Induction of ER stress-mediated apoptosis through SOD1 upregulation by deficiency of CHI3L1 inhibits lung metastasis

Identification and validation of protein biomarkers for predicting gastrointestinal stromal tumor recurrence

We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers-GPX4, RBM4, TPM3, PFKFB2, and PGAM5-and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients. Kaplan-Meier survival analysis showed that high expressions of GPX4, RBM4, TPM3, PFKFB2, and PGAM5 correlated with lower RFS, and GPX4 and RBM4 with lower OS. All biomarker pairs showed positive associations, with high expressions correlating with increased recurrence rates, and GPX4 and RBM4 with higher mortality rates. In conclusion, the biomarkers GPX4, RBM4, TPM3, PFKFB2, and PGAM5 are clinically relevant for predicting GIST recurrence, with their high expressions in primary tumors linked to poorer RFS and OS. They serve as potential prognostic indicators, enabling early treatment and improved outcomes. The observed interrelationships among these biomarkers further validate their accuracy in predicting GIST recurrence.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer

BACKGROUND

Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear.

METHODS

Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy.

RESULTS

A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues.

CONCLUSIONS

We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Targeting ERS-mitophagy in hippocampal neurons to explore the improvement of memory by tea polyphenols in aged type 2 diabetic rats

Increasing evidence demonstrated that mitophagy and endoplasmic reticulum stress (ERS) was closely associated with memory decline in elderly type 2 diabetes mellitus (T2DM). Tea polyphenols (TP), an excellent natural antioxidant, has been reported to have neuroprotective properties in aging and diabetes, but the underlying mechanisms are still not fully understood. This study targets ERS-mitophagy in hippocampal neurons to investigate the improvement effect of memory in aged T2DM rats by TP. Rats were randomly divided into the control group, the aged group, the aged T2DM model group, the TP 75, 150, 300 mg/kg groups. TP 300 mg/kg ameliorated mitophagy by decreasing the levels of p-mTOR (S2448), P62 and HSP60 and increasing the levels of PINK1 and Parkin, the ratio of LC3Ⅱ/LC3Ⅰ, co-localization of LC3 and HSP60 and the number of autophagosomes and autolysosomes. TP 300 mg/kg attenuated ERS by downregulating the levels of p-PERK, p-eIF2α, ATF4, GRP78 and restoring the ER structure. To further verify epigallocatechin gallate (EGCG), which is the main active component of TP, enhanced mitophagy by inhibiting ERS, PC12 cells were pretreated with ERS activator tunicamycin (TM) or ERS inhibitor 4-phenylbutyric acid (4-PBA). The results showed that the improvement of mitophagy by EGCG was inhibited by TM and promoted by 4-PBA. Collectively, ERS-mitophagy in hippocampal neurons plays a key role in the improvement of memory by TP in aged T2DM rats. This study will provide a new perspective and strategy for the prevention of memory decline in elderly with T2DM.

Identication and validation of cell senescence biomarkers in idiopathic pulmonary hypertension via integrated transcriptome analyses and machine learning

BACKGROUND

Idiopathic pulmonary hypertension (IPAH) is a rare and severe disease that affects the pulmonary vasculature. As the diagnosis of IPAH requires invasive right heart catheterization surgery, early detection of this condition is notoriously challenging. Therefore, it is of utmost importance to investigate biomarkers present in peripheral blood that could aid physicians in the early identification and management of IPAH.

METHOD

We speculate that cellular senescence may be involved in the occurrence and development of IPAH through various pathways. In this study, we utilized integrated transcriptome analyses and machine learning-based approach to develop a diagnostic model for IPAH cell senescence. To select genetic features, we employed two machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). Additionally, we validated our findings through both external data sets and qRT-PCR experiments.

RESULTS

The resulting diagnostic nomogram was able to identify five important biomarkers that can aid in the diagnosis of IPAH, including TNFRSF1B, CCL16, GCLM, IL15, and SOD1. These genes are primarily associated with the immune system, as well as with cell senescence and apoptosis.

CONCLUSION

Our study demonstrates the utility of machine learning algorithms in making accurate diagnoses of IPAH, providing clinicians with a more directed approach to the diagnosis and treatment of this disease.