Background: Hepatocellular carcinoma (HCC) stands out as one of the most lethal cancers globally, given its complexity, recurrence following surgical resection, metastatic potential, and inherent heterogeneity. In recent years, researchers have systematically elucidated the significance of long non-coding RNA (lncRNA) in the initiation and progression of HCC. The introduction of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases has significantly enhanced the prognostic assessment of HCC. However, the association between HCC and cell senescence has been infrequently explored in the literature. Method: We downloaded liver hepatocellular carcinoma (LIHC)-related messenger RNA and lncRNA expression levels from TCGA. Correlation analysis, Cox regression, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to validate the lncRNA risk model associated with cellular aging. Comparing the infiltration of diverse immune cells enabled the identification of distinct differences in the immunological microenvironments of the two risk groups. Subsequently, we conducted a real-time polymerase chain reaction (qPCR) experiment to confirm the accuracy of the selected lncRNAs. Results: A predictive framework for HCC was constructed based on the expression levels of five lncRNAs. Multivariate and univariate Cox regression analyses revealed that lncRNA signatures associated with senescence were independently correlated with an increased risk of HCC. Additionally, the nomogram also provides a more refined and sensitive model. Further investigation into the variations in immune cells and functions between the high-risk and low-risk groups was conducted. Subsequently, a qPCR experiment results revealed underexpression of AC068756.1, AC090578.1, AC145343.1, and LINC0022 in Huh7 and LM3 cells. In contrast, AP003392.4 did not exhibit a significant difference between Huh7 and control cells. Conclusion: The prognostic features and nomogram, consisting of five aging-related lncRNAs (AC068756.1, AC090578.1, AC145343.1, AP003392.4, and LINC00221), may be useful in predicting the overall survival of HCC.

As an important part of Traditional Chinese Medicine (TCM) inspection, face diagnosis is significant in judging the rise and fall of viscera and essence and diagnosing and treating diseases. Cancer is a serious disease that endangers human life and health. Face diagnosis can play a great role in diagnosing and treating tumors, but this approach has not achieved the expected effects due to the lack of research. Herein, we summarized the research regarding facial expression, color, shape, and state, demonstrating that facial diagnosis is significant in diagnosing and treating tumors. We also propose that facial diagnosis must be combined with computer technology to realize the objectification of facial diagnosis as soon as possible to play a greater role in diagnosing and treating tumors.