BackgroundHepatocellular carcinoma (HCC) is an aggressive and heterogeneous disease characterized by high morbidity and mortality. The liver is the vital organ that participates in tyrosine catabolism, and abnormal tyrosine metabolism could cause various diseases, including HCC. Besides, the tumor immune microenvironment is involved in carcinogenesis and can influence the patients’ clinical outcomes. However, the potential role of tyrosine metabolism pattern and immune molecular signature is poorly understood in HCC.MethodsGene expression, somatic mutations, copy number variation data, and clinicopathological information of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. GSE14520 from the Gene Expression Omnibus (GEO) databases was used as a validation dataset. We performed unsupervised consensus clustering of tyrosine metabolism-related genes (TRGs) and classified patients into distinct molecular subtypes. We used ESTIMATE algorithms to evaluate the immune infiltration. We then applied LASSO Cox regression to establish the TRGs risk model and validated its predictive performance.ResultsIn this study, we first described the alterations of 42 TRGs in HCC cohorts and characterized the clinicopathological characteristics and tumor microenvironmental landscape of the two distinct subtypes. We then established a tyrosine metabolism-related scoring system and identified five TRGs, which were highly correlated with prognosis and representative of this gene set, namely METTL6, GSTZ1, ADH4, ADH1A, and LCMT1. Patients in the high-risk group had an inferior prognosis. Univariate and multivariate Cox proportional hazards regression analysis also showed that the tyrosine metabolism-related signature was an independent prognostic indicator. Besides, receiver operating characteristic curve (ROC) analysis demonstrated the predictive accuracy of the TRGs signature that could reliably predict 1-, 3-, and 5-year survival in both TCGA and GEO cohorts. We also got consistent results by performing clone formation and invasion analysis, and immunohistochemical (IHC) assays. Moreover, we also discovered that the TRGs signature was significantly associated with the different immune landscapes and therapeutic drug sensitivity.ConclusionOur comprehensive analysis revealed the potential molecular signature and clinical utilities of TRGs in HCC. The model based on five TRGs can accurately predict the survival outcomes of HCC, improving our knowledge of TRGs in HCC and paving a new path for guiding risk stratification and treatment strategy development for HCC patients.

Lung adenocarcinoma featured as mixed ground-glass opacity (mGGO) doubled its volume half of the time in comparison with that featured as pure ground-glass opacity (pGGO). The mechanisms underlying the heterogeneous appearance of mGGO remain elusive. In this study, we macro-dissected the solid (S) components and ground-glass (GG) components of mGGO and performed single-cell sequencing analyses of six paired components from three mGGO patients. A total of 19,391 single-cell profiles were taken into analysis, and the data of each patient were analyzed independently to obtain a common alteration. Cancer cells and macrophages were the dominant cell types in the S and GG components, respectively. Cancer cells in the S components, which showed relatively malignant phenotypes, were likely to originate from both the GG and S components and monitor the surrounding tumor microenvironment (TME) through an intricate cell interaction network. SPP1hi macrophages were enriched in the S components and showed increased activity of chemoattraction, while macrophages in the GG components displayed an active antimicrobial process with a higher stress-induced state. In addition, the CD47–SIRPA axis was demonstrated to be critical in the maintenance of the GG components. Taken together, our study unraveled the alterations of cell components and transcriptomic features between different components in mGGOs.

Breast cancer is a malignancy with the highest incidence and mortality in women worldwide. Senescence is a model of arrest in the cell cycle, which plays an important role in tumor progression, while the prognostic value of cellular senescence-related genes (SRGs) in evaluating immune infiltration and clinical outcomes of breast cancer needs further investigation. In the present study, we identified two distinct molecular subtypes according to the expression profiles of 278 SRGs. We further explored the dysregulated pathways between the two subtypes and constructed a microenvironmental landscape of breast cancer. Subsequently, we established a senescence-related scoring signature based on the expression of four SRGs in the training set (GSE21653) and validated its accuracy in two validation sets (GSE20685 and GSE25055). In the training set, patients in the high-risk group had a worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed that risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The prognostic value of this signature was further confirmed in the validation sets. We also observed that a lower risk score was associated with a higher pathological response rate in patients with neoadjuvant chemotherapy. We next performed functional experiments to validate the results above. Our study demonstrated that these cellular senescence patterns effectively grouped patients at low or high risk of disease recurrence and revealed their potential roles in the tumor–immune–stromal microenvironment. These findings enhanced our understanding of the tumor immune microenvironment and provided new insights for improving the prognosis of breast cancer patients.