Highly expressed SLCO1B3 inhibits the occurrence and development of breast cancer and can be used as a clinical indicator of prognosis

Transcriptome analysis of healthy and fatty liver revealed that inhibition of SLCO1B3 induces abnormal liver metabolism and lipid synthesis

The liver serves as the central organ for lipid metabolism, making it a crucial component of chicken physiology. However, the intricate regulation of lipid absorption, synthesis, decomposition, and transport within the liver is influenced by various factors, such as environmental conditions, diet, and genetics. Recent research has suggested that numerous functional genes and transcription factors play a pivotal role in liver metabolism via different molecular mechanisms. In this study, we examined the transcriptomes of both healthy and fatty chicken livers to better understand the role of functional genes in chicken liver fat metabolism. Our bioinformatics analysis of RNA-seq data revealed differential expression of SLCO1B3 in healthy liver and fatty liver, with lower ex-pression levels observed in fatty liver. To further investigate the potential role of SLCO1B3 in liver metabolism, we conducted in vitro experiments to knock down its expression in primary hepatocytes. Our results indicated that SLCO1B3 could suppress lipogenesis, hepatocyte apoptosis, and inflammation. These findings provide insight into the molecular mechanism of SLCO1B3 as a functional gene capable of regulating fat metabolism in chicken liver, and may contribute to ad-dressing the issue of fatty liver in chicken.

A novel metabolism-related prognostic gene development and validation in gastric cancer

BACKGROUND

The importance of metabolism-related alterations in the development of gastric cancer (GC) is increasingly recognized. The present study aimed to identify metabolism-related genes to facilitate prognosis of GC patients.

METHODS

Gene expression datasets and clinical information of GC patients were downloaded from TCGA and GEO databases. We scored the enrichment of human metabolism-related pathways (n = 86) in GC samples by GSV, constructed prognostic risk models using LASSO algorithm and multivariate Cox regression analysis, combined with clinical information to construct a nomogram, and finally cis score algorithm to analyze the abundance of immune-related cells in different subtypes. We used Weka software to screen for prognosis-related marker genes and finally validated the expression of the selected genes in clinical cancer patient tissues.

RESULTS

We identified that two GC metabolism-related signatures were strongly associated with OS and the levels of immune cell infiltration. Moreover, a survival prediction model for GC was established based on six GC metabolism-related genes. Time-dependent ROC analysis showed good stability of the risk prediction scoring model. The model was successfully validated in an independent ACRG cohort, and the expression trends of key genes were also verified in the GC tissues of patients. DLX1, LTBP2, FGFR1 and MMP2 were highly expressed in the cluster with poorer prognosis while SLC13A2 and SLCO1B3 were highly expressed in the cluster with better prognosis.

CONCLUSIONS

We identified a risk predictive score model based on six metabolism-related genes related to survival, which may serve as prognostic indicators and potential therapeutic targets for GC.

The Role of Solute Carrier Transporters in Efficient Anticancer Drug Delivery and Therapy

Transporter-mediated drug resistance is a major obstacle in anticancer drug delivery and a key reason for cancer drug therapy failure. Membrane solute carrier (SLC) transporters play a crucial role in the cellular uptake of drugs. The expression and function of the SLC transporters can be down-regulated in cancer cells, which limits the uptake of drugs into the tumor cells, resulting in the inefficiency of the drug therapy. In this review, we summarize the current understanding of low-SLC-transporter-expression-mediated drug resistance in different types of cancers. Recent advances in SLC-transporter-targeting strategies include the development of transporter-utilizing prodrugs and nanocarriers and the modulation of SLC transporter expression in cancer cells. These strategies will play an important role in the future development of anticancer drug therapies by enabling the efficient delivery of drugs into cancer cells.

An N6-methyladenosine and target genes-based study on subtypes and prognosis of lung adenocarcinoma

Purpose: Lung adenocarcinoma (LUAD) is a highly lethal subtype of primary lung cancer with a poor prognosis. N6-methyladenosine (m⁶A), the most predominant form of RNA modification, regulates biological processes and has critical prognostic implications for LUAD. Our study aimed to mine potential target genes of m⁶A regulators to explore their biological significance in subtyping LUAD and predicting survival. Methods: Using gene expression data from TCGA database, candidate target genes of m⁶A were predicted from differentially expressed genes (DEGs) in tumor based on M⁶A2 Target database. The survival-related target DEGs identified by Cox-regression analysis was used for consensus clustering analysis to subtype LUAD. Uni-and multi-variable Cox regression analysis and LASSO Cox-PH regression analysis were used to select the optimal prognostic genes for constructing prognostic score (PS) model. Nomogram encompassing PS score and independent prognostic factors was built to predict 3-year and 5-year survival probability. Results: We obtained 2429 DEGs in tumor tissue, within which, 1267 were predicted to m⁶A target genes. A prognostic m⁶A-DEGs network of 224 survival-related target DEGs was established. We classified LUAD into 2 subtypes, which were significantly different in OS time, clinicopathological characteristics, and fractions of 12 immune cell types. A PS model of five genes (C1QTNF6, THSD1, GRIK2, E2F7 and SLCO1B3) successfully split the training set or an independent GEO dataset into two subgroups with significantly different OS time (p < 0.001, AUC = 0.723; p = 0.017, AUC = 0.705).A nomogram model combining PS status, pathologic stage, and recurrence was built, showing good performance in predicting 3-year and 5-year survival probability (C-index = 0.708, 0.723, p-value = 0). Conclusion: Using candidate m⁶A target genes, we obtained two molecular subtypes and designed a reliable five-gene PS score model for survival prediction in LUAD.

Lipid metabolism-related gene prognostic index (LMRGPI) reveals distinct prognosis and treatment patterns for patients with early-stage pulmonary adenocarcinoma

Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.

Genome-Wide Identification of m6A-Associated Single-Nucleotide Polymorphisms in Colorectal Cancer

Background

N6-methyladenosine (m6A)-associated single-nucleotide polymorphisms (SNPs) play important roles in cancers, with previous research suggesting potential associations between m6A-SNPs and cancer. However, the relationship between the genetic determinants of m6A modification and colorectal cancer (CRC) remains unclear.

Methods

An integrative method combining raw data and summary statistics of genome-wide association studies with expression quantitative trait loci (eQTL) and differential expression data was applied to screen potential candidate CRC-associated m6A-SNPs.

Results

A total of 402 m6A-SNPs were identified as being associated with CRC (p < 0.001), with 98 showing eQTL signals. In particular, three genes were found to harbor CRC-associated m6A-SNPs: rs178184 in NOVA1, rs35782901 in HTR4, and rs60571683 in SLCO1B3. These genes were differentially expressed in at least one publicly available dataset (p < 0.05), with NOVA1 (p = 3.41×10^-11) and HTR4 (p = 5.56×10^-7) being significantly downregulated in CRC (dataset: GSE89076), and SLCO1B3 was significantly overexpressed (datasets: GSE32323 [p = 3.27×10^-5], GSE21510 [p = 1.09×10^-6], and GSE89076 [p = 7.63×10^-6]).

Conclusion

This study identified three m6A-SNPs (rs178184, rs35782901, and rs60571683) that may be associated with CRC. However, the lack of analysis of primary CRC samples in order to further elucidate the underlying pathogenesis is a major limitation of this study. Future investigations are needed to validate these CRC-associated m6A-SNPs and explore the m6A-mediated pathogenic mechanism in CRC.