A Novel Cellular Senescence-related lncRNA Signature for Predicting the Prognosis of Breast Cancer Patients.

Background: Long non-coding RNA (lncRNA), a crucial regulator in breast cancer (BC) development, is intricately linked with cellular senescence. However, there is a lack of cellular senescence-related lncRNAs (CSRLs) signature to evaluate the prognosis of BC patients. Methods: Correlation analysis was conducted to identify lncRNAs associated with cellular senescence. Subsequently, a CSRL signature was crafted in the training cohort. The model's accuracy was evaluated through survival analysis and receiver operating characteristic curves. Furthermore, prognostic nomograms amalgamating cellular senescence and clinical characteristics were devised. Tumor microenvironment and checkpoint disparities were compared between low-risk and high-risk groups. The correlation between these signatures and treatment response in BC patients was also investigated. Finally, functional experiments were conducted for validation. Results: A signature comprising nine CSRLs was devised, which demonstrated adept prognostic capability in BC patients. Functional enrichment analysis revealed that tumor and immune-related pathways were predominantly enriched. Compared to the low-risk group, the high-risk group could benefit more from immunotherapy and certain chemotherapeutic agents. The expression of the 9 CSRLs was validated through in vitro experiments in different subtypes of BC cell lines and tissues. AC098484.1 was specifically verified for its association with senescence-associated secretory phenotypes. Conclusion: The CSRLs signature emerges as a promising prognostic biomarker for BC, with implications for immunological studies and treatment strategies. AC098484.1 has potential relevance in the treatment of BC cell senescence, and these findings improve the clinical treatment levels for BC patients.

Identification of Hypoxia and Mitochondrial-related Gene Signature and Prediction of Prognostic Model in Lung Adenocarcinoma.

Background: The correlation between hypoxia and tumor development is widely acknowledged. Meanwhile, the foremost organelle affected by hypoxia is mitochondria. This study aims to determine whether they possess prognostic characteristics in lung adenocarcinoma (LUAD). For this purpose, a bioinformatics analysis was conducted to assess hypoxia and mitochondrial scores related genes, resulting in the successful establishment of a prognostic model. Methods: Using the single sample Gene Set Enrichment Analysis algorithm, the hypoxia and mitochondrial scores were computed. Differential expression analysis and weighted correlation network analysis were employed to identify genes associated with hypoxia and mitochondrial scores. Prognosis-related genes were obtained through univariate Cox regression, followed by the establishment of a prognostic model using least absolute shrinkage and selection operator Cox regression. Two independent validation datasets were utilized to verify the accuracy of the prognostic model using receiver operating characteristic and calibration curves. Additionally, a nomogram was employed to illustrate the clinical significance of this study. Results: 318 differentially expressed genes associated with hypoxia and mitochondrial scores were identified for the construction of a prognostic model. The prognostic model based on 16 genes, including PKM, S100A16, RRAS, TUBA4A, PKP3, KCTD12, LPGAT1, ITPRID2, MZT2A, LIFR, PTPRM, LATS2, PDIK1L, GORAB, PCDH7, and CPED1, demonstrates good predictive accuracy for LUAD prognosis. Furthermore, tumor microenvironments analysis and drug sensitivity analysis indicate an association between risk scores and certain immune cells, and a higher risk scores suggesting improved chemotherapy efficacy. Conclusion: The research established a prognostic model consisting of 16 genes, and a nomogram was developed to accurately predict the prognosis of LUAD patients. These findings may contribute to guiding clinical decision-making and treatment selection for patients with LUAD, ultimately leading to improved treatment outcomes.

The impact of investor sentiment on firms' green total factor productivity-facilitator or inhibitor?

Investor sentiment does not only have negative impacts. It may also improve green total factor productivity by invigorating funds. This research constructs a new indicator at the firm level to measure the green total factor productivity of firms. We research the effect of investor sentiment on firms' green total factor productivity using a sample of Chinese heavy polluters listed on Shanghai and Shenzhen A-shares between 2015 and 2019. Through a series of tests, the mediating role of agency costs and financial situations is confirmed. It is discovered that the digitization of businesses facilitates the effect of investor sentiment on the green total factor productivity of businesses. And when managerial competence reaches a certain threshold, the impact of investor sentiment on green total factor productivity is amplified. Tests for heterogeneity reveal that high investor sentiment has a larger impact on green total factor productivity in firms with superior supervision.

A study of the impact of corporate digitization on environmental protection: Take Chinese A-share companies in Shanghai and Shenzhen as an example.

Textual analysis and the Entropy-TOPSIS method are used in this research to create a measure of corporate environmental protection, and multiple regressions are used to find out how digitalization affects corporate environmental protection. The research sets up a theoretical framework for how corporate digitalization affects environmental protection and looks into how external financing constraints and an organization's own financial position play a role in the middle. The research then looks at how outside factors like the business environment of the market and the level of competition in the industry affect the relationship. Using a threshold regression approach, the research also examines the change in the impact of digitalization on environmental protection after investor sentiment crosses the threshold from the distinct perspective of investor sentiment. Our research provides theoretical support for environmental protection by corporations and government policy direction.