Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, 113-8602 Tokyo, Japan.
Front Biosci (Landmark Ed). 2023 Jun 28;28(6):125. doi: 10.31083/j.fbl2806125.
Ferroptosis is an iron-dependent programmed cell death mode induced by the toxic buildup of phospholipid peroxidation. Although it is known to affect the initiation and growth of tumors, the association between ferroptosis-related genes (FRGs) and small cell lung cancer (SCLC) has yet to be established.
We used the gene expression omnibus (GEO) and ferroptosis database (FerrDb) to acquire information on SCLC and its associated FRGs. Marker genes were subsequently identified using Least Absolute Shrinkage and Selection Operator (LASSO) and support vector machine recursive feature eilmination (SVM-RFE) algorithms and analyzed for single-gene function and pathway enrichment. Using the drug-gene interaction database (DGIdb), we identified forty drugs targeting six marker genes. The competing endogenous RNA (ceRNA) network revealed the regulation pattern for long non-coding RNA (LncRNA)-microRNA (miRNA)-messenger RNA (mRNA) based on marker genes.
Six differentially expressed FRGs (, , , , , and ) were identified as marker genes with accurate diagnostic capabilities. According to single-gene function and pathway enrichment analyses, these marker genes may be involved in immunomodulation and the cell cycle, as well as numerous pathways connected to tumorigenesis, including the JAK-STAT and PPAR signal pathways. In addition, CIBERSORT analysis showed that and expression may affect the immune microenvironment in SCLC.
We confirmed the accuracy of marker genes for the diagnosis of SCLC using a logistic regression model, thus providing further opportunities to study SCLC-related mechanisms. The accuracy of these results for the diagnosis of SCLC must now be confirmed by further research prior to clinical application.
铁死亡是一种由脂质过氧化毒性堆积诱导的铁依赖性程序性细胞死亡方式。尽管已知其会影响肿瘤的发生和生长，但铁死亡相关基因（FRGs）与小细胞肺癌（SCLC）之间的关联尚未确定。
我们使用基因表达综合数据库（GEO）和铁死亡数据库（FerrDb）获取 SCLC 及其相关 FRGs 的信息。随后使用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）算法识别标记基因，并分析其单基因功能和通路富集。利用药物-基因相互作用数据库（DGIdb），我们鉴定了针对六个标记基因的四十种药物。竞争内源性 RNA（ceRNA）网络根据标记基因揭示了长链非编码 RNA（LncRNA）-微小 RNA（miRNA）-信使 RNA（mRNA）的调控模式。
六个差异表达的 FRGs（，，，，，和）被确定为具有准确诊断能力的标记基因。根据单基因功能和通路富集分析，这些标记基因可能参与免疫调节和细胞周期，以及与肿瘤发生相关的许多途径，包括 JAK-STAT 和 PPAR 信号通路。此外，CIBERSORT 分析表明和表达可能影响 SCLC 中的免疫微环境。
我们使用逻辑回归模型证实了标记基因诊断 SCLC 的准确性，从而为研究 SCLC 相关机制提供了更多机会。这些结果对 SCLC 诊断的准确性必须通过进一步的临床应用前研究来确认。
