A novel lipid metabolism gene signature for clear cell renal cell carcinoma using integrated bioinformatics analysis

Bioinformatics screening of prognostic immune-related genes in renal clear cell carcinoma

This study aims to harness bioinformatics to identify prognostic immune-related genes in clear cell renal cell carcinoma (ccRCC), focusing particularly on LILRB3. It evaluates LILRB3's expression in ccRCC, its association with patient prognosis, and its potential as a biomarker for predicting survival, thereby providing a preliminary basis for the diagnosis of ccRCC. Utilizing The Cancer Genome Atlas (TCGA) datasets and an immune gene set, we sought immune-related genes with elevated expression in ccRCC. Seventy-two normal tissue samples and 531 ccRCC samples were analyzed, and differential genes were identified with a screening criterion of fold change (FC) > 2 and P value < 0.01. Survival analysis and receiver operating characteristic (ROC) curve analysis were employed to discover genes of prognostic and diagnostic relevance to ccRCC. Pearson correlation analysis with a cutoff of |r|≥ 0.5, facilitated by cBioPortal, assessed genes co-expressed with LILRB3. The DAVID online tool conducted functional and pathway enrichment analyses for LILRB3-coexpressed genes. The TIMER and TCIA databases were utilized to explore LILRB3's influence on immune infiltration in the tumor microenvironment and its relation to key immunological checkpoints. Screening the TCGA database revealed 3719 up-regulated differential genes in ccRCC, with 355 overlapping immune-related genes. Survival analysis of these 355 genes revealed 100 with significant survival impact. ROC curve analysis pinpointed the top 10 genes, including LILRB3, with the highest diagnostic efficiency. LILRB3 emerged as an independent risk factor from the Cox risk regression model. GO and KEGG analyses linked LILRB3 to various biological processes, including chemokine signaling pathways, immunological response, antigen processing and presentation, inflammatory response, T cell co-stimulation, and signal transduction. LILRB3 significantly affected ccRCC immune infiltration and correlated positively with several immunological checkpoints, such as PD-1, LAG3, IDO1, PD-L1, CTLA4, TIM3, TIGIT, and VISTA. LILRB3 shows higher expression levels in ccRCC than in normal tissues and correlates with poor patient prognosis. Its impactful role in the immune infiltration of the RCC microenvironment suggests that LILRB3 could serve as a novel target for ccRCC treatment and prognosis, underlining its diagnostic and prognostic significance.

Insight into dipeptidase 1: structure, function, and mechanism in gastrointestinal cancer diseases

Dipeptidase 1 (DPEP1), initially identified as a renal membrane enzyme in mature human kidneys, plays a pivotal role in various cellular processes. It facilitates the exchange of materials and signal transduction across cell membranes, contributing significantly to dipeptide hydrolysis, glucose and lipid metabolism, immune inflammation, and ferroptosis, among other cellular functions. Extensive research has delineated the complex role of DPEP1 in oncogenesis and tumor progression, with its influence being context dependent. DPEP1 has been observed to promote oncogenic activities in hepatocellular carcinoma, non-small cell lung cancer, colorectal cancer, and lymphoblastic malignancies and is hypothesized to participate in multiple biological processes, including tumor cell invasion, metastatic spread, cellular signaling pathways, cell-matrix interactions, and evasion of immune surveillance. Conversely, DPEP1 has been identified as a tumor suppressor in pancreatic adenocarcinoma, lobular breast carcinoma, and Wilms tumor. Moreover, the role of DPEP1 in colorectal cancer has been increasingly recognized in recent research. Emerging evidence suggests that DPEP1 substantially augments the metastatic and invasive potential of colorectal cancer cells, facilitates immune evasion, and confers resistance to chemotherapeutic agents. Despite these findings, the precise molecular mechanisms remain to be fully characterized. This systematic review endeavors to elucidate the structural and functional attributes of the DPEP1 protein, with the aim to clarify its regulatory mechanisms and assess its clinical relevance in oncology. Gaining a thorough understanding of the physiological role and molecular underpinnings of DPEP1 is critical to informing the diagnostic, therapeutic, and prognostic paradigms of related pathologies. It is anticipated that these insights will facilitate the discovery of novel therapeutic targets and generate new investigative trajectories, particularly in the clinical management of colorectal cancer.

Establishment of a prognostic model for pancreatic cancer based on mitochondrial metabolism related genes

AIM

Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients.

METHOD

Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan-Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages.

RESULTS

A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax.

CONCLUSION

By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients.

Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma

Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

CBX3 promotes clear cell renal carcinoma through PI3K/AKT activation and aberrant immunity

BACKGROUND

A chromobox homologue 3 (CBX3) is elevated in various cancers and significantly contributes to the promotion of malignant behavior; despite this, its exact involvement in clear cell renal cell carcinoma (ccRCC) is yet unknown.

METHODS

The Cancer Genome Atlas database served to evaluate CBX3 production and its connection to survival in patients with ccRCC. Our team evaluated the effects of knockdown of CBX3 levels in ccRCC cell populations using in vitro together with in vivo models. CBX3, proteins related to death, and epithelial-to-mesenchymal transition (EMT)-related proteins were measured in ccRCC cells using western blotting and immunohistochemical assays. Through the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneOntology (GO) and Gene Set Enrichment Analysis (GSEA), the biological processes and signal pathways related to CBX3 expression were identified. Immune-related activity reduced by CBX3 was assessed using various online tools.

RESULTS

Both genomic and protein expression showed that CBX3 was upregulated in ccRCC. Further functional analyses revealed that CBX3 played a crucial role in enhancing cell growth, migration, and EMT in vitro along with in vivo. Moreover, the study results provided distinct mechanistic evidence that CBX3 exerts its pathological functions in ccRCC by activating the PI3K/AKT pathway. Finally, immunoassays revealed that CBX3, a possible biomarker of ccRCC, was significantly associated with immunity.

CONCLUSIONS

Our results suggest that the overexpression of CBX3 promotes ccRCC advancement through PI3K/AKT activation and even immunological dysregulation, making it a potentially viable and beneficial therapeutic target.

PI3K/AKT/mTOR Dysregulation and Reprogramming Metabolic Pathways in Renal Cancer: Crosstalk with the VHL/HIF Axis

Renal cell carcinoma (RCC) represents 85-95% of kidney cancers and is the most frequent type of renal cancer in adult patients. It accounts for 3% of all cancer cases and is in 7th place among the most frequent histological types of cancer. Clear cell renal cell carcinoma (ccRCC), accounts for 75% of RCCs and has the most kidney cancer-related deaths. One-third of the patients with ccRCC develop metastases. Renal cancer presents cellular alterations in sugars, lipids, amino acids, and nucleic acid metabolism. RCC is characterized by several metabolic dysregulations including oxygen sensing (VHL/HIF pathway), glucose transporters (GLUT 1 and GLUT 4) energy sensing, and energy nutrient sensing cascade. Metabolic reprogramming represents an important characteristic of the cancer cells to survive in nutrient and oxygen-deprived environments, to proliferate and metastasize in different body sites. The phosphoinositide 3-kinase-AKT-mammalian target of the rapamycin (PI3K/AKT/mTOR) signaling pathway is usually dysregulated in various cancer types including renal cancer. This molecular pathway is frequently correlated with tumor growth and survival. The main aim of this review is to present renal cancer types, dysregulation of PI3K/AKT/mTOR signaling pathway members, crosstalk with VHL/HIF axis, and carbohydrates, lipids, and amino acid alterations.