Portraying the dark side of endogenous IFN-λ for promoting cancer progression and immunoevasion in pan-cancer.

BACKGROUND: IFN-λ has been shown to have a dual function in cancer, with its tumor-suppressive roles being well-established. However, the potential existence of a negative ''tumor-promoting'' effect of endogenous IFN-λ is still not fully understood. METHODS: We conducted a comprehensive review and analysis of the perturbation of IFN-λ genes across various cancer types. Correlation coefficients were utilized to examine the relationship between endogenous IFN-λ expression and clinical factors, immune cell infiltration, tumor microenvironment, and response to immunotherapy. Genes working together with IFN-λ were obtained by constructing the correlation-based network related to IFN-λ and the gene interaction network in the KEGG pathway and IFN-λ-related genes obtained from the networks were integrated as candidate markers for the prognosis model. We then applied univariate and multivariate COX regression models to select cancer-specific independent prognostic markers associated with IFN-λ and to investigate risk factors for these genes by survival analysis. Additionally, computational methods were used to analyze the transcriptome, copy number variations, genetic mutations, and methylation of IFN-λ-related patient groups. RESULT: Endogenous expression of IFN-λ has been linked to poor prognosis in cancer patients, with the genes IFN-λ2 and IFN-λ3 serving as independent prognostic markers. IFN-λ acts in conjunction with related genes such as STAT1, STAT2, and STAT3 to affect the JAK-STAT signaling pathway, which promotes tumor progression. Abnormalities in IFN-λ genes are associated with changes in immune checkpoints and immune cell infiltration, which in turn affects cancer- and immune-related pathways. While there is increased immune cell infiltration in patients with IFN-λ expression, this does not improve survival prognosis, as T-cell dysfunction and an inflammatory environment are also present. The amplification of IFNL2 and IFNL3 copy number variants drives specific endogenous expression of IFN-λ in patients, and those with this specific expression have been found to have more mutations in the TP53 gene and lower levels of DNA methylation. CONCLUSION: Our study integrated multi-omics data to provide a comprehensive insight into the dark side of endogenous IFN-λ, providing a fundamental resource for further discovery and therapeutic exploration in cancer.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer.

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

Integration of multi-omics data reveals a novel hybrid breast cancer subtype and its biomarkers.

Tumor heterogeneity in breast cancer hinders proper diagnosis and treatment, and the identification of molecular subtypes may help enhance the understanding of its heterogeneity. Therefore, we proposed a novel integrated multi-omics approach for breast cancer typing, which led to the identification of a hybrid subtype (Mix_Sub subtype) with a poor survival prognosis. This subtype is characterized by lower levels of the inflammatory response, lower tumor malignancy, lower immune cell infiltration, and higher T-cell dysfunction. Moreover, we found that cell-cell communication mediated by NCAM1-FGFR1 ligand-receptor interaction and cellular functional states, such as cell cycle, DNA damage, and DNA repair, were significantly altered and upregulated in patients with this subtype, and that such patients displayed greater sensitivity to targeted therapies. Subsequently, using differential genes among subtypes as biomarkers, we constructed prognostic risk models and subtype classifiers for the Mix_Sub subtype and validated their generalization ability in external datasets obtained from the GEO database, indicating their potential therapeutic and prognostic significance. These biomarkers also showed significant spatially variable expression in malignant tumor cells. Collectively, the identification of the Mix_Sub breast cancer subtype and its biomarkers, based on the driving relationship between omics, has deepened our understanding of breast cancer heterogeneity and facilitated the development of breast cancer precision therapy.