Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer

Background Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten women’s lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets. Methods Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates. Results Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression. Conclusion These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.

Developmental timing scales wing patterning between avian species

How development is timed between differently sized species is a fundamental question in biology. To address this problem, we compared wing development in the quail and the larger chick. We reveal that developmental timing is faster in the quail than in the chick, and is associated with pattern specification, proliferation, organiser duration, differentiation and apoptosis. However, developmental timing is independent of the growth rate, which is equivalent between both species, and therefore scales pattern to the size of the wing. We reveal that developmental timing can be either maintained or reset in interspecies tissue grafts, and we implicate retinoic acid as the resetting signal. Accordingly, retinoic acid can switch species developmental timing and rescale pattern, both between the quail and chick, and the chick and the larger turkey. We suggest that the scaling of pattern to wing bud size is achieved by the modulation of developmental timing against a comparable rate of growth.Summary- We show that developmental timing scales wing patterning

LPCAT1 is a Prognostic Biomarker and Correlated with Tumor Microenvironment in Endometrial Cancer

Background: Endometrial cancer (EC) is one of the three malignant reproductive tumors threatening women’s life and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes including cancer. Immune-infiltration of the tumor microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPLs-related factors associated with TME in endometrial cancer can aid in the prognosis of patients and provide new therapy targets. Methods: Differentially expressed GPLs-related genes were identified from TCGA-UCEC datasets and Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPLs-related genes with prognostic values. Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to find out genes associated with TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of differentiation analysis. GO and KEGG enrichment analyses were performed to explore the underlying mechanism. In addition, we used ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates.Results: Twenty-three differentially expressed GPLs-related genes were identified and eleven prognostic genes were selected by Univariate Cox regression analysis. Four significant genes were found out by two different algorithms and PPI network. Only LPCAT1 was significantly correlated with tumor microenvironment. Then, we found that LPCAT1 was highly expressed in tumors samples compared with normal tissues, and lower survival rates was along with high expression groups of LPCAT1. Moreover, the expression of LPCAT1 was positively correlated with histologic grades and types. ROC curve indicated LPCAT1 had good accuracy of prognostic value. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using ssGSEA algorithm, fifteen kinds of tumor infiltrating cells (TICs) were found correlated with LPCAT1 expression.Conclusion: These findings suggested that LPCAT1 was a valuable prognostic biomarker and correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options and improved treatment of EC.

Genome-wide measurement of spatial expression in patterning mutants of Drosophila melanogaster

Genome sequencing has become commonplace, but the understanding of how those genomes ultimately specify cell fate during development is still elusive. Extrapolating insights from deep investigation of a handful of developmentally important Drosophila genes to understanding the regulation of all genes is a major challenge. The developing embryo provides a unique opportunity to study the role of gene expression in pattern specification; the precise and consistent spatial positioning of key transcription factors essentially provides separate transcriptional-readout experiments at a critical point in development.We cryosectioned and sequenced mRNA from single Drosophila melanogaster embryos at the blastoderm stage to screen for spatially-varying regulation of transcription. Expanding on our previous screening of wild type embryos, here we present data from dosage mutants for key maternally provided regulators, including depletion of zelda and hunchback and both over-expression and depletion of bicoid. These data recapitulate all of the expected patterning changes driven by these regulators; for instance, we show spatially-confined up-regulation of expression in the bicoid over-expression condition, and down-regulation of those genes in the bicoid knock-down case, consistent with bicoid’s known function as an anterior-localized activator.Our data highlight the role of combinatorial regulation of patterning gene expression. When comparing changes in multiple conditions, genes responsive to one mutation tend to respond to other mutations in a similar fashion. Furthermore, genes that respond differently to these mutations tend to have more complex patterns of TF binding.