Shared network pattern of lung squamous carcinoma and adenocarcinoma illuminates therapeutic targets for non-small cell lung cancer.
Shared network pattern of lung squamous carcinoma and adenocarcinoma illuminates therapeutic targets for non-small cell lung cancer.
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肺鳞癌和腺癌的共享网络模式阐明了非小细胞肺癌的治疗靶点
DOI：
10.3389/fsurg.2022.958479
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发表时间：
2022
影响因子：
1.8
通讯作者：
Ma L
中科院分区：
文献类型：
--
作者：
Li P;Kuang X;Zhang T;Ma L
Non-small cell lung cancer (NSCLC) is a malignant tumor with high mortality. Lung squamous carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the common subtypes of NSCLC. However, how LUSC and LUAD are compatible remains to be elucidated. We used a network approach to find highly interconnected genes shared with LUSC and LUAD, and we then built modules to assess the degree of preservation between them. To quantify this result, Z-scores were used to summarize the interrelationships between LUSC and LUAD. Furthermore, we correlated network hub genes with patient survival time to identify risk factors. Our findings provided a look at the regulatory pattern for LUSC and LUAD. For LUSC, several genes, such as AKR1C1, AKR1C2, and AKR1C3, play key roles in regulating network modules of cell growth pathways. In addition, CCL19, CCR7, CCL21, and LY9 are enriched in LUAD network modules of T lymphocyte-related pathways. LUSC and LUAD have similar expressed gene expression patterns. Their networks share 46 hub genes with connectivity greater than 0.9. These genes are correlated with patient survival time. Among them, the expression level of COL5A2 in LUSC and LUAD is higher than that in normal tissues, which is closely related to the poor prognosis of LUSC and LUAD patients. LUSC and LUAD share a network pattern. COL5A2 may be a risk factor in poor prognosis in LUSC and LUAD. The common landscape of LUSC and LUAD will help better define the regulation of NSCLC candidate genes and achieve the goals of precision medicine.
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影响因子：
9
作者：
Liu M;Zhang Y;Zhang J;Cai H;Zhang C;Yang Z;Niu Y;Wang H;Wei X;Wang W;Gao P;Li H;Zhang J;Sun G
通讯作者：
Sun G
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
14.9
作者：
Davis AP;Grondin CJ;Johnson RJ;Sciaky D;McMorran R;Wiegers J;Wiegers TC;Mattingly CJ
通讯作者：
Mattingly CJ
DOI：
10.1186/s13046-015-0268-9
发表时间：
2015-12-15
期刊：
Journal of experimental & clinical cancer research : CR
影响因子：
--
作者：
Shi M;Chen D;Yang D;Liu XY
通讯作者：
Liu XY
影响因子：
5.6
作者：
Dong, Hui-Xing;Wang, Ren;Pan, Jing
通讯作者：
Pan, Jing