Antibodies have emerged as a fast‐growing category of biopharmaceuticals that have been widely applied in scientific research, medical diagnosis, and disease treatment. However, many antibodies and other biopharmaceuticals display inferior biophysical properties, such as low stability and a propensity to undergo aggregation. Enhancing the stability of biopharmaceuticals is essential for their wide applications. Here, a facile in vitro protective coating strategy based on metal–organic frameworks (MOFs) is proposed to efficiently protect antibodies against perturbation environments and quickly recover them from the MOFs before usage, which avoids introducing protective additives into the body, which may cause biosafety risks. The protected antibodies exhibit extraordinary thermal, chemical, and mechanical stabilities, and they can survive for long‐term storage (>3 weeks) under severe temperature variation (4 ↔ 50 °C) at a fast ramp rate (25 °C min−1). More importantly, the encapsulated antibodies can be easily released as quickly as 10 s with high efficiency (≈100%) to completely remove the MOFs before use. This study paves a new avenue for the facile preparation and storage of biopharmaceuticals represented by antibodies under ambient or perturbation conditions, which may greatly broaden and promote the applications of both MOFs and biopharmaceuticals.

Fabrication of zeolite‐like metal–organic frameworks (ZMOFs) for advanced applications, such as enzyme immobilization, is of great interest but is a great synthetic challenge. Herein, we have developed a new strategy using proteins as structure‐directed agents to direct the formation of new ZMOFs that can act as versatile platforms for the in situ encapsulation of proteins under ambient conditions. Notably, protein incorporation directs the formation of a ZMOF with a sodalite (sod) topology instead of a non‐porous diamondoid (dia) topology under analogous synthetic conditions. Histidines in proteins play a crucial role in the observed templating effect. Modulating histidine content thereby influenced the resultant MOF product (from dia to dia + sod mixture and, ultimately, to sod MOF). Moreover, the resulting ZMOF‐incorporated proteins preserved their activity even after exposure to high temperatures and organic solvents, demonstrating their potential for biocatalysis and biopharmaceutical applications.

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Chemotherapy resistance in breast cancer is an important factor affecting the prognosis of breast cancer patients. We computationally analyzed the differences in gene expression before and after chemotherapy in breast cancer patients, drug-sensitive groups, and drug-resistant groups. Through functional enrichment analysis, immune microenvironment analysis, and other computational analysis methods, we identified PRC1, GGTLC1, and IRS1 as genes that may mediate breast cancer chemoresistance through the immune pathway. After validation of certain other clinical datasets and in vitro cellular assays, we found that the above three genes influenced drug resistance in breast cancer patients and were closely related to the tumor immune microenvironment. Our finding that chemoresistance in breast cancer could be influenced by the mediation of tumor immunity expanded our knowledge of how to address this problem and could guide future research involving chemoresistance.

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.