Identification of gene co-expression modules and hub genes associated with the invasiveness of pituitary adenoma

Zhou, Yuancheng; Fu, Xiaorui; Zheng, Zhicheng; Ren, Yu; Zheng, Zijian; Zhang, Bohan; Yuan, Min; Duan, Jian; Li, Meihua; Hong, Tao; Lu, Guohui; Zhou, Dongwei

doi:10.1007/s12020-020-02316-2

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…Raw data from the 3 datasets were normalized using “sva” package in R software (version 3.5.2). [ 21 , 22 ] Datasets were then grouped classified into 2 groups: unruptured intracranial aneurysms (UIA) and RIA groups after removal of batch effects from merged data. Differentially expressed genes (DEGs) between the 2 groups were revealed using “limma” package in R statistical software.…”

Section: Methodsmentioning

confidence: 99%

Profiling of immune infiltration landscape of ruptured intracranial aneurysm

Li,

Su,

et al. 2024

Medicine

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Background: Previous research has indicated that the rupture of intracranial aneurysm (IA) is a significant contributor to mortality from stroke. The objective of this present study was to examine the infiltration patterns in ruptured intracranial aneurysm (RIA), with the aim of generating insights that could inform the development of effective immunotherapeutic approaches. Methods: To achieve this, we obtained Gene Expression Omnibus datasets pertaining to ruptured aneurysms, encompassing a total of 19 unruptured intracranial aneurysms (UIA) and 27 RIA. Subsequently, we conducted differential gene analysis and immune cell analysis specifically for the RIA. Results: According to the conducted studies, the analysis has identified 10 hub genes within key modules. Through the utilization of Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology terms analyses, it has been established that genes exhibiting differential expression are associated with immune cell infiltration in the aneurysm wall. Furthermore, the implementation of the CIBERSORT algorithm has revealed that there are 22 distinct immune cells between RIA and tissues of UIA. IA samples contained a higher proportion of macrophages M1, mast cells resting, and CD4 naive T cells, while macrophages M0 and neutrophils were relatively lower in RIA compared with those in UIA. Conclusion: The current study initially identified highly conservative hub genes and immune cell infiltration patterns in IA. Data presented in the current study improved understanding of immune genes that drive IA which can be exploited in development of effective immunotherapies.

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Section: Methodsmentioning

confidence: 99%

Profiling of immune infiltration landscape of ruptured intracranial aneurysm

Li,

Su,

et al. 2024

Medicine

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“…Zhang et al (45) further distinguish the invasive adenomas as having significantly greater tumor diameters and volumes. In pituitary adenomas (PA), invasiveness has been shown to be the main contributing factor to recurrence and poor prognosis (95). Most of the early studies focused on using the invasiveness of the lesion as an imaging marker for prognosis; however, subsequent to this, other imaging biomarkers that can be extracted from more modern imaging techniques were assessed with increasing interest.…”

Section: Overall Assessmentmentioning

confidence: 99%

Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

et al. 2023

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Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity.Systematic Review Registration: PROSPERO, CRD42022306922

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