Abstract
Objectives
Crebanine, an alkaloid exhibiting sedative, anti-inflammatory, and anticancer properties, remains unexplored in terms of its anticancer potential against colorectal cancer (CRC). This study aims to bridge this knowledge gap, specifically investigating whether crebanine can suppress CRC and elucidating its underlying molecular mechanism.
Methods
We employed the MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide) assay, cell scratch assay, and flow cytometry to observe the effects of crebanine on the growth, migration, and apoptosis of CRC SW480 cells, respectively. High-throughput sequencing was employed to detect differentially expressed genes (DEGs) in SW480 cells treated with crebanine. Enriched pathways of these DEGs were identified through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Genes exhibiting the highest correlation in the enriched pathway were further analyzed using clinical data from The Cancer Genome Atlas Program (TCGA) public database, utilizing R software.
Results
Crebanine effectively inhibited the proliferation, migration, and invasion of SW480 cells, with concentrations of ≥15 μg/mL promoting apoptosis. Analysis revealed that the function of DEGs linked to the most enriched pathways was associated with immune infiltration by regulatory T cells (Tregs). When analyzed in conjunction with clinical data, the genes exhibiting the highest correlation in the enrichment pathway were found to be directly associated with clinical prognostic survival.
Conclusions
Our study demonstrates that crebanine inhibits colorectal cancer by regulating prognostic target genes related to Tregs. This finding offers a novel approach for pharmacological inhibition and Tregs-targeted therapy in CRC.
Introduction
In recent years, the incidence and mortality rates of colorectal cancer have been increasing in China. Although the incidence of colorectal cancer is decreasing in the United States, it remains firmly among the top five most common malignant tumors [1]. Multiple risk factors contribute to the development of colorectal cancer [2]. Significant progress has been made in the prevention, diagnosis, and treatment of colorectal cancer, including promoting healthy dietary habits, avoiding smoking and alcohol consumption, surgical resection, chemotherapy, radiotherapy, and neoadjuvant radiotherapy [3], [4], [5], [6]. Nevertheless, the survival rate for colorectal cancer patients remains extremely low [7].
Crebanine, a monomer compound extracted from Stephania Epigaea H. S. Lo, possesses diverse biological properties including analgesic, anti-inflammatory, and anti-tumor effects. Research demonstrates that crebanine exerts anti-inflammatory actions by suppressing the activation of nuclear factor-kappaB (NF-κB) and activator protein-1 (AP-1) via the inhibition of mitogen-activated protein kinase (MAPKs) and protein kinase B (Akt) signaling pathways in RAW264.7 macrophages [8]. Crebanine has also been demonstrated to be toxic towards leukemia cells, human fibrosarcoma cells, hepatocellular carcinoma cells, and gastric cancer cells [9]. For instance, suppressing the expression of matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), membrane type 1-matrix metalloproteinase (MT1-MMP), and urokinase-type plasminogen activator (uPA) effectively hindered the invasive capability of human fibrosarcoma cells [10]. This suggests that crebanine holds promise as an anticancer drug. Nevertheless, the specific mechanism of its action on colorectal cancer remains unclear.
Certain studies have demonstrated that Traditional Chinese Medicine (TCM) can enhance cancer patients’ sensitivity to chemotherapeutic agents and mitigate their adverse effects [11]. In this study, our primary objective was to investigate the impact of crebanine on SW480 cells’ growth, invasion, and migration capabilities, while elucidating the underlying molecular mechanisms. To accomplish this, we utilized cell scratch assay, MTT assay, and flow cytometry, enabling a quantitative assessment of crebanine’s effect on SW480 cells’ biological behavior. Additionally, we delved into the molecular mechanism underlying crebanine’s inhibition of SW480 cells, leveraging high-throughput sequencing technology. To validate aberrantly expressed mRNAs, bioinformatics analysis was conducted in tandem with the TCGA public database. These aberrantly expressed mRNAs hold potential as drug targets for colorectal cancer treatment.
Materials and methods
Materials
Crebanine (C20H21NO4), with a purity of 98.25 % and a Chemical Abstracts Service (CAS) number of 25127-29-1, was obtained from Chengdu Desite Biotechnology in China. Its structural formula is depicted in Figure 1A. A 4 mg/mL solution of crebanine was prepared using 0.2 M phosphate buffered solution (PBS, pH=7.4) from Solarbio (China), containing 0.1 % methyl sulfoxide (DMSO) also sourced from Solarbio.
Crebanine intervention in SW480 cell experiments. (A) The structural formula of crebanine. (B) MTT experiment. (C) Cell scratch experiment. The imaging magnification is 20× and the scale bar is 200 μm.
Cell cultures
Human colorectal carcinoma cells, SW480, were cultured in Roswell Park Memorial Institute 1640 (RPMI-1640) medium (Gibco, USA) supplemented with 10 % Fetal Bovine Serum (FBS) (Sigma, Australia) and 1 % penicillin–streptomycin (Solarbio, China). Cells were maintained in a humidified incubator at 37 °C and 5 % CO2, with media changes every two days. Passage was performed once cell confluence reached approximately 80 %.
MTT assay was conducted to determine the optimal concentration of crebanine that inhibits the proliferation of SW480 cells
After reviewing the literature, we learned that the IC50 value of crebanine was 6.5 μg/mL [12], and the five experimental concentrations of 5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, and 30 μg/mL were finally selected in combination with the feedback from the pre-experiment. Cells were inoculated in 96-well plates at a density of 5×104 cells per well using a cell suspension. After 24 h of adherent cell growth, various concentrations of crebanine (5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, and 30 μg/mL) were added to intervene in the culture. Subsequently, a 5 mg/mL MTT solution was introduced to assess the number of viable cells. Following a 4 h incubation period, the supernatant was carefully aspirated and discarded. DMSO was then added, and the mixture was shaken for 15 min to disrupt the cell membranes. The absorbance was measured at 490 nm using iMark™ Microplate Absorbance Reader (1681130, Bio-Rad, USA). Finally, the cell inhibition rate was calculated based on these measurements.
Assessment of the invasive and migratory effects of crebanine on SW480 cells using a cell scratch assay
Cells were inoculated in a 6-well plate at a density of 6×105 cells per well using a cell suspension. Once the cells adhered to the plate and reached approximately 80 % confluence, a wound was created in each well using a sterile 200 µL pipette tip. Subsequently, the cells were washed twice with 2 mL of PBS. Fresh medium containing various concentrations of crebanine (5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, 30 μg/mL) was added to continue the culture. The progress of scratch repair was monitored every 24 h.
Flow cytometry was used to investigate the apoptotic effect of crebanine on SW480 cells
A cell suspension was inoculated into 6-well plates at a density of 6×105 cells per well. After 24 h of adherent growth, 3 mL of crebanine, containing various concentration gradients (5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, and 30 μg/mL), were added to each cell well. The cells were then digested with EDTA-free trypsin, washed once with PBS, and labeled with fluorescein isothiocyanate (FITC) and propidium iodide (PI) according to the Annexin V-FITC/PI Apoptosis Detection Kit (556547, BD, USA) instructions. Finally, the apoptosis rate was determined using a flow cytometer (BF-700, URIT, China).
High-throughput mRNA sequencing to screen DEGs
SW480 cells were treated with various concentrations of crebanine (5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, 30 μg/mL) for 72 h. Untreated cells served as the control group, while cells exposed to crebanine comprised the experimental group. Subsequently, the samples were submitted to Shenzhen Huada Gene Co. (China) for RNA sequencing. The differentially expressed mRNAs were analyzed using the DNBSEQ-T7 platform (Huada, China). DEGs were identified based on the criteria of |log2FC| ≥1 and p<0.05. These genes were further classified into low and high expression groups based on log2FC values. Volcano plots were generated to visualize the distribution of the two expression groups, and a gene clustering heatmap was produced to demonstrate the differential expression levels through color coding.
GO and KEGG enrichment analysis for functional pathways of DEGs
Using the Dr. Tom system of Beijing Genomics Institution (https://biosys.bgi.com/), we conducted both KEGG and GO enrichment analyses on the intersected DEGs. The GO analysis encompassed three aspects: cellular composition (CC), biological process (BP), and molecular function (MF). For the KEGG analysis, we mapped the genes to be analyzed to various functional modules in the KEGG database (https://www.genome.jp/kegg/), such as metabolic and signaling pathways, to assess their significance within the gene collection.
Bioinformatics analysis of the interested DEGs in the highest correlation enrichment pathway
We aim to further analyze the most significant signaling pathways enriched by the R software (version 4.3.1) and TCGA public database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga), focusing on their corresponding DEGs. We retrieved clinical data from 41 normal and 476 CRC tissues from the TCGA database. Utilizing the ‘e1071’ (1.7.14), ‘parallel’ (4.3.1), and ‘preprocessCore’ (1.62.1) packages in R, we calculated the immune cell content in each clinical sample. The immune cell infiltration analysis results were then processed using R software, and highly correlated genes were grouped into a module through a gene clustering algorithm. A heat map was generated to visualize the correlation between the gene module and immune cells, with the color block indicating the highest correlation having the smallest p-value. The genes with the highest correlation were compared with the sequencing-derived DEGs to identify their intersection. These intersecting genes represent the key targets of crebanine’s inhibitory effect on CRC through immune prognosis-related pathways.
Screening crebanine-regulated core target genes from DEGs
We aim to screen for genes linked to CRC’s clinical prognostic features among intersecting DEGs, which are the primary targets for crebanine’s inhibitory effects on CRC. Utilizing the ‘glmnet’ (4.1.8) and ‘survival’ (3.5.7) packages in R software, we categorized 452 sets of clinical data from the TCGA database into high- and low-risk groups, based on risk scores. A lasso regression model was then constructed to identify the point with the lowest cross-validation error, and the intersecting DEGs corresponding to this point were deemed the most significant prognostic genes for lasso regression. To further validate the reliability of these prognostic DEGs, we downloaded clinical prognostic data of 156 CRC patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Based on this data, we plotted various charts, including risk curves, ROC curves, forest plots for independent prognostic analyses, Nomo plots, waterfall plots for mutation frequency, survival curves for tumor mutation load, and bubble plots for correlation of risk scores for immune cells. These charts were generated for each clinical characteristic and the composite risk score. We employed R software for data analysis and visualization, utilizing various packages including ‘limma’ (3.56.2), ‘e1071’ (1.7.14), ‘parallel’ (4.3.1), ‘preprocessCore’ (1.62.1), ‘pheatmap’ (1.0.12), ‘corrplot’ (0.92), ‘GO.db’ (3.17.0), ‘impute’ (1.74.1), ’matrixStats’ (1.0.0), ‘Hmisc’ (5.1.1), ‘foreach’ (1.5.2), ‘fastcluster’ (1.2.3), ‘dynamicTreeCut’ (1.63.1), ‘survival’ (3.5.7), ‘sva’ (3.48.0), ‘glmnet’ (4.1.8), ‘survminer’ (0.4.9), ‘timeROC’ (0.4), ‘regplot’ (1.1), ’rms’ (6.7.1), ‘plyr’ (1.8.8), ‘ggplot2’ (3.4.4), ‘ggpubr’ (0.6.0), ’maftools’ (2.16.0), and ‘reshape2’ (1.4.4). To validate the expression changes of prognosis-related genes in SW480 cells, we conducted RT-qPCR. RNA was extracted from crebanine-treated SW480 cells using the GeneJET RNA Purification Kit (K0731, Thermo Fisher Scientific, USA). Cells were detached from the culture dish with tryptic digestion and centrifuged at 250×g for 5 min to discard the supernatant. The cell pellet was resuspended in 600 μL of lysate containing DTT (dithiothreitol) and vortexed for 10 s. Then, 360 μL of anhydrous ethanol was added and mixed by pipetting. The lysate was loaded into a purification column with a collection tube and centrifuged at 12,000×g for 1 min. The column was washed twice with 700 and 600 μL of rinse solutions, respectively, followed by a final wash with 250 μL of rinse solution. After centrifuging at 12,000×g for 2 min, the purification column was transferred to a nuclease-free microcentrifuge tube. RNA was eluted by adding 100 μL of nuclease-free water to the column membrane and centrifuging at 12,000×g for 1 min. The purified RNA was then ready for reverse transcription into cDNA. This cDNA was then analyzed using the ABI7500 real-time fluorescence quantitative PCR instrument (Thermo Fisher Scientific, USA). The gene expression level was determined by the 2−∆∆Ct method, with GAPDH serving as the internal control. Finally, the results were graphically represented using GraphPad Prism 9.5 software. The t-test was employed to assess the significance of the results.
Statistical analysis
We analyzed all the data using GraphPad Prism, version 9.5. All differences between the two groups were assessed using the student’s t-test, with a p-value threshold of less than 0.05 considered statistically significant.
Results
Crebanine inhibits CRC cell growth
Compared with the control group, treatment of colorectal cancer cells with crebanine at concentrations of 5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, and 30 μg/mL inhibited cell proliferation (Figure 1B). The lowest effective concentration was 5 μg/mL, achieving an inhibition rate of 5.82 %, while the highest inhibitory concentration of 30 μg/mL crebanine yielded an inhibition rate of up to 75.59 %.
Crebanine inhibits CRC cell migration and invasion
Scratching experiments conducted at 24 h, 48 h, and 72 h revealed that the healing rate of cell scratches was significantly slower in the crebanine-treated cultures compared to the control group (Figure 1C). This finding indicated that crebanine notably impaired the migration capacity of SW480 cells.
Effect of graded concentrations of crebanine on apoptosis in CRC cells
The assay results revealed that low concentrations of crebanine did not trigger apoptosis in colorectal cancer cells, whereas high concentrations did. As depicted in Figure 2A, the Q1LR region represents early apoptotic cells, and the Q1UR region corresponds to late apoptotic cells. We used “Q1LR+Q1UR” to denote the total apoptotic cells. The apoptotic cell proportions in the crebanine-treated groups (5 μg/mL, 10 μg/mL, 15 μg/mL, 20 μg/mL, 30 μg/mL) were 7.64 %, 10.76 %, 21.17 %, 38.53 %, and 40.17 %, respectively. The rate of apoptosis steadily rose as the concentration of crebanine increased.
Apoptosis analysis and gene visualization by high-throughput sequencing. (A) Flow cytometry to detect the effect of crebanine on SW480 cell apoptosis. (B) Volcano plot of differentially expressed genes. (C) Clustering heat map of differentially expressed genes.
High-throughput mRNA sequencing enables the identification of DEGs
Using |log2FC| ≥1 and p<0.05 as criteria, mRNAs with differential expression were screened and visualized. The analysis results were presented in a volcano plot, where red dots represented 105 up-regulated DEGs, green dots represented 294 down-regulated DEGs, and gray dots indicated no DEG (Figure 2B). Additionally, a heat map of cluster analysis revealed the expression profiles of 399 DEGs between the experimental and control groups (Figure 2C).
The most enriched pathways among DEGs pertain to immune regulation
KEGG enrichment analysis indicated that the 399 DEGs were primarily implicated in signaling pathways, specifically nicotinate and nicotinamide metabolism (p=0.00247) and chemical carcinogenesis – DNA adducts (p=0.00932) (Figure 3A). Furthermore, GO-CC analysis revealed a significant enrichment of DEGs within the C/EBP homologous protein (CHOP) ATF3 complex (p=0.00351) and the serine C-palmitoyltransferase complex (p=0.00875) (Figure 3B). Additionally, GO-BP analysis highlighted a strong concentration of DEGs in the nicotinamide adenine dinucleotide (NAD) metabolic process (p=4.12e-4) (Figure 3C). GO-MF analysis revealed that the DEGs were primarily involved in androsterone dehydrogenase (A-specific) activity (p=0.0019) (Figure 3D). These functionally enriched pathways were strongly linked to CRC chemical high-risk exposure, immune stress injury, and poor clinical prognosis. To visualize the immune cell composition, we conducted immune infiltration analysis to determine the content of each immune cell type in each sample and generated a histogram (Figure 3E). Using a clustering algorithm, we grouped immune genes with high similarity into modules and generated a gene dendrogram for further analysis (Figure 3F). Using R software, we plotted a correlation heatmap between gene modules and immune cells, revealing that the “MEblue” colorectal cancer gene module exhibited the strongest correlation with Tregs (p=5e-46) (Figure 3G). Subsequently, we exported all 35 genes from the “MEblue” module, which coincided with the DEGs sequenced in crebanine-treated cell cultures (Figure 3H). Based on these findings, we hypothesized that crebanine may regulate these 35 genes associated with Tregs immune infiltration, thereby inhibiting CRC proliferation and migration (Table 1).
Pathway functional exploration of DEGs and analysis of CRC immune infiltration. (A) KEGG enrichment analysis. (B) GO-CC enrichment analysis. (C) GO-BP enrichment analysis. (D) GO-MF enrichment analysis. (E) CRC immune cell composition histogram. (F) CRC immune gene clustering dendrogram. (G) Heatmap of gene module-immune cell correlation. (H) Wayne diagram of Tregs-related gene modules taking intersections with sequencing-derived DEGs.
The 35 key differentially expressed genes (DEGs) for the inhibitory effect of crebanine.
| Gene ID | Gene symbol | Type | log2 (crebanine/control) | FDR (crebanine/control) |
|---|---|---|---|---|
| 10 | NAT2 | mRNA | −9.813781191 | 0.007809485 |
| 10,788 | IQGAP2 | mRNA | −1.031942893 | 0.000000000 |
| 11,221 | DUSP10 | mRNA | −1.059572445 | 0.000074700 |
| 130,367 | SGPP2 | mRNA | −1.000000000 | 0.000062900 |
| 145,781 | GCOM1 | mRNA | 12.454299290 | 0.000000000 |
| 163,071 | ZNF114 | mRNA | −1.070389328 | 0.018580210 |
| 1646 | AKR1C2 | mRNA | −1.581765746 | 0.018564371 |
| 165,679 | SPTSSB | mRNA | 1.361456459 | 0.000000000 |
| 166,824 | RASSF6 | mRNA | −1.266280065 | 0.000024000 |
| 257,019 | FRMD3 | mRNA | −1.041820176 | 0.001990579 |
| 25,924 | MYRIP | mRNA | −1.103093493 | 0.003434336 |
| 26,047 | CNTNAP2 | mRNA | −1.350497247 | 0.001121572 |
| 285,513 | GPRIN3 | mRNA | −1.186532234 | 0.000000000 |
| 285,755 | PPIL6 | mRNA | −1.061282462 | 0.005153868 |
| 3,161 | HMMR | mRNA | 1.275278002 | 0.000000000 |
| 340,547 | VSIG1 | mRNA | −2.807354922 | 0.009236232 |
| 3,426 | CFI | mRNA | −1.321928095 | 0.010227958 |
| 353,322 | ANKRD37 | mRNA | 1.081730372 | 0.000000000 |
| 414,328 | IDNK | mRNA | 2.070389328 | 0.005943528 |
| 467 | ATF3 | mRNA | −2.161554063 | 0.000000000 |
| 4,907 | NT5E | mRNA | 1.009267880 | 0.000000000 |
| 5,019 | OXCT1 | mRNA | 1.114796219 | 0.000000000 |
| 54,981 | NMRK1 | mRNA | 1.076815597 | 0.000003010 |
| 55,612 | FERMT1 | mRNA | 1.388386371 | 0.000000000 |
| 55,635 | DEPDC1 | mRNA | 1.055205502 | 0.000000000 |
| 55,659 | ZNF416 | mRNA | −1.099535674 | 0.000179000 |
| 63,901 | FAM111A | mRNA | 1.134485332 | 0.000000000 |
| 79,696 | ZC2HC1C | mRNA | −1.225985686 | 0.036543109 |
| 81,796 | SLCO5A1 | mRNA | −1.793549123 | 0.000032500 |
| 8,204 | NRIP1 | mRNA | −1.195550809 | 0.000000193 |
| 4,157 | MC1R | mRNA | −1.648738411 | 0.000723000 |
| 105,375,355 | UPK3B | mRNA | −9.813781191 | 0.007805728 |
| 200,765 | TIGD1 | mRNA | −1.229481846 | 0.000000000 |
| 4,100 | MAGEA1 | mRNA | −1.139090950 | 0.000000000 |
| 10,891 | PPARGC1A | mRNA | 2.860596943 | 0.000407000 |
The core target genes for crebanine intervention were pinpointed through lasso regression analysis
Using R software, we first conducted a lasso regression analysis (Figure 4A). Subsequently, a cross-validation model was established to identify the point with the minimal error in the cross-validation graph (Figure 4B). The five DEGs corresponding to this point represent the most significant genes in the lasso regression and are the CRC prognostic signature target genes we seek. These genes are melanocortin 1 receptor (MC1R), uroplakin 3b (UPK3B), tigger transposable element derived 1 (TIGD1), MAGE family member A1 (MAGEA1), and PPARG coactivator 1 alpha (PPARGC1A). To further validate the reliability of the prognostic genes, we categorized the 452 sets of TCGA clinical data (Figure 4C) and 156 GEO data cases (Figure 4D) into high-risk and low-risk groups, respectively, using the median riskscore as the cutoff. The survival status plots clearly depict the survival time and status of patients relative to their risk scores, with a notable increase in deaths over time among patients in the high-risk group (Figure 4E and F). Additionally, the risk heatmap reveals a strong association between the expression of prognostic genes and risk score. Our findings indicate that MC1R, UPK3B, TIGD1, and MAGEA1 are highly expressed in the high-risk group, while PPARGC1A is predominantly expressed in the low-risk group (Figure 4G and H). Based on these observations, we hypothesize that MC1R, UPK3B, TIGD1, and MAGEA1 are associated with a poor prognosis in CRC. To validate our inference, we conducted survival analysis on each of the five prognostic genes. The results revealed that patients with lower expression levels of MC1R (p<0.001), UPK3B (p=0.001), TIGD1 (p<0.001), and MAGEA1 (p<0.001) exhibited better prognoses compared to those with high expression levels (Figure 5A–D). Conversely, PPARGC1 (p<0.001) showed an opposite trend, with high expression correlating with a better prognosis than low expression (Figure 5E). This further confirmed our initial inference. To evaluate the prognostic significance of these genes, we conducted one-way and multifactorial Cox regression analyses. The results of the unifactorial analysis revealed that age (p=0.003), grade (p<0.001), stage (p<0.001), and riskscore (p<0.001) were significant risk factors for patient survival (Figure 5F). Furthermore, the multifactorial analysis confirmed that age (p<0.001), stage (p=0.007), and riskscore (p<0.001) were independent prognostic indicators (Figure 5G). The ROC curves demonstrated excellent predictive performance, with AUCs of 0.676, 0.718, and 0.751 at 1, 3, and 5 years, respectively (Figure 5H), all exceeding the threshold of 0.5. Integrating risk scores with clinical factors, we generated Nomograms (Figure 5I), enabling scientific predictions of patient survival. RT-qPCR results demonstrated that the expression of MC1R, UPK3B, TIGD1, and MAGEA1 decreased significantly in SW480 cells following crebanine intervention, in contrast to the control group (Figure 5J–M). Conversely, PPARGC1A expression was notably upregulated (Figure 5N). In summary, the five prognostic genes identified from 35 key DEGs effectively predict the survival outcomes of CRC patients. Specifically, MC1R, UPK3B, TIGD1, and MAGEA1 are high-risk prognostic genes, whereas PPARGC1A is a low-risk prognostic gene. Crebanine inhibits the onset and progression of CRC by modulating the expression of these five Tregs immune infiltration prognostic target genes.
Construction of a CRC prognostic model with the help of Tregs immune prognostic key genes, and dual validation of the accuracy of the prognostic model with TCGA data and GEO data. (A) Lasso regression graph. (B) Cross validation graph. (C) Risk profile for TCGA data. (D) Risk curves for GEO data. (E) Survival graph for TCGA data. (F) Survival state plot for GEO data. (G) Risk heat map for TCGA data. (H) Risk heatmap for GEO data.
Survival analysis of five key CRC prognostic genes, and assessment of survival prognosis of CRC patients. (A) Survival curve of MC1R gene. (B) Survival curve of UPK3B gene. (C) Survival curve of TIGD1 gene. (D) Survival curve of the MAGEA1 gene. (E) Survival curve of PPARGC1A gene. (F, G) Independent prognostic analysis. (H) ROC curves. (I) Nomo plot. RT-qPCR experiments for MC1R (J), UPK3B (K), TIGD1 (L), MAGEA1 (M) and PPARGC1A (N). **p<0.01; ***p<0.001.
Association between Tregs infiltration and prognosis in CRC patients
To assess differences in tumor mutation load between high- and low-risk groups, we utilized the “maftools” package in R software to generate a waterfall plot depicting gene mutation frequency and risk classification. Upon comparison, it is evident that the mutation frequencies of the top 20 CRC genes in the high-risk group surpass those in the low-risk group (Figure 6A and B). The survival curves revealed a significant difference in survival rates between the high and low mutation load groups (p=0.019), with the low mutation load group exhibiting a generally higher survival rate (Figure 6C). To delve deeper, we combined tumor mutation load with patient risk for further analysis. This combined analysis revealed a stark difference in survival among the four groups (p<0.001), with the lowest mutation and risk group having the highest survival rate, and the highest mutation and risk group exhibiting the lowest survival rate (Figure 6D). To comprehensively assess the immune cells associated with CRC patient risk scores, we generated a correlation bubble plot using R software (Figure 6E). The horizontal axis depicts the correlation coefficient between immune cells and patient risk scores, with positive values indicating a positive correlation and negative values indicating a negative correlation. Given that a prior study demonstrated crebanine’s inhibitory effects on CRC proliferation and migration via Tregs-related prognostic genes, we focused on exploring the correlation between Tregs, a subclass of immune cells, and CRC patient risk scores. Utilizing R software, we plotted a scatter plot revealing a potential negative correlation between Tregs and patient risk scores (R=0.16, p=6.7e-4) (Figure 6F).
Association analysis of Tregs infiltration degree with CRC risk prognosis. (A) Waterfall plot of CRC gene mutation frequency in the high-risk group. (B) Waterfall plot of CRC gene mutation frequency in the low-risk group. (C) Tumor mutation load survival curve. (D) Survival curves for tumor mutation load combined with risk score. (E) Bubble plot of immune cell correlation with risk score. (F) Scatter plot of Tregs correlation with risk score.
Discussion
This experiment centered on five prognostic target DEGs, with crebanine serving as a pivotal regulatory factor. MC1R is the gene responsible for encoding the melanocyte-stimulating hormone (MSH) receptor protein, a seven-transmembrane G-protein-coupled receptor. MC1R interacts with specific genes involved in DNA repair, jointly influencing an individual’s susceptibility to and recovery from ultraviolet (UV) damage [13]. Mutations in these genes can elevate the risk of barrier damage and cancer. Song et al. discovered that aberrant expression of MC1R elevates the likelihood of missense mutations during DNA repair [14], corroborating the finding of this study that MC1R serves as a high-risk prognostic gene for colorectal cancer (CRC). UPK3B is a protein-coding gene encoding an enzyme crucial for intracellular uridine triphosphate (UTP) metabolism. Lennartz et al. analyzed 151 tumors using tissue microarray immunohistochemistry and discovered that UPK3B was present in 17 of them, accounting for 11.3 % [15]. High expression of UPK3B is frequently linked to unfavorable clinicopathological characteristics, including tumor aggressiveness, metastatic potential, and a poorer prognosis. TIGD1 has been demonstrated to be overexpressed in various digestive system tumors, encompassing colorectal, gastric, liver, and pancreatic cancers [16]. TIGD1 was discovered to hinder the maturation of dendritic cells [17], facilitate the transition of tumor cells from G1 to S phase [18], and ultimately result in immunosuppression and tumor progression. Clinical analysis further corroborated the significant upregulation of TIGD1 expression in patients with lymph node and distant metastasis from colorectal cancer [18]. MAGEA1, the final high-risk prognostic gene, is a widely recognized tumor-associated antigen that carries significant prognostic implications in lung [19], bladder [20], and hepatocellular carcinomas [21]. Boshi Fu’s comprehensive study uncovered aberrant promoter methylation and subsequent aberrant gene expression as crucial epigenetic mechanisms underlying MAGEA1’s role in colorectal cancer progression [22]. The low-risk prognostic gene PPARGC1A, a regulator of energy metabolism crucial for mitochondrial biogenesis [23] in tumor cells, has been reported in several studies as a prognostic target gene in colorectal cancer [24], [25], [26]. Additionally, we have verified their differential expression in SW480 tumor cells using RT-qPCR. Furthermore, we aim to conduct in-depth studies on their regulatory mechanisms by establishing mouse models.
Crebanine holds the potential to inhibit CRC by modulating downstream signaling pathways and associated biological functions among the remaining 30 intersecting DEGs. The Warburg effect in colorectal cancer cells, despite aerobic conditions, remains characterized by elevated glycolysis and lactic acid fermentation, with NAT2 in intersecting DEGs playing a mediating role in this pathological process [27]. Enhanced glycolysis and lactic acid fermentation not only support biosynthesis but also consistently furnish energy for tumor cell growth and proliferation [28], [29], [30], [31]. IQGAP2, a member of the IQGAP family, serves as a repressor in numerous cancers. IQGAP2 promotes apoptosis in breast cancer cells via the ROS-p38-p53 pathway [32] and reduces epithelial-mesenchymal transition (EMT) in bladder cancer cells through a MEK-ERK-dependent mechanism [33]. This aligns with our experimental observation that crebanine concentrations of ≥15 μg/mL enhance apoptosis in colorectal cancer cells. DUSP10, alternatively named MAP kinase phosphatase 5 (MKP5), exerts negative regulatory effects on p38 MAPK and c-Jun N-terminal kinase (JNK) across diverse cellular and tissue types [34]. Compared to wild-type (WT) mice, DUSP10 knockout (KO) mice exhibited a significant increase in both the number and severity of colon tumors following the administration of dextrose sulfate sodium (DSS) and azomethane (AOM) [35]. This indicates that both DUSP10 and IQGAP2 share similar tumor suppressor functions. Hypermethylation of RASSF6 is a prevalent phenomenon across multiple types of solid tumors. microRNA-496 (miR-496) represents an emerging oncomiR that exhibits direct binding to RASSF6. miR-496/RASSF6 axis was found to promote cell migration and epithelial–mesenchymal transition through activation of Wnt signaling [36], which shows that RASSF6 is a driving factor in colorectal cancer metastasis. Cancer development and progression is not merely an isolated pathologic process. Extensive epidemiologic evidence indicates a strong molecular correlation between type 2 diabetes (T2D) and the development of colorectal cancer [37], [38], [39], [40]. Recent research has revealed that HMMR, among intersecting DEGs, serves as a common hub gene linking type 2 diabetes (T2D) and colorectal cancer [41]. Additionally, studies on lung adenocarcinoma have shown that HMMR is also associated with immune infiltration levels of neutrophils, CD8+ T cells, and CD4+ T cells, and thus affects the prognosis of patients [42]. Autophagy, a fundamental catabolic process in eukaryotic cells, plays a crucial role in maintaining cellular and energy homeostasis, as well as cancer development [43], [44], [45], [46]. In solid tumors, hypoxia prompts the elevation of HIF-1α, which subsequently facilitates the nuclear translocation of ANKRD37 within intersecting DEGs, ultimately activating tumor cell autophagy [47]. The primary limitations of conventional antitumor therapies include resistance to chemotherapy, drug toxicity, as well as metastasis and recurrence. Therapies targeting Tregs in the tumor immune microenvironment are receiving increasing attention from experts [48]. The single-cell RNA sequencing data from colorectal cancer patients reveal that the Tregs-regulated glucose-responsive transcription factor TXNIP axis within the microenvironment suppresses glucose uptake and glycolysis in cancer cells [49], effectively slowing down their progression. It has also been confirmed that Tregs exhibit a gene signature oriented towards lipid synthesis. The signals emanating from the tumor microenvironment trigger lipid synthesis and oxidative circuits, thereby enabling Tregs to preferentially proliferate [50]. It is thus evident that Tregs can influence the progression and prognosis of colorectal cancer cells by modulating their glucose response and lipid metabolism.
In summary, the DEGs regulated by crebanine are intricately linked to energy supply, metabolic homeostasis, cellular autophagy, apoptosis, tumor microenvironment modulation, EMT propensity, and other vital processes in colorectal cancer cells. Further elucidation of their respective molecular mechanisms is necessary through subsequent studies.
Conclusions
This study corroborated that crebanine effectively suppresses the proliferation, migration, and invasion of colorectal cancer cells. High-throughput mRNA sequencing, coupled with bioinformatics analysis, revealed that crebanine primarily targets five prognostic genes linked to immune infiltration of Tregs in colorectal cancer. Notably, MC1R, UPK3B, TIGD1, and MAGEA1 emerged as high-risk prognostic genes for CRC, whereas PPARGC1A was identified as a low-risk marker. These findings offer promising new genetic markers for early diagnosis, chemoprevention, Tregs-targeted immunotherapy, and prognostic monitoring of colorectal cancer.
Funding source: Science and Technology Plan of Guilin
Award Identifier / Grant number: 20220139-8-4
Funding source: Guangxi Medical and health key cultivation discipline construction project and Guangxi Health Commission Key Laboratory of Glucose and Lipid Metabolism Disorders
Award Identifier / Grant number: 19-xkjs-05
-
Research ethics: The conducted research is not related to either human or animals use.
-
Informed consent: Not applicable.
-
Author contributions: Jiajun Xu: Data curation, writing – original draft. Lingyu Huang: Conceptualization, writing – original draft. Yu Sha: Investigation. Chune Mo: Project administration. Weiwei Gong: Supervision. Xiayu Tian: Software. Xianliang Hou: Visualization. Wei Chen: Resources. Minglin Ou: Funding acquisition, writing – review & editing. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Research funding: This work was supported by Science and Technology Plan of Guilin (20220139-8-4), Guangxi Medical and Health Key Cultivation Discipline Construction Project and Guangxi Health Commission Key Laboratory of Glucose and Lipid Metabolism Disorders (grant no. 19-xkjs-05).
-
Data availability: Data available on request from the authors. The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
1. Siegel, RL, Giaquinto, AN, Jemal, A. Cancer statistics, 2024. CA A Cancer J Clin 2024;74:12–49. https://doi.org/10.3322/caac.21820.Search in Google Scholar PubMed
2. Raskov, H, Pommergaard, HC, Burcharth, J, Rosenberg, J. Colorectal carcinogenesis – update and perspectives. World J Gastroenterol 2014;20:18151–64. https://doi.org/10.3748/wjg.v20.i48.18151.Search in Google Scholar PubMed PubMed Central
3. Dekker, E, Tanis, PJ, Vleugels, JLA, Kasi, PM, Wallace, MB. Colorectal cancer. Lancet 2019;394:1467–80. https://doi.org/10.1016/s0140-6736(19)32319-0.Search in Google Scholar
4. Rawla, P, Sunkara, T, Barsouk, A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Przegląd Gastroenterol 2019;14:89–103. https://doi.org/10.5114/pg.2018.81072.Search in Google Scholar PubMed PubMed Central
5. Taylor, FG, Quirke, P, Heald, RJ, Moran, B, Blomqvist, L, Swift, I, et al.. Preoperative high-resolution magnetic resonance imaging can identify good prognosis stage I, II, and III rectal cancer best managed by surgery alone: a prospective, multicenter, European study. Ann Surg 2011;253:711–9. https://doi.org/10.1097/sla.0b013e31820b8d52.Search in Google Scholar PubMed
6. Zhu, J, Lian, J, Xu, B, Pang, X, Ji, S, Zhao, Y, et al.. Neoadjuvant immunotherapy for colorectal cancer: right regimens, right patients, right directions? Front Immunol 2023;14:1120684. https://doi.org/10.3389/fimmu.2023.1120684.Search in Google Scholar PubMed PubMed Central
7. Engstrand, J, Nilsson, H, Strömberg, C, Jonas, E, Freedman, J. Colorectal cancer liver metastases – a population-based study on incidence, management and survival. BMC Cancer 2018;18:78. https://doi.org/10.1186/s12885-017-3925-x.Search in Google Scholar PubMed PubMed Central
8. Yodkeeree, S, Ooppachai, C, Pompimon, W, Limtrakul Dejkriengkraikul, P. O-Methylbulbocapnine and dicentrine suppress LPS-induced inflammatory response by blocking NF-κB and AP-1 activation through inhibiting MAPKs and Akt signaling in RAW264.7 macrophages. Biol Pharm Bull 2018;41:1219–27. https://doi.org/10.1248/bpb.b18-00037.Search in Google Scholar PubMed
9. Wongsirisin, P, Yodkeeree, S, Pompimon, W, Limtrakul, P. Induction of G1 arrest and apoptosis in human cancer cells by crebanine, an alkaloid from Stephania venosa. Chem Pharm Bull 2012;60:1283–9. https://doi.org/10.1248/cpb.c12-00506.Search in Google Scholar PubMed
10. Yodkeeree, S, Wongsirisin, P, Pompimon, W, Limtrakul, P. Anti-invasion effect of crebanine and O-methylbulbocapnine from Stephania venosa via down-regulated matrix metalloproteinases and urokinase plasminogen activator. Chem Pharm Bull 2013;61:1156–65. https://doi.org/10.1248/cpb.c13-00584.Search in Google Scholar PubMed
11. Zhang, Y, Lou, Y, Wang, J, Yu, C, Shen, W. Research status and molecular mechanism of the traditional Chinese Medicine and antitumor therapy combined strategy based on tumor microenvironment. Front Immunol 2020;11:609705. https://doi.org/10.3389/fimmu.2020.609705.Search in Google Scholar PubMed PubMed Central
12. Rojsanga, P, Boonyarat, C, Utsintong, M, Nemecz, Á, Yamauchi, JG, Talley, TT, et al.. The effect of crebanine on memory and cognition impairment via the alpha-7 nicotinic acetylcholine receptor. Life Sci 2012;91:107–14. https://doi.org/10.1016/j.lfs.2012.06.017.Search in Google Scholar PubMed
13. Hacker, E, Boyce, Z, Kimlin, MG, Wockner, L, Pollak, T, Vaartjes, SA, et al.. The effect of MC1R variants and sunscreen on the response of human melanocytes in vivo to ultraviolet radiation and implications for melanoma. Pigment Cell Melanoma Res 2013;26:835–44. https://doi.org/10.1111/pcmr.12157.Search in Google Scholar PubMed
14. Song, D, Zhang, D, Chen, S, Wu, J, Hao, Q, Zhao, L, et al.. Identification and validation of prognosis-associated DNA repair gene signatures in colorectal cancer. Sci Rep 2022;12:6946. https://doi.org/10.1038/s41598-022-10561-w.Search in Google Scholar PubMed PubMed Central
15. Lennartz, M, Atug, D, Dwertmann Rico, S, Reiswich, V, Viehweger, F, Büscheck, F, et al.. Analysis of more than 16,000 human tumor and normal tissues identifies uroplakin 3B as a useful diagnostic marker for mesothelioma and normal mesothelial cells. Diagnostics 2022;12:2516. https://doi.org/10.3390/diagnostics12102516.Search in Google Scholar PubMed PubMed Central
16. Yin, L, Yan, J, Wang, Y, Sun, Q. TIGD1, a gene of unknown function, involves cell-cycle progression and correlates with poor prognosis in human cancer. J Cell Biochem 2019;120:9758–67. https://doi.org/10.1002/jcb.28256.Search in Google Scholar PubMed
17. Qiao, X, Zhu, L, Song, R, Shang, C, Guo, Y. A novel oncogene trigger transposable element derived-1 promotes oral squamous cell carcinoma progression via evoking immune inhibition. Mol Carcinog 2023;62:1213–27. https://doi.org/10.1002/mc.23557.Search in Google Scholar PubMed
18. Zou, J, Zhang, H, Wu, Z, Hu, W, Zhang, T, Xie, H, et al.. TIGD1 is an independent prognostic factor that promotes the progression of colon cancer. Cancer Biother Radiopharm 2022;39:223–35. https://doi.org/10.1089/cbr.2022.0052.Search in Google Scholar PubMed
19. Luo, B, Mao, G, Ma, H, Chen, S. The role of seven autoantibodies in lung cancer diagnosis. J Thorac Dis 2021;13:3660–8. https://doi.org/10.21037/jtd-21-835.Search in Google Scholar PubMed PubMed Central
20. Sun, X, Xin, S, Li, W, Zhang, Y, Ye, L. Discovery of Notch pathway-related genes for predicting prognosis and tumor microenvironment status in bladder cancer. Front Genet 2022;13:928778. https://doi.org/10.3389/fgene.2022.928778.Search in Google Scholar PubMed PubMed Central
21. Zang, C, Zhao, Y, Qin, L, Liu, G, Sun, J, Li, K, et al.. Distinct tumour antigen-specific T-cell immune response profiles at different hepatocellular carcinoma stages. BMC Cancer 2021;21:1007. https://doi.org/10.1186/s12885-021-08720-9.Search in Google Scholar PubMed PubMed Central
22. Fu, B, Du, C, Wu, Z, Li, M, Zhao, Y, Liu, X, et al.. Analysis of DNA methylation-driven genes for predicting the prognosis of patients with colorectal cancer. Aging 2020;12:22814–39. https://doi.org/10.18632/aging.103949.Search in Google Scholar PubMed PubMed Central
23. Zhang, X, Liang, C, Zhou, B, Pang, L. Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance. J Mol Recogn 2023;36:e3044. https://doi.org/10.1002/jmr.3044.Search in Google Scholar PubMed
24. Miao, Y, Zhang, H, Su, B, Wang, J, Quan, W, Li, Q, et al.. Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer. PeerJ 2021;9:e11219. https://doi.org/10.7717/peerj.11219.Search in Google Scholar PubMed PubMed Central
25. Ma, XB, Xu, YY, Zhu, MX, Wang, L. Prognostic signatures based on thirteen immune-related genes in colorectal cancer. Front Oncol 2020;10:591739. https://doi.org/10.3389/fonc.2020.591739.Search in Google Scholar PubMed PubMed Central
26. Liang, Z, Sun, R, Tu, P, Liang, Y, Liang, L, Liu, F, et al.. Immune-related gene-based prognostic index for predicting survival and immunotherapy outcomes in colorectal carcinoma. Front Immunol 2022;13:944286. https://doi.org/10.3389/fimmu.2022.944286.Search in Google Scholar PubMed PubMed Central
27. Liu, C, Liu, D, Wang, F, Xie, J, Liu, Y, Wang, H, et al.. Identification of a glycolysis- and lactate-related gene signature for predicting prognosis, immune microenvironment, and drug candidates in colon adenocarcinoma. Front Cell Dev Biol 2022;10:971992. https://doi.org/10.3389/fcell.2022.971992.Search in Google Scholar PubMed PubMed Central
28. Chen, L, Yuan, R, Wen, C, Liu, T, Feng, Q, Deng, X, et al.. E3 ubiquitin ligase UBR5 promotes pancreatic cancer growth and aerobic glycolysis by downregulating FBP1 via destabilization of C/EBPα. Oncogene 2021;40:262–76. https://doi.org/10.1038/s41388-020-01527-1.Search in Google Scholar PubMed
29. Fukushi, A, Kim, HD, Chang, YC, Kim, CH. Revisited metabolic control and reprogramming cancers by means of the Warburg effect in tumor cells. Int J Mol Sci 2022;23:10037. https://doi.org/10.3390/ijms231710037.Search in Google Scholar PubMed PubMed Central
30. Lin, S, Li, Y, Wang, D, Huang, C, Marino, D, Bollt, O, et al.. Fascin promotes lung cancer growth and metastasis by enhancing glycolysis and PFKFB3 expression. Cancer Lett 2021;518:230–42. https://doi.org/10.1016/j.canlet.2021.07.025.Search in Google Scholar PubMed PubMed Central
31. Zheng, T, Jäättelä, M, Liu, B. pH gradient reversal fuels cancer progression. Int J Biochem Cell Biol 2020;125:105796. https://doi.org/10.1016/j.biocel.2020.105796.Search in Google Scholar PubMed
32. Kumar, D, Patel, SA, Hassan, MK, Mohapatra, N, Pattanaik, N, Dixit, M. Reduced IQGAP2 expression promotes EMT and inhibits apoptosis by modulating the MEK-ERK and p38 signaling in breast cancer irrespective of ER status. Cell Death Dis 2021;12:389. https://doi.org/10.1038/s41419-021-03673-0.Search in Google Scholar PubMed PubMed Central
33. Song, F, Kotolloshi, R, Gajda, M, Hölzer, M, Grimm, MO, Steinbach, D. Reduced IQGAP2 promotes bladder cancer through regulation of MAPK/ERK pathway and cytokines. Int J Mol Sci 2022;23:13508. https://doi.org/10.3390/ijms232113508.Search in Google Scholar PubMed PubMed Central
34. Jiménez-Martínez, M, Stamatakis, K, Fresno, M. The dual-specificity phosphatase 10 (DUSP10): its role in cancer, inflammation, and immunity. Int J Mol Sci 2019;20:1626. https://doi.org/10.3390/ijms20071626.Search in Google Scholar PubMed PubMed Central
35. Png, CW, Weerasooriya, M, Guo, J, James, SJ, Poh, HM, Osato, M, et al.. DUSP10 regulates intestinal epithelial cell growth and colorectal tumorigenesis. Oncogene 2016;35:206–17. https://doi.org/10.1038/onc.2015.74.Search in Google Scholar PubMed
36. Wang, H, Yan, B, Zhang, P, Liu, S, Li, Q, Yang, J, et al.. MiR-496 promotes migration and epithelial-mesenchymal transition by targeting RASSF6 in colorectal cancer. J Cell Physiol 2020;235:1469–79. https://doi.org/10.1002/jcp.29066.Search in Google Scholar PubMed
37. Amshoff, Y, Maskarinec, G, Shvetsov, YB, Raquinio, PH, Grandinetti, A, Setiawan, VW, et al.. Type 2 diabetes and colorectal cancer survival: the multiethnic cohort. Int J Cancer 2018;143:263–8. https://doi.org/10.1002/ijc.31311.Search in Google Scholar PubMed PubMed Central
38. Gutiérrez-Salmerón, M, Lucena, SR, Chocarro-Calvo, A, García-Martínez, JM, Martín Orozco, RM, García-Jiménez, C. Metabolic and hormonal remodeling of colorectal cancer cell signalling by diabetes. Endocr Relat Cancer 2021;28:R191–r206. https://doi.org/10.1530/erc-21-0092.Search in Google Scholar PubMed
39. Gutiérrez-Salmerón, M, Lucena, SR, Chocarro-Calvo, A, García-Martínez, JM, Martín Orozco, RM, García-Jiménez, C. Remodelling of colorectal cancer cell signalling by microbiota and immunity in diabetes. Endocr Relat Cancer 2021;28:R173–r90. https://doi.org/10.1530/erc-20-0315.Search in Google Scholar PubMed
40. Lv, X, Ding, P, Li, L, Li, L, Zhou, D, Wang, X, et al.. Increased α-HB links colorectal cancer and diabetes by potentiating NF-κB signaling. Mol Metabol 2023;75:101766. https://doi.org/10.1016/j.molmet.2023.101766.Search in Google Scholar PubMed PubMed Central
41. Nayan, SI, Rahman, MH, Hasan, MM, Raj, S, Almoyad, MAA, Liò, P, et al.. Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities. Life Sci 2023;335:122244. https://doi.org/10.1016/j.lfs.2023.122244.Search in Google Scholar PubMed
42. Ma, X, Xie, M, Xue, Z, Yao, J, Wang, Y, Xue, X, et al.. HMMR associates with immune infiltrates and acts as a prognostic biomaker in lung adenocarcinoma. Comput Biol Med 2022;151:106213. https://doi.org/10.1016/j.compbiomed.2022.106213.Search in Google Scholar PubMed
43. Debnath, J, Gammoh, N, Ryan, KM. Autophagy and autophagy-related pathways in cancer. Nat Rev Mol Cell Biol 2023;24:560–75. https://doi.org/10.1038/s41580-023-00585-z.Search in Google Scholar PubMed PubMed Central
44. Fleming, A, Bourdenx, M, Fujimaki, M, Karabiyik, C, Krause, GJ, Lopez, A, et al.. The different autophagy degradation pathways and neurodegeneration. Neuron 2022;110:935–66. https://doi.org/10.1016/j.neuron.2022.01.017.Search in Google Scholar PubMed PubMed Central
45. Li, W, He, P, Huang, Y, Li, YF, Lu, J, Li, M, et al.. Selective autophagy of intracellular organelles: recent research advances. Theranostics 2021;11:222–56. https://doi.org/10.7150/thno.49860.Search in Google Scholar PubMed PubMed Central
46. Li, X, He, S, Ma, B. Autophagy and autophagy-related proteins in cancer. Mol Cancer 2020;19:12. https://doi.org/10.1186/s12943-020-1138-4.Search in Google Scholar PubMed PubMed Central
47. Deng, M, Zhang, W, Yuan, L, Tan, J, Chen, Z. HIF-1a regulates hypoxia-induced autophagy via translocation of ANKRD37 in colon cancer. Exp Cell Res 2020;395:112175. https://doi.org/10.1016/j.yexcr.2020.112175.Search in Google Scholar PubMed
48. Alvisi, G, Termanini, A, Soldani, C, Portale, F, Carriero, R, Pilipow, K, et al.. Multimodal single-cell profiling of intrahepatic cholangiocarcinoma defines hyperactivated Tregs as a potential therapeutic target. J Hepatol 2022;77:1359–72. https://doi.org/10.1016/j.jhep.2022.05.043.Search in Google Scholar PubMed
49. Lu, Y, Li, Y, Liu, Q, Tian, N, Du, P, Zhu, F, et al.. MondoA-thioredoxin-interacting protein axis maintains regulatory T-cell identity and function in colorectal cancer microenvironment. Gastroenterology 2021;161:575–91.e16. https://doi.org/10.1053/j.gastro.2021.04.041.Search in Google Scholar PubMed
50. Pacella, I, Procaccini, C, Focaccetti, C, Miacci, S, Timperi, E, Faicchia, D, et al.. Fatty acid metabolism complements glycolysis in the selective regulatory T cell expansion during tumor growth. Proc Natl Acad Sci U S A 2018;115:E6546–e55. https://doi.org/10.1073/pnas.1720113115.Search in Google Scholar PubMed PubMed Central
© 2024 the author(s), published by De Gruyter, Berlin/Boston
This work is licensed under the Creative Commons Attribution 4.0 International License.
