Culturing and identification of LCSCs derived from patients

Cancer stem cells were a subpopulation of cancer cells that drove the development of tumors [6]. Recently, we established a method for long-term culture of LCSCs derived from patients with liver cancers, the culture system was adopted and modified from those to culture CD34+ LCSCs isolated from PLC/PRF/5 cells as described in our early study [18]. MEFs as feeder layers were prepared one day before the isolation of liver cancer specimens, next day the specimen was treated into single cells and seeded at 1.5 × 105 -2.0 × 105 cells on MEFs per well. Then the growth of cloned cells could be observed within 3–14 days. We had successfully isolated 16 cloned cell strains from 24 patients, including three subtypes of liver cancers (Supplementary Table 3). The morphologies of CSCs from five patients were displayed with round and packed features (Fig. 1A), and those isolated CSCs could be maintained continuous culture over 12 passages (to date) and recovered from frozen cells (Fig. 1A). The morphological characteristics of all cloned LCSCs were shown in Supplementary materials (Fig. 1A, Figure S1A). We observed the proliferation of individual clones at days 3, 5, and 7 and performed KI67 staining to show the cell numbers of proliferating cloned cells (Fig. 1B). The cloned cells were dissociated into single cells and seeded a single cell into one well in 96-well plates using the limited dilution method to determine the clone formation rate of single stem cells. As expected, the clonogenicity of single stem cells could reach more than 80% (Fig. 1B), indicating unique characteristics of symmetric division of stem cells. Then, we evaluated putative CSC markers in these cloned cells by immunofluorescence, and the results showed that CD44, EPCAM, NCAM, CD133, AFP, SOX2, SOX9, OV6, ALDH1A1, and CD24, were expressed (Fig. 1C). Additionally, the cancer-associated fibroblasts marker (FAP) was not expressed in these cloned cells as determined by immunofluorescence (Figure S1B). Consistently, we found that the percentages of ALDH, as functioning marker of progenitor and stem cells, were significantly higher in cloned cells than that in primary tumor cells (Fig. 1D and E). Taken together, these results indicated that we successfully established a novel method for isolating and culturing patient-derived CSCs from primary liver cancers.

Fig. 1

Clonogenically culturing and identification of liver cancer stem cells (LCSCs). (A) Morphologies of three subtypes of LCSCs cultured on MEFs after continuous passages (Top panel) and recoveries (Bottom panel) after the cryopreservation. Scale bar 100 μm. (B) The proliferation of ALDH+ singe LCSCs cultured on MEFs at days 3, 5 and 7 (Left panel), scale bar 50 μm, and ki67 staining at days 3, 5 and 7 (Left panel). Scale bar 25 μm. The colony formation rate of single LCSCs. n = 3 biological replicates (Right panel). (C) Immunofluorescence staining showed that the cultured cloned cells expressed putative LCSC markers (CD44, NCAM, CD133, AFP, SOX2, SOX9, OV6, ALDH1A1, CD24). Scale bar 100 μm. (D, E) Determination of the percentages of ALDH positive cells between primary cancer cells and cloned cells. n = 3 biological replicates. Values were represented as mean ± SD. ****p < 0.0001

Cloned cells could be differentiated in vitro

Previous studies have implicated that cloned cells possessed stem cell characteristics. To further validate these findings, we performed the differentiation of cloned cells in vitro. The cloned cells were cultured in DMEM medium supplemented with 10% fetal bovine serum to induce the differentiation, and then the stemness characteristics of the cells under the two different culture conditions were assessed 7 days after the differentiation. After the differentiation, the morphology of the cloned cells changed from round colonies to polygons, resembling the morphology of ordinary liver tumor cells (Fig. 2A). Next, we assessed positive rate of ALDH in the differentiated cells. As expected, the percentages of ALDH positive cells were only about 20% (Fig. 2B and C). In addition, we found that previously reported liver stem cell markers (CD133, SOX2, NCAM1, CD90, SOX9) were downregulated after the differentiation in three different cloned cell lines (Fig. 2D). In contrast, the liver specific marker (ALB) was upregulated after the differentiation. Furthermore, two stem cell markers were further confirmed at the protein level, the results showed that SOX2 and SOX9 was highly expressed in the cloned cells, as determined by immunofluorescence (Fig. 2E). Collectively, all these results revealed that the stemness of cloned cells was decreased after the differentiation.

Fig. 2

Stemness analysis between LCSCs and differentiated cells. (A) Morphologies of LCSCs and differentiated cells. Scale bar 100 μm. (B, C) Determination of the percentages of ALDH positive cells between cloned cells and differentiated cells. n = 3 biological replicates. (D) The relative expression levels of putative LCSC marker genes (SOX2, SOX9, CD133, NCAM1, CD90) and differentiated marker gene (ALB) were quantified by qPCR in cloned and differentiated cells. n = 3 biological replicates. (E, F) Immunofluorescence staining was performed to detect the expressions of putative LCSC markers SOX2 (green) (E), and SOX9 (red) (F) in cloned and differentiated cells. Nucleus were counterstained by DAPI (blue), Scale bar 30 μm. Values were represented as mean ± SD. *P < 0.05 and **P < 0.01 and ***P < 0.001

Identification of tumorigenicity of liver cancer stem cells

To evaluate the tumorigenic ability of cloned cells, we sorted ALDH + cells from the cloned population and injected them subcutaneously into immunodeficient mice (Fig. 3A). Simultaneously, we injected parental tumor cells as controls, and the observation period for all mice was six months. Tumorigenic potential was observed within two months using 100 and 1,000 ALDH+ clone cells from three cell strains (HCC2, HCC12, HCC14). However, tumorigenicity with only 10 cells was observed exclusively in HCC12 cells. Then, we observed that the inoculation of 100 or 1,000 ALDH+ cloned cells resulted in almost 50% or more mice with the tumorigenesis. More importantly, the inoculation of 1,000 of HCC12 cloned cells resulted in 100% mice (8/8) with the tumorigenesis in about one month. In contrast, it was challenging for the primary tumor cells from patients to form xenografts even with the injection of 100,000 cells subcutaneously. The exception was the case from the cells of HCC14 patient, from which tumors were observed 111 days after the transplantation (Fig. 3B). Subsequently, we conducted hematoxylin and eosin (HE) staining of the xenografts produced by the cloned cells and compared it with the corresponding primary tumor tissues. The results indicated that the histologies of the xenografts was very similar to that of the primary liver cancers (Fig. 3C). Notably, the immunofluorescence staining results showed that the tissue of the xenografts also expressed liver-specific markers (ALB) and liver cancer markers (AFP) as the primary liver cancers did, further confirming that the cloned cells could generate tumors resembling the primary liver cancers (Fig. 3D). Furthermore, the statistical analysis of the immunofluorescence data also indicated that there were no significant differences between the two groups (Fig. 3E). It is extensively acknowledged that CSCs have a stronger ability to promote angiogenesis than ordinary tumor cells [33]. As expected, the tumors produced by cloned cells expressed higher level of CD31 (vascular marker) than those produced by primary tumor cells, which is consistent with previous reports (Fig. 3F, G and Figure S2). In conclusion, these in vivo experimental results demonstrated the characterization of cloned cells with tumorigenicity as CSCs. However, it appeared that clonal cells from different patients exhibited significant differences in tumor-forming ability. For example, HCC12 cells were able to form a greater number of tumors in a shorter period of time when compared to those from HCC2 cells, indicating higher expression levels of genes associated with proliferation to promote rapid tumor growth in HCC12 cells. These individual differences further confirmed the tumorigenic capacity of CSCs across patients.

Fig. 3

Tumorigenicity of LCSCs and primary liver cancer cells. (A) ALDH+ cells from cloned growth cells were sorted after the removal of MEFs and injected into NCG mice. (B) The ability of tumorigenicity in vivo between primary liver cancer cells and LCSCs. (C) Hematoxylin and eosin staining of primary liver cancer tissues from patients and LCSC-derived xenografts formd by the injection of LSCSs into mice. Scale bar 50 μm. (D) Human liver-specific proteins, AFP (green), ALB (purple) were expressed in primary liver cancer tissue (PHCC2) and LCSC-derived xenografts (MHCC2), as determined by immunofluorescence staining. Nucleus were stained by DAPI. Scale bar 50 μm. (E) Bar graph showed the statistical immunofluorescent intensity of AFP and ALB immunostaining in PHCC2 and MHCC2, n = 3. Values were represented as mean ± SD, ns indicates no statistical significance. (F) Tumor formations by primary liver cancer cells and LCSCs (Left panel). Vascular marker CD31(red) was detected by immunofluorescence staining in LCSC-derived xenograft and primary liver cancer cells (Right). Scale bar 50 μm. (G) The bar graph displayed the statistical immunofluorescent intensity of CD31 in tumor tissue formed by primary cells and LCSCs, n = 6. Values were represented as mean ± SD, *P < 0.05

Comparison of CSCs between suspension culture and culture with feeder cells

CSCs are commonly cultured using serum-free suspension culture to maintain their self-renewal ability. To compare the effects of two different culture methods on CSCs and to determine which is more effective in preserving the stemness and proliferative capabilities of CSCs, we sorted ALDH+ CSCs and cultured them on MEFs as feeder cells (MEF group) and in low-attachment plates for suspension culture (SP group). CSCs cultured on MEFs exhibited uniform clonal growth, whereas those in suspension culture formed spheroids with uneven sizes and shapes (Fig. 4A). The average diameters of clones on MEFs were larger than those of spheroids from suspension culture 5 to 7 days after culturing (Fig. 4B, Figure S3A). At day 7, we assessed the rate of ALDH-positive cells under two distinct culture conditions. About 87% of HCC2 cells cultured on MEFs were tested positive for ALDH, whereas ALDH positivity rate in suspended cells was only 51.9%. HCC14 cells showed a similar trend. These results indicated that CSCs cultured on MEFs possessed more pronounced stem cell characteristics (Fig. 4C, Figure S3B). To elucidate the differences between MEF-cultured cells and those cultured under suspension culture, we performed transcriptome sequencing, and found that the expression of stemness-associated genes in MEF-cultured cells, including LGR5, PROM1, SOX2, KLF4, ITGA6, NCAM1, LIF, and SOX9, were elevated. In contrast, the expression of liver differentiated gene ALB, hepatocyte marker, was increased in suspension-cultured spheroids (Fig. 4D), indicating the occurrence of partial differentiation. To validate results of the transcriptome sequencing, qPCR was performed to verify the expression of selected stemness genes, confirming that these genes were more highly expressed in MEF-cultured cells. In contrast, the expression of ALB gene was increased in suspended culture cells. Especially, HCC2 CSCs reduced the expression of CD133, CTNNB1, NCAM1, and SOX2, were significantly reduced to 18.68%±6.11%, 6.03%±1.06%, 0.12 ± 0.16%%, and 34.42%±3.84%, and ALB expression was 9.31 ± 1.71 times higher in suspended CSCs when compared to MEF-cultured cells (Fig. 4E). Gene Set Enrichment Analysis (GSEA) further revealed significant enrichment of upregulated genes of MEF-cultured cells in pathways related to DNA repair, hypoxia and MYC target (Fig. 4F). These pathways are crucial for enhancing CSCs’ genetic damage repair capabilities, adapting to hypoxic tumor microenvironment, and regulating cell proliferation. In conclusion, clonal cells cultured on MEFs appeared to better preserve the undifferentiated state and stemness of CSCs, whereas spheroids under suspension culture condition showed signs of partial differentiation which could impact the purity, quantity, and growth microenvironment of CSCs, eventually diminishing the conditions favorable for stemness maintenance.

Fig. 4

Comparison of LCSCs cultured under two different conditions. (A) Morphological comparison of LCSCs cultured on MEFs and in suspension condition respectively. Scale bar 100 μm (B) Comparison of the diameters of LCSCs cultured under two conditions. Values were represented as mean ± SD. *P < 0.05, ***P < 0.001. (C) Comparison of ALDH positive rates of LCSCs cultured under two conditions. (D) Heatmap showing the differences in stemness-related genes and differentiated gene (ALB) in LCSCs cultured under two conditions. The statistical method employed is the default analysis within the DESeq2 R package, and the adjusted P-values were used to indicate statistical significance. *P < 0.05, **P < 0.01, ***P < 0.001, ns indicates no statistical significance. (E) qPCR was performed to verify the expression levels of stemness genes and differentiated gene ALB in LCSCs cultured under two conditions. Values were represented as mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, and **** P < 0.0001. (F) GSEA pathway enrichment analysis of LCSCs cultured under two conditions. MEF denoted LCSCs that were cloned and cultivated on MEFs as feeder cells, while Spheroid referred to LCSCs that were cultured under suspension condition

Gene expression analysis of LCSCs

To investigate the differences between LCSCs and their differentiated counterparts, we conducted transcriptome sequencing on LCSCs and cells collected 10 days after the differentiation of LCSCs. Our objective was to identify the changes in gene expression patterns between LCSCs and differentiated cells. Volcano plot revealed significant transcriptional differences with the upregulation of 638 genes and downregulation of 523 genes in differentiated cells compared to those in LCSCs (Fig. 5A). To further refine our analysis, we focused on the expression patterns of stemness-related genes in these two cell populations. Heatmap analysis demonstrated significant upregulation of stemness genes such as ASCL2, SEMA3F, SOX2, LIF, ITGA6, EPCAM, and KLF4 in LCSCs cultured on MEFs (Fig. 5B), indicating a higher state of maintenance in stemness, while differentiated cells exhibited a notable decrease in the expression of stemness-related genes. Subsequently, we performed GO function enrichment and KEGG pathway enrichment analyses on genes highly expressed in LCSCs, and the results showed predominant enrichment in key biological processes such as cellular modified amino acid metabolic process, response to toxic substance, fatty acid derivative metabolic process, tissue homeostasis, and extracellular matrix organization (Fig. 5C). These findings underscored the link between specific gene expression patterns and biological functions in LCSCs. The KEGG analysis showed that highly expressed genes were mainly involved in metabolic pathways related to protein digestion and absorption, drug metabolism-cytochrome P450, drug metabolism-other enzymes, PPAR signaling, and ECM-receptor interactions (Fig. 5D). Furthermore, transcription factor enrichment analysis of highly expressed genes in LCSCs revealed potential regulations by transcription factors including AHR, ARNT, SREBF1, SP1, and KLF4 among others (Fig. 5E). Based on these results, we selected transcription factors AHR, SREBF1, ESR1, SP1, CDX2, and SP3, along with their target genes, to construct a regulatory network of highly expressed genes and transcription factors in LCSCs (Fig. 5F), which was helpful to elucidate the regulatory mechanisms that underpinned the maintenance of stemness in LCSCs.

Fig. 5

Transcriptome analysis of LCSCs and differentiated cells. (A) The volcano plot displayed the upregulated and downregulated genes in LCSCs on MEFs compared to those in differentiated cells (log2 Fold Change > |2|); (B) The heatmap illustrated the differences in stemness-related genes between LCSCs and differentiated cells. (C) GO functional enrichment results for different biological process in LCSCs; (D) KEGG pathway enrichment results for different pathways in LCSCs; (E) Enrichment results for highly expressed transcription factor genes in LCSCs. (F) The network diagram showed the regulatory relationships between transcription factors and target genes in LCSCs

Analysis of liver cancer classification based on highly expressed genes in LCSCs

To further investigate the impact of genes highly expressed in LCSCs in patients, we conducted a differential analysis of these genes between HCC tissues and adjacent non-cancerous tissues using TCGA-LIHC database. The analysis revealed that 67 genes were significantly upregulated in liver cancers (log2FC > 1), with a heatmap illustrating the top 30 most differentially expressed genes in LCSCs (Fig. 6A). Subsequently, we performed univariate Cox regression analysis on these 67 LCSC-related genes. The results suggested that the majority of highly expressed genes associated with LCSCs could serve as potential risk factors for the development of liver cancers. In contrast, P2RY8 and SLC16A11 genes with low hazard ratio indicated a protective effect against liver cancers. At present, the functions of these two liver cancer protective genes in solid tumors have not been widely reported. It has been shown that P2RY8 is a Gα13-coupled receptor that mediates migration inhibition and regulates the growth of B cells in lymphoid tissues, primarily associated with immune modulation [34]. SLC16A11 is a proton-coupled monocarboxylate transporter, and genetic disruption of SLC16A11 can induce changes in fatty acid and lipid metabolism, which are related to an increased risk of type 2 diabetes. Therefore, enhancing the function of SLC16A11 may have potential therapeutic effects on type 2 diabetes [35]. These research findings provide new research directions for future investigation on these two genes.

Fig. 6

Classification of liver cancer subtypes based on highly expressed genes in LCSCs within the TCGA-LIHC Database. (A) The heatmap displayed the top 30 differentially expressed LCSC genes in cancerous and para-cancerous tissues; (B) The line chart presented genes highly expressed in LCSCs that are associated with liver cancer prognosis; (C) The clustering results of liver cancer subtypes; (D) The boxplot verified the expression levels of LCSCs-related genes in two subtypes of liver cancers; (E) Survival analysis for patients with two liver cancer subtypes; (F) The heatmap showed the correlation between two liver cancer subtypes and clinical characteristics; (G) The expression levels of putative LCSC markers in two subtypes of liver cancer; (H) GO functional enrichment of genes highly expressed in C2 subtype of liver cancers; (I) The heatmap illustrated the relationship between two liver cancer subtypes and the tumor microenvironment

These results suggested that most genes highly expressed by LCSCs promoted the development of liver cancers (Fig. 6B). In order to identify cancer subtypes associated with LCSCs, considering the close relationship between LCSCs and malignant characteristics of cancer, such as the metastasis, drug resistance, the recurrence, and poor prognosis, we excluded genes related to protective factors and prioritized genes (KCNE3, ALDOA, PRTFDC1, SRXN1, LOX, ENTPD2, MFAP2, CREB3L1, RIBC2, CTHRC1, FAM133A, and MEP1A) associated with poor prognosis in LCSCs for consensus clustering analysis. The aim of this strategy was to enhance the precision of cancer classification for achieving more accurately personalized targeted drug treatments in the future.

To further deepen our understanding of these core genes, we used bar graphs to visually display their expression differences and levels between LCSCs and differentiated cells (Figure S4A). In addition, we conducted a correlation analysis on these core genes and found that most of them showed positive correlations with each other (Figure S4B). Moreover, we explored the correlation between these core genes and stemness-related genes in LCSCs and discovered that the majority of them were positively correlated with stemness-related genes, particularly MFAP2, CREB3L1, and CTHRC1 (Figure S4C). These findings not only highlighted the critical role of these genes in defining the characteristics of LCSCs but also provided valuable insights for future research directions and the selection of therapeutic targets.

Based on the results of the heatmap from the consensus clustering matrix, cumulative distribution function plot, Delta Area Plot, and Tracking Plot for k = 2 to 9, the K = 2 group analysis was ultimately selected (Figure S5A-D). Therefore, we classified liver cancer patients into two subtypes, with 219 patients in group C1 and 151 patients in group C2 (Fig. 6C). Consistently, HCC is also classified into two subtypes based on the genotyping of LCSC-related genes in ICGC database (Figure S6A-C). In this categorization, subtype C2 represented liver cancers with high expression of key genes related to LCSCs (Fig. 6D, Figure S6D). Further survival analysis revealed significant prognostic differences based on the patient grouping constructed using LCSC-related genes (Fig. 6E), with a better prognosis for subtype C1 and a poorer prognosis for subtype C2. We also used a heatmap to display the differential genes between these two subtypes of liver cancers and their correlation with clinical characteristics (Fig. 6F). The analysis found that subtype C2 of liver cancers was significantly associated with clinical stage, grade, and gender, with a higher prevalence of late-stage, high-grade, and female patients in subtype C2. Additionally, genes known to be related to stemness, such as SOX9, EPCAM, KRT9, LIF, CD44, DLK1, SOX4, KLF4, PROM1, CD24, POU5F1, and AFP, were more highly expressed in subtype C2(Fig. 6G, Figure S6E). These results further confirmed that subtype C2 of liver cancers possessed more stemness and malignant characteristics. Furthermore, we have observed that the expression levels of these key genes are higher in liver cancer tissues compared to the adjacent non-cancerous tissues within the ICGC database (Figure S6G).

To identify biological processes enriched in subtype C2, GO enrichment analysis was conducted on genes that were highly expressed in patients with this subtype. The results showed significant enrichment in processes such as extracellular matrix organization and immune regulation (Fig. 6H, Figure S6F). To corroborate these findings, tumor microenvironment-related analyses were performed, and the results indicated that subtype C2 of liver cancers exhibited elevated stromal and immune scores, strongly correlating with the infiltration levels of various immune cells, including fibroblasts, natural killer (NK) cells, CD8+ T cells, B cells, monocytes, and myeloid-derived suppressor cells (MDSCs). Notably, neutrophil infiltration levels did not show this correlation (Fig. 6I). Thus, these results highlighted the close association between subtype C2 of liver cancers and the tumor microenvironments.

To investigate whether there is a difference in immunotherapy response between the two subtypes of hepatocellular carcinoma, we applied two predictive models, TCIA and TIDE, to evaluate the potential effect on the immunotherapy. However, the results showed that there was no significant difference in immunotherapy response between patients with hepatocellular carcinoma of C1 and C2 subtypes (Figure S7A, B). Notably, the expression levels of CTLA4 and CD274 (PD-L1) genes were significantly higher in C2 subtype compared to C1 subtype (Figure S7C). Theoretically, C2 subtype might respond better to the immunotherapy, but this was not the case in practice. More interestingly, in TIDE score analysis, we observed a higher expression level of MDSCs in C2 subtype and a positive correlation with the key marker genes of MDSCs, ITGAM and CD33 (Figure S7D, E). MDSCs are capable of suppressing the immune microenvironment [36], which may explain the reason, despite the higher expression of immune checkpoint genes in C2 subtype, it was not responsive to the immunotherapy. These findings revealed the complexity of the tumor immune microenvironment and indicated that co-targeting of genes associated with LCSCs might be necessary to improve the efficacy of the immunotherapy.

Stratification of key genes in LCSCs through single-cell transcriptomic analysis

To investigate the association between the expression of high-risk LCSC-related genes (KCNE3, ALDOA, PRTFDC1, SRXN1, LOX, ENTPD2, MFAP2, CREB3L1, RIBC2, CTHRC1, FAM133A, and MEP1A) and the extracellular matrix (ECM) composition, we acquired and analyzed single-cell sequencing data of HCC from GEO database. Initially, the dataset underwent rigorous preprocessing followed by dimensionality reduction and clustering via Uniform Manifold Approximation and Projection (UMAP). This analysis delineated the cellular heterogeneity within samples by categorizing cells into six populations, designated as tumor cells, T cells, B cells, macrophages (macro), cancer-associated fibroblasts (CAF), and endothelial cells (EC), using established lineage-specific markers (Fig. 7A, B). After segregating the tumor cells, we applied the Ucell algorithm to compute the distribution of stemness-related gene scores within the dimensionality-reduced space. The results of the analysis revealed varying stemness profiles, with samples HCC03T and HCC0T showing lower scores, while HCC08T, HCC09T, and HCC10T exhibited higher scores (Fig. 7C, D). Pseudotime trajectory analysis using Monocle 2 package indicated a differentiation trend where cells with elevated stemness scores progressed towards a state with reduced stemness (Fig. 7E-H). The epithelial-mesenchymal transition (EMT) scores calculated for these tumor cells correlated with stemness, with higher EMT indices observed in patients with higher stemness scores (Fig. 7I). By analyzing the expression of classic LCSC-related genes, we found that the expression levels of these genes (PROM1, CD24, CD44, EPCAM, POU5F1, KLF4, LIF, AFP, DLK1, KER19 and SOX9) were correspondingly elevated in patients with high stemness scores (Fig. 7J). Finally, the analysis within fibroblast subset unveiled an upsurge in the expression of collagen-associated genes (COL1A1, COL1A2, and COL3A1) in patients displaying high stemness scores, suggesting a potential link to enhanced collagen deposition within ECM (Fig. 7K, L). In summary, these results further confirmed that patients expressing high-risk LCSC-related genes had stronger EMT indices and stemness characteristics and might be closely related to collagen deposition in ECM.

Fig. 7

Analysis of prognosis-associated LCSC gene sets in single-cell sequencing data. (A) Umap-based clustering delineated primary liver cancer cell types; (B) The bubble chart represented the expressions of marker genes characteristic of distinct cellular subpopulations; (C) Extraction of tumor cell subsets from the dataset; (D) Ucell algorithm analysis of the scoring for prognosis-associated LCSCs gene sets; (E) Extraction of patient subsets for HCC03, HCC05T, HCC08T, HCC09T, and HCC10T; (F, G, & H) Illustrations of pseudotime analysis for tumor cells across five patients within the dataset; (I) Ucell algorithm analysis of EMT gene sets scoring in tumor cells from 5 patients; (J) The bubble chart displayed the expressions of LCSC markers in these five patients; (K) Extraction of CAF subsets from these five patients; (L) Analysis of the expressions of fibroblast and collagen-related genes among five patients

Predicting drug sensitivity using LCSC-associated gene scores

In an effort to understand the influence of LCSC-related genes on drug sensitivity of LIHC, we carried out additional drug sensitivity predictions. The results were presented in the form of box plots, revealing a significant statistical difference in drug sensitivities between patients with subtype C1 or C2 of liver cancers. Notably, subtype C1 patients showed heightened sensitivity to 5-FU, ABT-737, Gefitinib, ERK inhibitors, Foretinib, and Erlotinib (Fig. 8A-F). In contrast, subtype C2 patients exhibited increased sensitivity to Cisplatin, Axitinib, JAK1 inhibitors, WNT-c59, Sorafenib, and RO-3306 (Fig. 8G-L). These results indicated that stratifying HCC patients based on LCSC-related genes could facilitate the personalized treatment regimens according to their predicted drug sensitivities.

Fig. 8

Drug sensitivity analysis of LCSC-related genes in LIHC Patients. (A-F) Box plots depicted drug sensitivity predictions for patients with subtype C1 of liver cancers, highlighting increased sensitivity to 5-FU, ABT-737, Gefitinib, ERK inhibitors, Foretinib, and Erlotinib. (G-L) Box plots illustrated drug sensitivity predictions for patients with subtype C2 of liver cancers, indicating heightened sensitivity to Cisplatin, Axitinib, JAK1 inhibitors, WNT-c59, Sorafenib, and RO-3306