Performance indicators of rural public health system coverage in 2008, 2009 and 2010 by region.

*<p>Data are percentages (SD). # Data are numbers (SD).</p

Socio-demographic characteristics of hypertension participants and women in rural China.

<p>Socio-demographic characteristics of hypertension participants and women in rural China.</p

Performance indicators of rural public health services' equality in 2010 by region (hypertension patients).

*<p>Data are numbers (%).</p><p># Positive results of OR are not received healthcare regularly. An OR ranging from 0.9 to 1.1 indicates that the public health services in the two groups are equal. A value of 1 represents perfect equality. ORs ranging from 0.7 to 0.8 or from 1.2 to 1.4 indicate relative equal. ORs ranging from 0.4 to 0.6 or from 1.5 to 2.9 indicate relatively unequal. ORs beyond 0.3 or 3.0 indicate totally unequal.</p

Indicators of equality of public health services and associated measurement strategies.

*<p>Effective management means that a child has taken all immunization vaccination at his/her age and has undergone physical examination at least once in the recent year.</p><p>#People received sufficient follow-up visits according to the National Basic Public Health Service Specifications.</p

Socio-demographic characteristics of children aged under six and adults older than 65 in rural China.

<p>Socio-demographic characteristics of children aged under six and adults older than 65 in rural China.</p

Performance indicators of rural public health services' equality in 2010 by region (women).

*<p>Data are numbers (%).</p><p># Positive results of OR are not received postnatal care regularly. An OR ranging from 0.9 to 1.1 indicates that the public health services in the two groups are equal. A value of 1 represents perfect equality. ORs ranging from 0.7 to 0.8 or from 1.2 to 1.4 indicate relative equal. ORs ranging from 0.4 to 0.6 or from 1.5 to 2.9 indicate relatively unequal. ORs beyond 0.3 or 3.0 indicate totally unequal.</p

Image2_Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer.TIF

Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed.Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns.Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8+ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset.Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.</p

Image3_Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer.TIF

Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed.Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns.Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8+ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset.Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.</p

DataSheet3_Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer.CSV

Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed.Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns.Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8+ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset.Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.</p

Image1_Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer.TIF

Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed.Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns.Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8+ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset.Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.</p