Comprehensive analysis of LAMC1 expression and prognostic value in kidney renal papillary cell carcinoma and clear cell carcinoma

ZACN Associated with Poor Prognosis Promotes Proliferation of Kidney Renal Clear Cell Carcinoma Cells by Inhibiting JTC801-Induced Alkaliptosis

Alkaliptosis, crucial in various cancers, is a specific form of cell death. This study aims to screen a prognosis-related gene in kidney renal clear cell carcinoma (KIRC) using the alkaliptosis gene set and to investigate the roles of the gene in KIRC and its association with alkaliptosis. Transcriptome and clinical information of KIRC patients were collected from the TCGA-KIRC and GSE29609 database. We detected ZACN levels in normal kidney cells and KIRC cells and assessed the ZACN and JTC801-induced alkaliptosis relationship using immunoblotting and pH measurement. In the alkaliptosis gene set (IKBKB, NFKB1, CA9, CHUK, IKBKG, and RELA), NFKB1, CHUK, and IKBKG exhibited differential expression in TCGA-KIRC. Based on these three genes, two alkaliptosis patterns were identified in TCGA-KIRC. The independent prognostic gene for KIRC, ZACN, was screened. High ZACN in KIRC patients indicated a poor prognosis. ZACN was inversely related to the infiltration of anti-cancer immune cells such as CD4 + T cells, macrophages, and neutrophils, and it regulated the immune checkpoint and gene mutations. Patients characterized by high ZACN levels exhibited a heightened drug sensitivity to ABT737_1910, 5-Fluorouracil_1073, etc. ZACN expression in KIRC cells was increased relative to normal kidney cells and was inhibited in a concentration-dependent manner by JTC801. ZACN overexpression suppressed p-p65/p65 expression, increased expression of CA9, and lowered intracellular pH. In KIRC, ZACN inhibits JTC801-induced alkaliptosis. This study sheds light on a novel mechanism involving ZACN and alkaliptosis in KIRC, offering a promising avenue for further research and potential therapeutic interventions in KIRC management.

Application of Surface Plasmon Resonance Imaging Biosensors for Determination of Fibronectin, Laminin-5, and Type IV Collagen in Plasma, Urine, and Tissue of Renal Cell Carcinoma

Laminin, fibronectin, and collagen IV are pivotal extracellular matrix (ECM) components. The ECM environment governs the fundamental properties of tumors, including proliferation, vascularization, and invasion. Given the critical role of cell-matrix adhesion in malignant tumor progression, we hypothesize that the concentrations of these proteins may be altered in the plasma of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate the serum, urine, and tissue levels of laminin-5, collagen IV, and fibronectin among a control group and ccRCC patients, with the latter divided into stages T1-T2 and T3-T4 according to the TNM classification. We included 60 patients with histopathologically confirmed ccRCC and 26 patients diagnosed with chronic cystitis or benign prostatic hyperplasia (BPH). Collagen IV, laminin-5, and fibronectin were detected using Surface Plasmon Resonance Imaging biosensors. Significant differences were observed between the control group and ccRCC patients, as well as between the T1-T2 and T3-T4 subgroups. Levels were generally higher in plasma and tissue for fibronectin and collagen IV in ccRCC patients and lower for laminin. The ROC (Receiver operating characteristic) analysis yielded satisfactory results for differentiating between ccRCC patients and controls (AUC 0.84-0.93), with statistical significance for both fibronectin and laminin in plasma and urine. Analysis between the T1-T2 and T3-T4 groups revealed interesting findings for all examined substances in plasma (AUC 0.8-0.95). The results suggest a positive correlation between fibronectin and collagen levels and ccRCC staging, while laminin shows a negative correlation, implying a potential protective role. The relationship between plasma and urine concentrations of these biomarkers may be instrumental for tumor detection and staging, thereby streamlining therapeutic decision-making.

HIF-1α-mediated LAMC1 overexpression is an unfavorable predictor of prognosis for glioma patients: evidence from pan-cancer analysis and validation experiments

BACKGROUND

Laminin subunit gamma-1 (LAMC1) is a major extracellular matrix molecule involved in the tumor microenvironment. Knowledge of the biological features and clinical relevance of LAMC1 in cancers remains limited.

METHODS

We conducted comprehensive bioinformatics analysis of LAMC1 gene expression and clinical relevance in pan-cancer datasets of public databases and validated LAMC1 expression in glioma tissues and cell lines. The association and regulatory mechanism between hypoxia inducible factor-1α (HIF-1α) and LAMC1 expression were explored.

RESULTS

LAMC1 expression in most cancers in The Cancer Genome Atlas (TCGA) including glioma was significantly higher than that in normal tissues, which had a poor prognosis and were related to various clinicopathological features. Data from the Chinese Glioma Genome Atlas also showed high expression of LAMC1 in glioma associated with poor prognoses. In clinical glioma tissues, LAMC1 protein was highly expressed and correlated to poor overall survival. LAMC1 knockdown in Hs683 glioma cells attenuated cell proliferation, migration, and invasion, while overexpression of LAMC1 in U251 cells leads to the opposite trend. Most TCGA solid cancers including glioma showed enhancement of HIF-1α expression. High HIF-1α expression leads to adverse prognosis in gliomas, besides, HIF-1α expression was positively related to LAMC1. Mechanistically, HIF-1α directly upregulated LAMC1 promotor activity. Hypoxia (2% O2)-treated Hs683 and U251 cells exhibited upregulated HIF-1α and LAMC1 expression, which was significantly attenuated by HIF-1α inhibitor YC-1 and accompanied by attenuated cell proliferation and invasion.

CONCLUSIONS

High expression of LAMC1 in some solid tumors including gliomas suggests a poor prognosis. The hypoxic microenvironment in gliomas activates the HIF-1α/LAMC1 signaling, thereby promoting tumor progression. Targeted intervention on the HIF-1α/LAMC1 signaling attenuates cell growth and invasion, suggesting a new strategy for glioma treatment.

Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common malignant tumors worldwide. Nevertheless, GC still lacks effective diagnosed and monitoring method and treating targets. This study used multi omics data to explore novel biomarkers and immune therapy targets around sphingolipids metabolism genes (SMGs).

METHOD

LASSO regression analysis was performed to filter prognostic and differently expression SMGs among TCGA and GTEx data. Risk score model and Kaplan-Meier were built to validate the prognostic SMG signature and prognostic nomogram was further constructed. The biological functions of SMG signature were annotated via multi omics. The heterogeneity landscape of immune microenvironment in GC was explored. qRT-PCR was performed to validate the expression level of SMG signature. Competing endogenous RNA regulatory network was established to explore the molecular regulatory mechanisms.

RESULT

3-SMGs prognostic signature (GLA, LAMC1, TRAF2) and related nomogram were constructed combing several clinical characterizes. The expression difference and diagnostic value were validated by PCR data. Multi omics data reveals 3-SMG signature affects cell cycle and death via several signaling pathways to regulate GC progression. Overexpression of 3-SMG signature influenced various immune cell infiltration in GC microenvironment. RBP-SMGs-miRNA-mRNAs/lncRNAs regulatory network was built to annotate regulatory system.

CONCLUSION

Upregulated 3-SMGs signature are excellent predictive diagnosed and prognostic biomarkers, providing a new perspective for future GC immunotherapy.

Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction

Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic DL is challenged by the low-sample sizes (34-286 subjects), high-dimensionality (up to 21,653 genes) and unordered nature of clinical transcriptomic data. The established methods rely on ML algorithms at accuracy levels of 0.6-0.8 AUC/ACC values. Low-sample DL algorithms are needed for enhanced prediction capability. Here, an unsupervised manifold-guided algorithm was employed for restructuring transcriptomic data into ordered image-like 2D-representations, followed by efficient DL of these 2D-representations with deep ConvNets. Our DL models significantly outperformed the state-of-the-art (SOTA) ML models on 82% of 17 low-sample benchmark datasets (53% with >0.05 AUC/ACC improvement). They are more robust than the SOTA models in cross-cohort prediction tasks, and in identifying robust biomarkers and response-dependent variational patterns consistent with experimental indications.

Investigation on acquired palbociclib resistance by LC-MS based multi-omics analysis

Palbociclib is a specific CDK4/6 inhibitor that has been widely applied in multiple types of tumors. Different from cytotoxic drugs, the anticancer mechanism of palbociclib mainly depends on cell cycle inhibition. Therefore, the resistance mechanism is different. For clinical cancer patients, drug resistance is inevitable for almost all cancer therapies including palbociclib. We have trained palbociclib resistant cells in vitro to simulate the clinical situation and applied LC-MS multi-omics analysis methods including proteomic, metabolomic, and glycoproteomic techniques, to deeply understand the underly mechanism behind the resistance. As a result of proteomic analysis, the resistant cells were found to rely on altered metabolic pathways to keep proliferation. Metabolic processes related to carbohydrates, lipids, DNA, cellular proteins, glucose, and amino acids were observed to be upregulated. Most dramatically, the protein expressions of COX-1 and NDUFB8 have been detected to be significantly overexpressed by proteomic analysis. When a COX-1 inhibitor was hired to combine with palbociclib, a synergistic effect could be obtained, suggesting the altered COX-1 involved metabolic pathway is an important reason for the acquired palbociclib resistance. The KEGG pathway of N-glycan biosynthesis was identified through metabolomics analysis. N-glycoproteomic analysis was therefore included and the global glycosylation was found to be elevated in the palbociclib-resistant cells. Moreover, integration analysis of glycoproteomic data allowed us to detect a lot more proteins that have been glycosylated with low abundances, these proteins were considered to be overwhelmed by those highly abundant proteins during regular proteomic LC-MS detection. These low-abundant proteins are mainly involved in the cellular biology processes of cell migration, the regulation of chemotaxis, as well as the glycoprotein metabolic process which offered us great more details on the roles played by N-glycosylation in drug resistance. Our result also verified that N-glycosylation inhibitors could enhance the cell growth inhibition of palbociclib in resistant cells. The high efficiency of the integrated multi-omics analysis workflow in discovering drug resistance mechanisms paves a new way for drug development. With a clear understanding of the resistance mechanism, new drug targets and drug combinations could be designed to resensitize the resistant tumors.

Integrated analysis of Dendrobium nobile extract Dendrobin A against pancreatic ductal adenocarcinoma based on network pharmacology, bioinformatics, and validation experiments

Background: Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC). Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells. Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase. Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.