Publications by authors named "Tianfei Yi"

Congenital heart disease (CHD) represents a significant population warranting particular attention concerning vaccination coverage. To comprehend the vaccination status of CHD within Yinzhou District, Ningbo City, China, and to facilitate the formulation of preventive, control, and immunization strategies against vaccine-preventable diseases in children with congenital heart conditions. Using the China Yinzhou Electronic Health Record Study (CHERRY) database, we analyzed the vaccination coverage of children with CHD born between January 1, 2016 and September 20, 2021, and analyzed the influencing factors associated with the level of vaccination coverage.

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Objectives: Despite the increasingly modern surgical techniques in the oncology field, the factors that influence postoperative prognosis in patients with hypopharyngeal and laryngeal carcinoma (HLC) remain unclear. The study aimed to evaluate the factors influencing the prognosis of HLC patients with pathological diagnosis of squamous cell carcinoma, and the findings are intended to direct follow-up management strategies.

Methods: A retrospective cohort study was performed.

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Enhancer RNAs (eRNAs) are a subclass of long noncoding RNAs (lncRNAs) that have a wide effect in human tumors. However, the systematic analysis of potential functions of eRNAs-related genes (eRGs) in colon cancer (CC) remains unexplored. In this study, a total of 8231 eRGs including 6236 protein-coding genes and 1995 lncRNAs were identified in CC based on the multiple resources.

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Left-sided colon cancer (LCC) and right-sided colon cancer (RCC) have distinct characteristics in tumor immune microenvironment (TIME). Although existing studies have shown a strong association between gene mutations and TIME, whether the regulatory mechanisms between gene mutations and TIME are different between RCC and LCC is still unclear. In this study, we showed the fractions of CD8+ T cells were higher while those of regulatory T cells were lower in RCC.

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The CRISPR/Cas system has stood in the center of attention in the last few years as a revolutionary gene editing tool with a wide application to investigate gene functions. However, the labor-intensive workflow requires a sophisticated pre-experimental and post-experimental analysis, thus becoming one of the hindrances for the further popularization of practical applications. Recently, the increasing emergence and advancement of the methods play a formidable role to support and boost experimental work.

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Lung cancer has been the focus of attention for many researchers in recent years for the leading contribution to cancer-related death worldwide, in which lung adenocarcinoma (LUAD) is the most common histological type. However, the potential mechanism behind LUAD initiation and progression remains unclear. Aiming to dissect the tumor microenvironment of LUAD and to discover more informative prognosis signatures, we investigated the immune-related differences in three types of genetic or epigenetic characteristics (expression status, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and both the immune-related metabolic and neural systems by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal.

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Background: Accumulated evidences indicate that long non-coding RNAs (lncRNAs) participate in many biological mechanisms. Moreover, it acts as an essential regulator in various human diseases such as gastric cancer (GC). Nevertheless, the comprehensive regulatory roles and clinical significance of most lncRNAs in GC are not fully understood.

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Motivation: Long noncoding RNA (lncRNA) has been verified to interact with other biomolecules especially protein-coding genes (PCGs), thus playing essential regulatory roles in life activities and disease development. However, the inner mechanisms of most lncRNA-PCG relationships are still unclear. Our study investigated the characteristics of true lncRNA-PCG relationships and constructed a novel predictor with machine learning algorithms.

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