognosis: RS = -7.6526 x (Age-5.5679) + 0.0813 x (P/G0.3677) + 0.7069, where P/G is a ratio of genes associated with poor to good prognosis (Table 1). Based on RS, pts were clustered into 2 groups (high and low RS) with 2 KM curves showing p<0.0001 and p=0.014 in test and validation sets. Immune profiling of high and low RS groups in both test and validation sets shows that in low RS group, genes related to both immune activation (IA) and inhibition (II) (Table 2) are highly co-expressed, implying that co-expression of IA and II contributes to PAD’s poor prognosis even in pts with immune system activation. In high RS group, genes related to cancer stem cells (CD44 and EPCAM) significantly contributed to poor prognosis. Machine learning-assisted modeling of RS and gene analysis suggest that IA genes are suppressed by co-expression of high degree of II, contributing to poor prognosis in PAD. RS enables prognostication of pts encountered in the clinic when genomic profiles are provided. [1] Nature 531, 47-52 (2016). Table 1genes associated with good and poor prognosis out of 191 genes (identification via KM and CH with p<0.05)Good prognosisFCRL3, LILRA4, IL3RA, IL10, CCL22, DOK3, CXCR4, PDGFA, ICOSLG, TNFRSF4Poor prognosisTNFSF10, CD44 Table 2gene groups of immune activation (IA) and immune inhibition (II)IA gene groupscytotoxic T, B, NK, T-helper 1 cells, IFN, cytolytic activity, T cell co-stimulation, and antigen presentationII gene groupsregulatory T cells, desmoplasia, immunosuppressive chemokines, immune checkpoints, angiogenesis, cancer stem cells, epithelial-mesenchymal transition, and neutrophils Note: This abstract was not presented at the meeting. Citation Format: Sunyoung S. Lee, Seok Joon Kwon, Ahmed Elkhanany, Andrew Baird, Seongwon Lee, Jillian Dolan, Stuart Baird, Shinyoung Park, Renuka Iyer. Modeling of prognostication and immune profiling, based on genomic analysis in the tumor microenvironment of pancreatic adenocarcinoma via machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 136.
608 Background: Stromal elements in the tumor microenvironment (TME) impact prognosis and response to therapy. Advances in mRNA-seq improved understanding of gene expressivity, but few models exist to model prognosis in association with mRNA expression. Methods: Clinical data and mRNA-seq of 1,715 patients (pts) – pancreatic adenocarcinoma (PAAD), colorectal adenocarcinoma (CRC), hepatocellular carcinoma (HCC), gastric adenocarcinoma (GAAD), esophageal adenocarcinoma (EsoAd), and esophageal squamous cell carcinoma (EsoSCC) – were obtained from TCGA. The expressivity of 191 genes enriched in cellular and structural components of the TME and clinical data were analyzed using machine learning, multivariable COX model, and Kaplan-Meier (KM) analysis to model risk score (RS) to predict prognosis. Results: Genes associated with good and poor prognosis were identified via machine learning and statistic methods. Higher RS represents worse prognosis with max RS = 1 (Table). In all 6 cancers, high P/G (the expression ratio of genes associated with poor to good prognosis) and old age are related to worse survival except EsoAd with younger pts having worse prognosis. The location of tumors in CRC and sex in HCC impact RS. When pts are grouped into 3 pt groups in each cancer, KM curves in pts with low, intermediate, and high RS are statistically different (p < 0.0001) with high hazard ratio (HR > 2). Conclusions: Analysis of large data was assisted by machine learning and statistics, identifying genes associated with survival and creating RS as a tool to predict prognosis. This provides valuable information about prognosis for pts encountered in the clinic when genomic profiles are given. Computational modeling to predict response to chemotherapy and immunotherapy is underway. [Table: see text]
8544 Background: The tumor microenvironment (TME) influences prognosis and response to therapy. The correlation between immune profiles in the TME and cancer DNA mutations is not well established. Methods: Clinical outcomes data, mRNA-seq, and DNA mutation of 480 patients (pts) with lung adenocarcinoma (LAD) were obtained from TCGA. Pts were clustered into 4 groups using unsupervised machine learning, based on mRNA expression of genes related to antigen presentation (AP) and cytolytic activity (CA): group (G) 1 with high AP and CA (52 pts); G2, high AP, low CA (82); G3, low AP, high CA (66); G4, low AP and CA (280). Analysis of the immune landscape was performed using mRNA-seq of 191 genes enriched in cellular and structural elements of TME. DNA mutations were analyzed using the R package ggpubr and correlated in G1-G4. Results: Pts in G1 have high expression of genes related to immune activation (IA) and decreased expression of immune suppression (IS) and have the best prognosis. Pts in G2 have intermediate prognosis with decreased IA genes and intermediate expression of genes related to IS and immune checkpoints. Pts in G3 have the worst prognosis with very high expression of genes related to immune checkpoints, desmoplasia, T cell co-inhibition, and IS. They also have low CD39 expression implying low cancer antigen-driven T cells. Pts in G4 have intermediate prognosis with highly depressed IA genes. Out of 70,199 non-synonymous mutations, the top 50 mutated genes in each pt group were identified: 36, 26, 31, and 17 DNA mutations were only found in G1, G2, G3, and G4 (refer to presentation). EGFR mutation was only found in G2; KRAS in G2/4; TP53 in G2/3/4. Conclusions: Our correlation analysis of mRNA-seq and DNA mutation shows that the immune landscape of TME can predict DNA mutations and prognosis. It further demonstrates a close connection between DNA mutations and changes in TME mRNA expressivity which appear to have valuable prognostication potential in the clinical setting with now widely available genomic testing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.