MARCKS (Myristoylated Alanine Rich Protein Kinase C Substrate)

This study integrates PDO models with single-cell transcriptomics technology to reveal key cell subpopulations and molecular mechanisms underlying irinotecan resistance. The core mechanisms driving resistance involve the activation of Wnt signaling and the synergistic effect of lipid metabolism-Notch pathways. Cluster 1 and Cluster 6 are identified as potential therapeutic targets, providing a theoretical basis for developing combination therapies targeting cancer stem cells or the metabolic microenvironment.

This study systematically characterizes the molecular landscape and features of primary breast tumors and their matched metastatic lymph nodes. These insights enhance our understanding of early-stage breast cancer metastasis and may pave the way for improved diagnostic tools and targeted therapeutic strategies.

UCHL1 has the potential to be used as a novel prognostic and therapeutic target for HGSOC.

In addition, the ED4A amino acid component is more critical than its order, and TAT is essential for the high sensitivity of cytotoxicity. Our study suggests that 4A-TAT peptide shows acute and robust killing effects on tumor cells in vitro and in vivo, making it a potentially intriguing cytotoxic peptide for tumor treatment.

In addition, the protein expression levels of CGB5, MATN3, MARCKS and APOD in GC tissues were significantly higher than those in normal tissues, and correlated with the pathological characteristics of GC patients. The risk model composed of 7 hub genes can accurately evaluate the prognosis of GC patients, which may contribute to the precise and personalized treatment of GC patients.

The AID model is a promising biomarker for accurately determining survival and predicting the effectiveness of immunotherapy in STAD patients. GLS1 plays a crucial role in driving tumor proliferation and migration and may act as a potential tumor biomarker of STAD.

SUV39H1 silencing reduced EMT marker genes CDH2, SNAI1 and MARCKS. SUV39H1 has an oncogenic role in pHGG and presents a promising therapeutic target.

Proteomic analyses allowed the identification of TMOD2-associated proteins involved in cytoskeletal dynamics, secretion, and focal adhesions, with further validation implicating STAG1 and MARCKS as mediators of TMOD2-driven pathways. Our findings demonstrate that TMOD2 plays a role in CRC progression by modulating cytoskeletal dynamics, enhancing cell adhesion and promoting liver metastasis, positioning TMOD2 as a target for therapeutic intervention in CRC.

No deep learning study was found even though deep learning was used as a search term together with machine learning. Genetic artificial intelligence is effective and non-invasive as a decision support system for GID.

These results demonstrate the capability of RIME to elucidate interactomes of membrane proteins such as HER2 in clinical tissue specimens. Furthermore, this study highlights its broader applicability beyond nuclear proteins, underscoring its potential for discovering novel prognostic and diagnostic clinical markers in diverse cancer types.

Four RBPs associated with SCI were identified: Nkrf, Marcks, NDRG4, and Ryr2. These key RBPs may serve as potential targets for the treatment of patients with SCI.

Our study revealed that SCAFs were strongly correlated with cancer stemness in HCC. A novel machine learning model based on senescent CAF-related genes was constructed to accurately predict prognosis in HCC patients. Furthermore, CTHRC1 was identified as a novel prognostic and therapeutic biomarker to predict poor prognosis in HCC and promote cancer stemness and metastasis through the Notch signaling pathway, with its expression being transcriptionally regulated by SOX4.