B-cell-specific signatures reveal novel immunophenotyping and therapeutic targets for hepatocellular carcinoma

Identification and validation of molecular subtypes and prognostic models in patients with kidney cancer based on differential genes based on B cells: a multiomics analysis

BACKGROUND

B cells play a variety of complex roles in cancer, both promoting cancer progression and enhancing anti-tumor immune responses, but their mechanism of action in kidney cancer has not been elucidated.

RESULTS

We collected kidney cancer sample data from the GEO database and TCGA database, mapped the single-cell landscape inside kidney cancer tissue, identified 25 B-cell-related genes, and based on this, identified related molecular subtypes of kidney cancer patients, and explored their internal microenvironment characteristics. Finally, we constructed a 6-gene biological prognostic model that can be used to predict survival in patients with renal cancer, and we further validated the predictive performance of the model based on imaging omics. It is worth mentioning that the structural patterns and functional sites of 6 model gene transcription proteins were also mined.

CONCLUSIONS

Overall, we explored for the first time the profound role of B cells in kidney cancer and developed a bio-predictive model based on B cell-related genes, providing scientific guidance for personalized treatment of kidney cancer patients.

Effects of NK cell-related lncRNA on the immune microenvironment and molecular subtyping for pancreatic ductal adenocarcinoma

Background

Patients with pancreatic ductal adenocarcinoma (PDAC) face a highly unfavorable outcome and have a poor response to standard treatments. Immunotherapy, especially therapy based on natural killer (NK) cells, presents a promising avenue for the treatment of PDAC.

Aims

This research endeavor seeks to formulate a predictive tool specifically designed for PDAC based on NK cell-related long non-coding RNA (lncRNA), revealing new molecular subtypes of PDAC to promote personalized and precision treatment.

Methods

Utilizing the Tumor Immune Single-cell Hub 2 platform, we discovered genes associated with NK cells in PDAC. We employed the TCGA-PAAD dataset to ascertain the expression profiles of these NK cell-related genes and to screen for lncRNAs correlated with NK cells. Subsequently, we utilized Cox regression analysis for hazard ratios and LASSO regression analysis to identify three NK cell-related lncRNAs that were used to develop a prognostic assessment model. The forecasting accuracy of this model was appraised using the ROC curve and validated using a test set and the complete dataset.

Results

Successful construction of a prognostic model comprising three lncRNAs was achieved, demonstrating good predictive efficiency in the training set, validation dataset, and the entire dataset. NK cells display robust interactions with malignant cells, CD8 T cells, and fibroblasts in the PDAC tumor microenvironment and participate in the transport of various signaling molecules and following immune responses in PDAC. According to the expression patterns of NK cell-related lncRNA, we labeled PDAC patients as four molecular subtypes, exhibiting significant differences in immune cell infiltration, drug sensitivity, and other aspects.

Conclusion

This study Uncovered the activity of NK cells within PDAC, proposed an NK cell-related lncRNA model, and delineated new molecular subtypes, thereby providing targets for personalized therapy.