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Non-small cell lung cancer (NSCLC) remains one of the deadliest malignant diseases, with high incidence and mortality worldwide. The insulin-like growth factor (IGF) axis, consisting of IGF-1, IGF-2, related receptors (IGF-1R, -2R), and high-affinity binding proteins (IGFBP 1–6), is associated with promoting fetal development, tissue growth, and metabolism. Emerging studies have also identified the role of the IGF axis in NSCLC, including cancer growth, invasion, and metastasis. Upregulation of IGE-1 and IGF-2, overexpression of IGF-1R, and dysregulation of downstream signaling molecules involved in the PI-3K/Akt and MAPK pathways jointly increase the risk of cancer growth and migration in NSCLC. At the genetic level, some noncoding RNAs could influence the proliferation and differentiation of tumor cells through the IGF signaling pathway. The resistance to some promising drugs might be partially attributed to the IGF axis. Therapeutic strategies targeting the IGF axis have been evaluated, and some have shown promising efficacy. In this review, we summarize the biological roles of the IGF axis in NSCLC, including the expression and prognostic significance of the related components, noncoding RNA regulation, involvement in drug resistance, and therapeutic application. This review offers comprehensive understanding of NSCLC and provides insightful ideas for future research.

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Background LUAD is one of the most common malignancies worldwide. This study aimed to construct an immunogenic cell death (ICD)-related long non-coding RNA (lncRNA) signature to effectively predict the prognosis of LUAD patients. Methods The RNA-sequencing and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to screen the ICD-related lncRNAs associated with prognosis. Then, least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox proportional hazard regression analysis were utilized to construct an ICD-related lncRNA signature. The reliability of the signature was evaluated in the training, validation and whole cohorts. In addition, the differences in the immune landscape and drug sensitivity between the low-risk and high-risk groups were analyzed. Finally, reverse transcription quantitative PCR (RT-qPCR) was used to detect the expression level of the selected ICD-related lncRNAs in cell lines. Results A signature consisting of 5 ICD-related lncRNAs was constructed. Kaplan-Meier (K-M) survival analysis showed that the overall survival (OS) of patients in the high-risk group was significantly shorter than that of patients in the low-risk group. The receiver operating characteristic (ROC) curves showed that the signature had good predictive ability. Multivariate Cox regression analysis revealed that the signature was an independent prognostic factor in LUAD. Moreover, the high-risk group had a lower level of antitumor immunity and was less sensitive to some chemotherapeutics and targeted drugs. Finally, the expression level of the selected ICD-related lncRNAs was validated in cell lines by qPCR. Conclusions In this study, an ICD-related lncRNA signature was constructed, which could accurately predict the prognosis of LUAD patients and guide clinical treatment.