Mechanism of prognostic marker SPOCK3 affecting malignant progression of prostate cancer and construction of prognostic model

PDZK1 inhibits MRP2-mediated oxaliplatin chemosensitivity in hepatocellular carcinoma

Recurrence after oxaliplatin chemotherapy is a major challenge in the treatment of advanced hepatocellular carcinoma patients. Differential expression gene analysis and Kaplan-Meier curves were screened biomarkers associated with OXA-treated recurrence in GSE51951, TCGA-LIHC, and Chinese Liver Cancer Atlas databases. We retrospectively collected 39 cases of HCC treated with platinum based drugs at the First Hospital of Shanxi Medical University. Immunohistochemistry was used to analyze the relationship between PDZK1 expression and patient recurrence of HCC. Cell model and subcutaneous transplant tumor model of HCC were established to detect the cell growth ability treated with OXA. Gene Set Enrichment Analysis analysis identified signaling pathways associated with high PDZK1. Co-Immunoprecipitation and immunofluorescence experiments were used to explore the potential interaction between PDZK1 and MRP2. We identified that high expression of PDZK1 was associated with OXA resistance and poor prognosis in HCC. PDZK1 promoted the cell viability, migration, and invasion of HCC after OXA treatment in vitro and vivo. MRP2-mediated ABC transporters pathway and bile acid metabolism were significantly activated in the PDZK1 overexpression group of HCC. PDZK1 interacted and co-localized with the carboxyl terminal PDZ binding motif of MRP2. Clinical specimen analysis have shown a positive correlation between the protein levels of PDZK1 and MRP2. Our study identified PDZK1 as a novel biomarker significantly associated with OXA chemosensitivity in HCC. Mechanistically, PDZK1 promoted the OXA sensitivity of HCC by activating the MRP2-mediated signaling pathway.

scPRINT: pre-training on 50 million cells allows robust gene network predictions

A cell is governed by the interaction of myriads of macromolecules. Inferring such a network of interactions has remained an elusive milestone in cellular biology. Building on recent advances in large foundation models and their ability to learn without supervision, we present scPRINT, a large cell model for the inference of gene networks pre-trained on more than 50 million cells from the cellxgene database. Using innovative pretraining tasks and model architecture, scPRINT pushes large transformer models towards more interpretability and usability when uncovering the complex biology of the cell. Based on our atlas-level benchmarks, scPRINT demonstrates superior performance in gene network inference to the state of the art, as well as competitive zero-shot abilities in denoising, batch effect correction, and cell label prediction. On an atlas of benign prostatic hyperplasia, scPRINT highlights the profound connections between ion exchange, senescence, and chronic inflammation.

Predicting Survival in Patients with Neuroendocrine Prostate Cancer: A SEER-Based Comprehensive Study

PURPOSE

Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC.

MATERIALS AND METHODS

Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses.

RESULTS

The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses.

CONCLUSIONS

The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

SPOCK: Master regulator of malignant tumors (Review)

SPARC/osteonectin, CWCV and Kazal‑like domain proteoglycan (SPOCK) is a family of highly conserved multidomain proteins. In total, three such family members, SPOCK1, SPOCK2 and SPOCK3, constitute the majority of extracellular matrix glycoproteins. The SPOCK gene family has been demonstrated to serve key roles in tumor regulation by affecting MMPs, which accelerates the progression of cancer epithelial‑mesenchymal transition. In addition, they can regulate the cell cycle via overexpression, inhibit tumor cell proliferation by inactivating PI3K/AKT signaling and have been associated with numerous microRNAs that influence the expression of downstream genes. Therefore, the SPOCK gene family are potential cancer‑regulating genes. The present review summarizes the molecular structure, tissue distribution and biological function of the SPOCK family of proteins, in addition to its association with cancer. Furthermore, the present review documents the progress made in investigations into the role of SPOCK, whilst also discussing prospects for the future of SPOCK‑targeted therapy, to provide novel ideas for clinical application and treatment.

Identification of a basement membrane-related gene signature for predicting prognosis, immune infiltration, and drug sensitivity in colorectal cancer

Background

Colorectal cancer (CRC) is the most common malignancy affecting the gastrointestinal tract. Extensive research indicates that basement membranes (BMs) may play a crucial role in the initiation and progression of the disease.

Methods

Data on the RNA expression patterns and clinicopathological information of patients with CRC were sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A BM-linked risk signature for the prediction of overall survival (OS) was formulated using univariate Cox regression and combined machine learning techniques. Survival outcomes, functional pathways, the tumor microenvironment (TME), and responses to both immunotherapy and chemotherapy within varying risk classifications were also investigated. The expression trends of the model genes were evaluated by reverse transcription polymerase chain reaction (RT-PCR) and the Human Protein Atlas (HPA) database.

Results

A nine-gene risk signature containing UNC5C, TINAG, TIMP1, SPOCK3, MMP1, AGRN, UNC5A, ADAMTS4, and ITGA7 was constructed for the prediction of outcomes in patients with CRC. The expression profiles of these candidate genes were verified using RT-PCR and the HPA database and were found to be consistent with the findings on differential gene expression in the TCGA dataset. The validity of the signature was confirmed using the GEO cohort. The patients were stratified into different risk groups according to differences in clinicopathological characteristics, TME features, enrichment functions, and drug sensitivities. Lastly, the prognostic nomogram model based on the risk score was found to be effective in identifying high-risk patients and predicting OS.

Conclusion

A basement membrane-related risk signature was constructed and found to be effective for predicting the prognosis of patients with CRC.

The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery

Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.