Ginsenoside Rh2 in combination with IFNγ potentiated the anti-cancer effect by enhancing interferon signaling response in colorectal cancer cells

Abstract

Interferon gamma (IFNγ) can amplify immune cell-mediated anti-tumor immunity, as well as directly kill cancer cells. Ginsenoside Rh2 (Rh2), a bioactive compound in traditional Chinese medicine, exhibits anti-cancer effects such as inhibiting proliferation and metastasis. Our earlier research found that Rh2 combined with IFNγ enhanced CXCL10 secretion in cancer cells. Here, we explored whether Rh2 and IFNγ exerted more potent anti-cancer activity in vitro and in vivo, along with its mechanisms and clinical value. Our data showed that Rh2 in combination with IFNγ resulted in a remarkably increased cytotoxicity in colorectal cancer cells including HT29, LoVo and T84 cell lines. Consistently, intratumoral injection with Rh2 plus IFNγ further restricted the HT29 tumor growth in vivo, and importantly, it was demonstrated to be safe for mice. Meanwhile, the combo treatment activated the stimulator of interferon genes (STING) pathway in cancer cells, promoting the transcription of downstream type I interferon. RNA sequencing revealed a dramatically transcriptional alteration in cancer cells with combo treatment and indicated that Rh2 further augmented the activation of interferon signaling pathway, compared with the IFNγ alone. Inhibition of janus kinase (JAK) by ruxolitinib could significantly rescue the cell death-triggered by the combo treatment. Then, a gene set named Rh2+IFNγ signature genes (RISG) was defined, which contained top 20 significantly upregulated genes from the combo treatment. Patients who exhibited a favorable response to the immunotherapy had a higher expression of RISG in tumor compared with those who did not respond. And the high expression of RISG was correlated with better clinical outcome in patients with colorectal cancer (CRC) and skin cutaneous melanoma (SKCM). Herein, the combination of Rh2 with IFNγ served as a promising strategy for cancer treatment, and its-derived RISG gene set also exhibited potential value in predicting clinical outcome.

Schematic diagram of the anti-cancer effect of Rh2 combined with IFNγ. The schematic diagram illustrated that ginsenoside Rh2 in combination with IFNγ robustly activated the interferon signals in cancer cells, ultimately leading a significant cell death of cancer cells. ISGs, interferon-stimulated genes. Created with BioRender.com.

Introduction

Interferon gamma (IFNγ) is primarily generated by activated T cells and natural killer cells, exerting diverse and significant effects on both cancer cells and immune cells [1, 2]. The production of IFNγ in the tumor microenvironment can amplify immune cell-mediated anti-tumor immunity, as well as directly kill cancer cells via inducing anti-proliferative or pro-apoptotic effects [3]. Immune checkpoint inhibitors like anti-programmed cell death-1 (PD-1) / programmed death-ligand 1 (PD-L1) have brought revolutionary changes to the field of cancer treatment and provided a brand-new hope for curing cancer [4]. However, a large portion of the patients couldn’t benefit from these immunotherapy, due to the de-novo and acquired resistance [5]. One of the primary reasons for encountering resistance to the cancer immunotherapy is the low activity or impaired IFNγ signaling within the tumor microenvironment, particularly among tumor cells [6]. A clinical study performed a whole-exome sequencing and revealed that the loss-of-function mutations of interferon signaling-Janus kinase (JAK) 1 or JAK2 would be highly correlated with the emergence of acquired resistance to the anti-PD-1 immunotherapy [7]. Consistently, researchers found that depleting the components of IFNγ pathway, like IFNγ receptor, JAK1, JAK2, or signal transducer and activator of transcription 1 (STAT1), would cause the resistance to the immunotherapy in mice tumor models [8].

Clinically, IFNγ has been reported for use in treating adult T cell leukemia [9]. Another clinical study showed that ovarian cancer patients receiving chemotherapy combined with IFNγ treatment had better progression-free survival [10]. Increasing evidence underscored the pivotal role of the IFNγ signaling pathway in orchestrating an effective anti-tumor immune response during immunotherapy. A pre-clinical study has illuminated an intriguing finding that melanoma patients who failed to respond to tumor immunotherapy exhibited elevated levels of IFNγ and tumor necrosis factor-α (TNFα) in their bloodstreams [11]. Sensitizing cancer cells to these cytokines could significantly trigger cytotoxicity and enhance the tumor’s response to the immunotherapy [11]. Another study reported that inhibiting Ptpn2 drastically augmented the cytotoxicity of cancer cells upon the IFNγ and TNFα stimulation, thereby triggering more cancer cell death [8, 12], providing a new strategy for cancer treatment. In a nutshell, enhancing the responsive sensitivity of cancer cells to cytokine stimulation, such as IFNγ, could be a promising anti-cancer strategy.

Ginsenoside Rh2 (Rh2) is a famous bioactive constituent isolated from Panax ginseng C. A. Meyer, a tonic traditional Chinese medicine, which is well-known for its diverse pharmacological properties [13–15]. It is garnering heightened interest within the area of cancer research, since pharmacological studies have already unveiled its capacity to exert anti-proliferation, anti-metastasis, as well as to alleviate the adverse effects [14, 16]. Actually, Rh2 shares an extremely similar chemical structure with ginsenoside Rg3 [17], which has already been utilized in clinical application for cancer treatment [18]. Our previous study has demonstrated that Rh2 in combination with IFNγ increased the secretion of CXCL10 by cancer cells via activation of TANK binding kinase 1 (TBK1)-interferon regulatory factor 3 (IRF3) signaling pathway, suggesting a potential mechanism of Rh2 enhancing the anti-cancer effect of PD-L1 antibody [19]. Following this, an intriguing phenomenon was noticed that Rh2 plus IFNγ could induce more severe cytotoxicity in cancer cells than Rh2 or IFNγ alone, which had not been reported before. This finding suggested that Rh2 might enhance the sensitivity of cancer cells to IFNγ, thereby indicating that their combined use holds significant potential as an effective anti-cancer strategy. Therefore, in this study, we focused on studying whether Rh2+IFNγ-induced cell death has anti-tumor effect in vivo, as well as its potential mechanism and clinical value.

Materials and methods

Reagents and kits

For cell culture experiments, DMEM, F-12 Nutrient Mixture, DMEM/F-12, McCoy’s 5 A (Modified) medium, RPMI-1640 medium, phosphate-buffered saline (PBS), fetal bovine serum (FBS), penicillin-streptomycin (P/S), and trypsin were all procured from the Gibco (CA, USA). In this section, 20(S)-Rh2, which has the purity of more than 98%, utilized in animal and cellular experiments was sourced from the company of Sichuan Weikeqi Biological Technology (Chengdu, China). Solvent dimethyl sulfoxide (DMSO) and cosolvent TWEEN 80 were acquired from the company of Sigma-Aldrich. 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) and bovine serum albumin (BSA) with the purity >98%, as well as RNAeasy™ Animal RNA Isolation Kit with Spin Column were ordered from Beyotime (Shanghai, China). For protein concentration quantitation, Pierce™ BCA Protein Assay Kits were ordered from Thermo Fisher Scientific (MA, USA). The electron microscopy fixation solution with an active ingredient of 2.5% glutaraldehyde was ordered from Servicebio (Wuhan, China). For qRT-PCR detection, Bio-Rad’s SYBR^® Green was ordered from Bio-Rad Laboratories (CA, USA). For total mRNA extraction, TRIzol™ reagent was procured from the Life Technologies company (Shanghai, China).

Cell culture

Human HT29 colon cancer cells, presented by Prof Yong-hua Zhao at University of Macau, were propagated in DMEM. Human NSCLC cell line NCI-H460 (maintained in the RPMI-1640 medium), human colon carcinoma LoVo (F-12), T84 (DMEM/F-12, 20% FBS), and HCT116 (5 A) were kindly provided by the Cell Bank, Chinese Academy of Sciences (Shanghai, China). Human NSCLC cell lines A549, NCI-H460, NCI-H1975, human ovarian cancer cell line HEY, and human colon carcinoma RKO cell line were sourced from ATCC (MD, USA), which were propagated in RPMI-1640 under similar conditions. Human prostate cancer cell line 22RV1 was provided by Beyotime, which was cultured in RPMI-1640 medium. Human prostate cancer cell lines DU145 (DMEM) and PC3 (F-12) were kindly provided by Cell Bank, Chinese Academy of Sciences. Human pancreatic cancer cell line PANC1 and MIA-PaCa-2 were abtained from ATCC, which were cultured in the DMEM. Human ovrian cancer cell line SKOV3 was a gift from Prof Hong Zhu (Zhejiang University, Hangzhou, China), cultured in RPMI-1640 medium. All prostate cancer cells are routinely cultured using complement-free media. Without any specification, all mediums were added with 10% FBS and 1% P/S. All cell lines were incubated at 37 °C with 5% CO₂.

Mice

Age-matched BALB/c nude mice, approximately 6–8 weeks old, were sourced from the Macau University Animal Center and housed in a specific-pathogen-free (SPF) feeding environment. All animal procedures were conducted ethically and standardly, adhering to the University of Macau’s Animal Ethics Committee Guidelines and the Care and Use of Laboratory Animals Principles (Protocol IDs: UMARE-008-2023).

Establishment of tumor model and treatment

The right flank of BALB/c nude mice was inoculated with 3 × 10⁶ HT29 cells via subcutaneous injection. When the tumor formation, mice were randomly assigned to treatment groups including control, Rh2, IFNγ and Rh2+IFNγ. 5 mg/kg of Rh2, dissolved in PBS containing 1.3% DMSO and 1.3% TWEEN 80, was administered intratumorally to the mice. The dosage of IFNγ was administered as follows: 0.5 μg/mouse from day 8 to 16, 1 μg/mouse on day 18, and 3 μg/mouse from day 20 to 28. The administration frequency of both Rh2 and IFNγ was every 3 days from day 8 to 14 and then every 2 days from day 14 to 28. The mice in the control group received intratumoral injections of the same solvent as the experimental group, under the same administration frequency. Tumor dimensions and body weight were recorded bi-daily. The calculation formula for tumor volume was length×width²/2.

Cytotoxicity assay (MTT)

Cancer cells were plated into 96-well plates and adhered overnight. Subsequently, they were treated with Rh2 (10 μM), IFNγ (10 ng/mL), or a combination of both for a duration of 24 h. Next, the MTT solution with a stock concentration of 2.5 mg/mL was diluted in a ratio of 1:5 with the culture medium and used to incubate the Rh2+IFNγ-treated cancer cells for another 3–4 h. Afterward, the MTT-containing medium was aspirated clearly, and 100 μL DMSO was added to every well. Following thorough mixing, the absorbance of each well was detected at both 490 and 570 nm wavelengths using a SpectraMax M5 microplate reader (Molecular Devices, CA, USA).

Cancer cell morphology observation

HT29 and LoVo colon cancer cells were plated into the IncuCyte^® ImageLock 96-well plates and adhered overnight. Then, cancer cells were incubated with Rh2 (10 μM), IFNγ (10 ng/mL), or both for 24 h. Cell morphology was observed and captured by an Essen BioScience IncuCyte S3 Live-Cell Analysis System (Sartorius Company, Goettingen, Germany).

Transmission electron microscope scanning

Cancer cells were incubated with Rh2 in combination with IFNγ for a duration of 24 h. For cells suspended in the supernatant of the culture medium, collect them by centrifugation at 2000 rpm; for adherent cells, gently scrape them off using a cell scraper and collect them by centrifugation. Resuspend all cells in electron microscope fixative and immediately centrifuge them at 10,000 rpm for 10 min. Discard the supernatant, slowly add fresh electron microscope fixation buffer. The final transmission electron microscope scanning was performed by Servicebio Company (Wuhan, China).

RNA-seq

HT29 cells were incubated with Rh2, IFNγ, and both for 24 h and then three repetitions of RNA sample were extracted by using TRIzol reagent. The RNA sequencing (RNA-seq) was performed by Novogene Co., Ltd. (Tianjin, China) by using a Novaseq-PE150 Sequencing System.

qRT-PCR

Quantitative real-time PCR (qRT-PCR) was conducted according to the established protocol [19]. Cancer cell RNA was isolated utilizing a RNAeasy™ Animal RNA Isolation Kit (Beyotime, Shanghai, China). Subsequently, cDNA synthesis was accomplished with a RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, MA, USA). qRT-PCR was then performed in a QuantStudio™ 7 Flex Real-Time PCR System (Thermo Fisher Scientific, MA, USA), utilizing primer sequences detailed in the Table 1.

Table 1.

Sequences of primers.

Gene	Primer	Sequence
IFNA1 (Homo)	Forward	5ʹ-GCCTCGCCCTTTGCTTTACT-3ʹ
	Reverse	5ʹ-CTGTGGGTCTCAGGGAGATCA-3ʹ
IFNB1 (Homo)	Forward	5ʹ-ATGACCAACAAGTGTCTCCTCC-3ʹ
	Reverse	5ʹ-GGAATCCAAGCAAGTTGTAGCTC-3ʹ
HLA-A (Homo)	Forward	5ʹ-AAAAGGAGGGAGTTACACTCAGG-3ʹ
	Reverse	5ʹ-GCTGTGAGGGACACATCAGAG-3ʹ
HLA-C (Homo)	Forward	5ʹ-CACACCTCTCCTTTGTGACTTCAA-3ʹ
	Reverse	5ʹ-CCACCTCCTCACATTATGCTAACA-3ʹ
XAF1 (Homo)	Forward	5ʹ-GCTCCACGAGTCCTACTGTG-3ʹ
	Reverse	5ʹ-GTTCACTGCGACAGACATCTC-3ʹ
MX1 (Homo)	Forward	5ʹ-GTTTCCGAAGTGGACATCGCA-3ʹ
	Reverse	5ʹ-CTGCACAGGTTGTTCTCAGC-3ʹ
ISG20 (Homo)	Forward	5ʹ-CTCGTTGCAGCCTCGTGAA-3ʹ
	Reverse	5ʹ-CGGGTTCTGTAATCGGTGATCTC-3ʹ
GAPDH (Homo)	Forward	5ʹ-GCGACACCCACTCCTCCACCTTT-3ʹ
	Reverse	5ʹ-TGCTGTAGCCAAATTCGTTGTCATA-3ʹ

GSEA

Analysis of the raw data was performed on the NovaMagic online system (https://magic.novogene.com/). After that, the RNA-seq data were input into a professional software of Gene Set Enrichment Analysis (GSEA) and conduced the analysis [20, 21]. The analysis of the phenotypic enrichment in control, Rh2, IFNγ, and Rh2+IFNγ was conducted according to the gene sets retrieved from the Molecular Signatures Database (MSigDB) [22, 23].

Heatmap profiling

Firstly, the z-score of gene expression was calculated using the formula z = (x - μ) / σ, where x represents the expression value of genes, μ represents the mean of the data set, σ stands for the standard deviation, and Z is the standard score. Next, the heatmap was drew by using the GraphPad Prism 10 (GraphPad Software Inc., San Diego, USA).

Venn diagram and EnrichR analysis

A cutoff threshold was conducted on the gene expression profiling of various comparisons including “Rh2 versus Control”, “IFNγ versus Control”, “Rh2+IFNγ versus IFNγ”, and “Rh2+IFNγ versus Control” according to the standard of P value < 0.05, |Fold change (FC) | > 2. Then, Venn diagrams for “Rh2 versus Control (Up)”+“Rh2+IFNγ versus IFNγ (Up)”+“Rh2+IFNγ versus Control (Up)”, “Rh2 versus Control (Down)”+“Rh2+IFNγ versus IFNγ (Down)”+“Rh2+IFNγ versus Control (Down)”, and “Rh2 versus Control (Up)”+“IFNγ versus Control (Up)”+“Rh2+IFNγ versus Control (Up)” was drew by using the website of Bioinformatics & Evolutionary Genomics (https://bioinformatics.psb.ugent.be/webtools/Venn/). The co-upregulated, co-downregulated, or the specifically expression genes were then put into the EnrichR for the enrichment analysis (https://maayanlab.cloud/Enrichr/) [24–26].

Metascape analysis

The selected gene list was uploaded into the Metascape (https://metascape.org/gp/index.html#/main/step1) for the analysis of protein and protein interaction (PPI) and enrichment [27]. Finally, the network layout generated through this process provided a comprehensive and intuitive visualization of the enriched functional terms.

Expression correlation and patient clinical outcome analysis

The top 20 genes were finally selected as the RISG gene set based on their better predicted ability. Here are the criteria used for this selection: (1) Initially, the differential genes expression in Rh2+IFNγ vs IFNγ were sorted with the criteria of fold change (FC) more than two (FC > 2) and showing significant difference (P < 0.05) in a descending order. (2) It was ideal if the gene set could predict clinically relevant expression, patient prognosis, and responsiveness to cancer immunotherapy. (3) While possessing the aforementioned predictive value, it was preferable to have as few genes as possible in the gene set. Signature genes list was input into the Biomarker Exploration of Solid Tumors (BEST) platform (https://rookieutopia.hiplot.com.cn/app_direct/BEST/) to investigate the correlation between the expression of these genes and disease progression/classification, their relationship with clinical patient outcome benefit, their association with immune cell infiltration in tumor, and their predictive value for the responsiveness to immunotherapy.

Statistical analysis

Statistical analyses were conducted utilizing GraphPad Prism 10 (GraphPad Software Inc., CA, USA). Comparisons encompassing more than two groups were analyzed through one-way ANOVA. Additionally, a two-way ANOVA with a multiple-comparisons test was applied for analyzing tumor growth curve.

Results

Rh2 in combination with IFNγ inhibited the cancer cell viability and tumor growth

Firstly, the cytotoxicity of Rh2 plus IFNγ was evaluated among different cancer cell lines including colon cancer cells (HT29, LoVo, T84, RKO, and HCT116), prostate cancer cells (22RV1, DU145, and PC3), pancreatic cancer cells (PANC1 and MIA-PaCa-2), ovarian cancer cells (SKOV3 and HEY), as well as the lung cancer cells (A549, NCI-H1975, and NCI-H460) (Fig. 1a). The MTT assay revealed that Rh2 treatment at a concentration of 10 μM exhibited a certain degree of cytotoxicity across various cell lines, whereas IFNγ with a 10 ng/mL concentration had insignificant suppressive effects on these same cell lines (Fig. 1a). Of particular interest, an enhancement of cytotoxicity with the combination of Rh2 and IFNγ was observed in the HT29, LoVo, and T84 cell lines exclusively. After 24 h exposure to Rh2 combined with IFNγ, the cell viability of HT29 cells was only 32.2% on average, compared to 68.9% for Rh2 alone and 94.0% for IFNγ. The survival rate of LoVo cells was only 44.8% on average, compared to 64.5% for Rh2 alone and 105.0% for IFNγ. Similarly, the survival rate of T84 cells after being treated with Rh2 plus IFNγ was 50.3% on average, compared to 75.9% in Rh2 group and 91.4% in IFNγ’s. Additionally, the cellular morphological images showed that Rh2 in combination with IFNγ induced significant cell death in HT29 and LoVo cells (Fig. 1b). These data indicated that Rh2 in combination with IFNγ triggered more cytotoxicity in HT29, LoVo, and T84 cells. Following these findings, we performed a cytotoxicity assay using different concentrations of Rh2 in combination with varying concentrations of IFNγ in HT29 cancer cells (data not shown). 10 μM of Rh2 balanced its cytotoxicity and the potential of enhancement with IFNγ for killing cancer cells. For IFNγ, 10 ng/mL was a commonly used in previous studies [28, 29], and fell within the range tested in our cytotoxicity assay. Besides, we compared the cytotoxicity of different ginsenosides Re, Rb1, and Rh2 in combination with IFNγ on HT29 cancer cells. Unlike Rh2, the combination of Re or Rb1 with IFNγ did not further enhance the cytotoxicity (data not shown).

Fig. 1. Rh2 in combination with IFNγ inhibited the cancer cell viability and tumor growth.

a HT29, LoVo, T84, RKO, HCT116, 22RV1, DU145, PC3, PANC1, MIA-PaCa-2, SKOV3, HEY, NCI-H1975, NCI-H460 and A549 cancer cells were treated with Rh2 (10 μM), IFNγ (10 ng/mL), or both combination for 24 h. Then the cytotoxicity was assayed by MTT detection. “#” represented the comparison between Rh2 and Rh2+IFNγ. “*” represented the comparison between IFNγ and Rh2+IFNγ. b The cellular morphology of HT29 and LoVo cancer cells after being treated with Rh2 (10 μM), IFNγ (10 ng/mL), or both for 24 h. Scale bar: 100 μm. c Schematic showing that BALB/c nude mice with heterogenic HT29 tumor were randomly grouped into control, Rh2, IFNγ and Rh2+IFNγ. Mice were given intratumoral injection of Rh2 and IFNγ. d Tumor growth curves of individual mice in different treatment group including control (n = 7), Rh2 (n = 7), IFNγ (n = 7) and Rh2+IFNγ (n = 7). e Tumor volume (mean ± SEM). f Images of HT29 tumors from control, Rh2, IFNγ and Rh2+IFNγ group. g Tumor weight was shown by the mean ± SEM. h Body weight was presented by the mean ± SEM. ^##P < 0.01, ^###P < 0.001, *P < 0.05, **P < 0.01, ***P < 0.001.

Next, the anti-tumor effect of Rh2 in combination with IFNγ was evaluated in BALB/c nude mice bearing HT29 tumor. Once the tumor volume reached approximately 100 mm³, mice were randomly assigned to distinct treatment groups including control, Rh2, IFNγ and Rh2+IFNγ (Fig. 1c). As shown by individual tumor growth curve, intratumoral injection of Rh2 slightly delayed the HT29 tumor growth; however, the combination of Rh2 with IFNγ obviously inhibited the tumor growth when compared with control or single treatment (Fig. 1d). Eventually, the combination treatment decreased an approximately 50% tumor volume compared with the control group, a 35% reduction compared with Rh2 group, as well as about a 38% decrease related to the IFNγ group (Fig. 1e). Similarly, macroscopic images revealed that the combined therapy led to a more pronounced reduction in tumor size than either the control or the monotherapy (Fig. 1f). Figure 1g showed that Rh2 in combination with IFNγ remarkably reduced the HT29 tumor weight compared with control. These data indicated that Rh2 in combination with IFNγ exhibited anti-tumor effect in vivo. During the animal experiment, the body weight of mice was recorded every two days and the result showed that neither monotreatment nor the combination of Rh2 and IFNγ had any effect on the body weight of the mice (Fig. 1h). Meanwhile, the daily behaviors of the mice were carefully observed, and no abnormalities were noted after the treatment. These data indicated that the combination treatment was safe for mice.

Rh2 plus IFNγ induced transcriptional alteration and apoptotic phenotype in HT29 cancer cells

To figure out why Rh2 combined with IFNγ showed favorable anti-cancer effect in vitro and in vivo, an RNA-seq was performed on HT29 cells treated with Rh2, IFNγ, and Rh2+IFNγ. The heatmap in Fig. 2a showed that the combination of Rh2 with IFNγ significantly altered the transcriptome of HT29 cancer cells. Additionally, the GSEA analysis revealed a high enrichment of hallmarks such as “Interferon gamma response”, “Interferon alpha response”, “TNFA signaling via NF-κB” and “Apoptosis” in the Rh2+IFNγ group, while hallmarks associated with tumor progression, including “MYC targets” and “E2F targets”, exhibited reduced enrichment (Fig. 2b). Furthermore, based on the cellular phenotypic observations, the enrichment of apoptosis signals was consistent with the cancer cell death phenotype-induced by the combo treatment (Fig. 2b).

Fig. 2. Rh2 plus IFNγ induced transcriptional alteration and apoptosis in HT29 cancer cells.

a Heatmap showing the overview of transcriptional alteration in HT29 cancer cells treated with Rh2 (10 μM), IFNγ (10 ng/mL), and Rh2 (10 μM) + IFNγ (10 ng/mL) for 24 h. n = 3. Gene expression was exhibited by z-score, with red to blue representing high to low expression levels. b The enrichment hallmark gene signatures in control (normalized enrichment score (NES) < 0, false discovery rate (FDR) q-value < 0.25) or Rh2+IFNγ (NES > 0, FDR q-value < 0.25) were identified by the GSEA ranked analysis based on the differentially expressed genes. NES was represented by the size of circle. Bigger size indicated higher score. FDR q-value was represented by the color gradient from red to blue. Values that were lower (indicated by redder hues) carried greater significance. c Caspase 3, cleaved Caspase 3, PARP, cleaved PARP and GAPDH were detected by Western blot in HT29 cancer cells treated with Rh2 (10 μM) and IFNγ (10 ng/mL) for 24 h. d Transmission electron microscope showing the detailed subcellular structure including apoptotic body of HT29 cancer cells treated with Rh2+IFNγ. Scale bars were indicated in the figures according to different magnification times. N for nuclear, C for cytoplasm, AB for apoptotic body. e–g GSEA showing upregulation of “Hallmark_apoptosis” gene signature in HT29 cancer cells treated with Rh2+IFNγ, compared with control, Rh2, or IFNγ, respectively.

Rh2 plus IFNγ increased the levels of cleaved caspase-3 and cleaved PARP in HT29 cells (Fig. 2c), indicating the currency of apoptosis. Furthermore, transmission electron microscopy images displayed significant alterations in nuclear morphology, including the appearance of apoptotic bodies, following the treatment with Rh2 combined with IFNγ (Fig. 2d). All of these suggested the occurrence of apoptosis. But, utilizing a caspase-3 inhibitor Z-DEVD-FM and a pan-caspase inhibitor Z-VAD-FMK did not reverse Rh2+IFNγ-induced cell death (data not shown). We hypothesized that the apoptosis induced by the combo treatment might be caspase-independent or that other modes of cell death were also involved. Thus, inhibiting the function of classical pro-apoptotic proteins such as caspases-3 could not prevent the cell death caused by hyperactivation of interferon signaling. Then, comparative analysis was conducted on the differentially expressed genes obtained from the RNA-seq, focusing on Rh2+IFNγ versus control, Rh2+IFNγ versus Rh2, and Rh2+IFNγ versus IFNγ. GSEA revealed that the gene signature of “Hallmark_apoptosis” was highly enriched in the Rh2+IFNγ group compared to the control, Rh2 and IFNγ groups (Fig. 2e–g), suggesting that the combo treatment potentiated the triggering of apoptotic signals. Additionally, various gene sets related to apoptotic signaling from the MSigDB was imported for GSEA analysis. Gene sets representing “Intrinsic apoptotic signaling pathway in response to ER-stress”, “Extrinsic apoptotic signaling pathway”, “Positive regulation of release of cytochrome c from mitochondria”, “Extrinsic apoptotic signaling pathway via death domain receptors”, and “Intrinsic apoptotic signaling pathway” were consistently enriched in the combo treatment across all three comparisons (Supplementary Fig. S1a–c), further indicating that combo treatment enhanced the apoptotic signaling in cancer cells.

Rh2 in combination with IFNγ promoted the type I interferon expression and STING activation

By intersecting the significantly upregulated genes in “Rh2 versus Control”, “Rh2+IFNγ versus IFNγ”, and “Rh2+IFNγ versus Control”, 231 commonly upregulated genes were identified in total (Fig. 3a). These genes were then imported into the EnrichR for analysis. According to the Reactome_2022 gene sets, 231 commonly upregulated genes were significantly associated with the signaling pathways such as “Interferon alpha/beta signaling”, “Interferon signaling”, “Cytokine signaling in immune system”, “Interferon gamma signaling”, and “Antiviral mechanism by IFN-stimulated genes” (Fig. 3b), which was consistent with previous analysis in Fig. 2b. Given that stimulator of interferon genes (STING) is an essential molecule mediating the innate immune response of type I interferon [30, 31], we speculated whether Rh2 combined with IFNγ activates the STING signal within tumor cells. The expression of interferon alpha 1 (IFNA1/IFNα) and interferon beta 1 (IFNB1/IFNβ), the production of STING signaling pathway, was firstly validated and the results showed that Rh2 in combination with IFNγ, rather than individual treatment of Rh2 or IFNγ, significantly promoted the mRNA transcription of IFNA1 and IFNB1 in cancer cells (Fig. 3c), which was consistent with the GSEA and EnrichR results. Intriguingly, compared to Rh2 or IFNγ alone, Rh2 combined with IFNγ significantly increased the phosphorylation of STING at the ser366 site (Fig. 3d), indicating the activation of STING by this combo treatment. Our previous study has reported that Rh2 in combination with IFNγ markedly increased the phosphorylation of TBK1 and IRF3 [19], which served as classical downstream of STING. Therefore, the current study well supported our previous findings and emphasized the activation of the STING-TBK1-IRF3 pathway due to the combination treatment. A GSEA analysis was performed on the RNA-seq data and results in Fig. 3e, f demonstrated that the signature gene sets of both “STING signaling pathway” and “Cytosolic DNA sensing pathway” were significantly enriched in the Rh2+IFNγ by the comparison with control group.

Fig. 3. Rh2 in combination with IFNγ promoted the type I interferon expression and STING activation.

a Venn diagram showing the intersection of significantly differentially co-upregulated genes identified by RNA-seq among “Rh2 versus Control (Up)”, “Rh2+IFNγ versus IFNγ (Up)”, and “Rh2+IFNγ versus Control (Up)”. Cut-off for the upregulation genes: P value < 0.05, FC > 2. b EnrichR analysis showing the Top 15 enrichment signaling pathways/gene sets from “Reactome_2022” based on the remarkably 231 co-upregulated genes among “Rh2 versus Control (Up)”, “Rh2+IFNγ versus IFNγ (Up)”, and “Rh2+IFNγ versus Control (Up)”. Odds ratio was represented by the size of circle. Bigger size indicated higher score. P value was represented by the color gradient. Values that were lower (indicated by redder hues) carried greater significance. c The transcriptional alteration of IFNA1 and IFNB1 in HT29 cancer cells when treated with Rh2 (10 μM) + IFNγ (10 ng/mL) for 24 h. d Western blot showing the protein expression level of p-STING (Ser366), STING, cGAS and HSP90β. e, f GSEA showing the increased enrichment of “STING signaling pathway” and “Cytosolic DNA sensing pathway” gene signatures in Rh2+IFNγ group, compared with control.

Rh2 in combination with IFNγ intensified the activation of downstream signaling pathway of IFNγ response

To further explore the potential mechanisms underlying the combinational effects of Rh2 plus IFNγ, a deeper analysis was conducted on the RNA-seq data. The commonly upregulated genes in “Rh2 versus Control”, “IFNγ versus Control”, and “Rh2+IFNγ versus Control” were intersected, focusing on genes specifically upregulated in the Rh2+IFNγ group and this analysis yielded 559 genes (Fig. 4a). These 559 genes were then subjected to GONetwork analysis on the metascape platform, revealing their association with the signaling networks such as “Cytokine signaling in immune system”, “Positive regulation of response to external stimulus”, “Innate immune response”, and “Antigen processing and presentation of peptide antigen” (Fig. 4b). This suggested that Rh2 might increase cancer cells’ sensitivity to IFNγ. The GSEA results in Fig. 4c further illustrated that the “Hallmark_IFNγ response” gene phenotype was further enriched in the Rh2+IFNγ group when compared to IFNγ alone. To verify this, several classic IFNγ signaling downstream genes were selected, including XAF1, MX2, ISG20, CD274, HLA-A, and HLA-C, which were significantly upregulated in the Rh2+IFNγ group according to the RNA-seq data (Fig. 4d), and their expression levels were validated using qRT-PCR. The results showed that Rh2+IFNγ treatment indeed significantly upregulated the mRNA levels of these IFNγ signaling-associated genes compared to the Rh2 or IFNγ (Fig. 4e). Thus, these findings indicated that Rh2 promoted the sensitivity of cancer cells to IFNγ, further intensifying the activation of the IFNγ signaling.

Fig. 4. Rh2 in combination with IFNγ intensified the activation of IFNγ signaling pathway.

a Venn diagram showing the intersection of significantly differentially co-upregulated genes identified by RNA-seq among “Rh2 versus Control (Up)”, “IFNγ versus Control (Up)”, and “Rh2+IFNγ versus Control (Up)”. Cut-off for the upregulation genes: P value < 0.05, FC > 2. b GONetwork diagram by metascape analysis showing the enriched ontology clusters associated with 559 genes which were uniquely upregulated in “Rh2+IFNγ versus Control (Up)”. Different enriched ontology clusters were represented by different color dots. c Mountain plot by GSEA analysis showing the increased gene signature of “Hallmark_IFNγ response” in Rh2+IFNγ compared with IFNγ. d Heatmap showing the transcriptional z-score of XAF1, MX1, ISG20, CD274, HLA-A, and HLA-C. The expression level of each gene was normalized using z-score, with red to blue representing high to low expression levels of the genes. e The mRNA level of XAF1, MX1, ISG20, CD274, HLA-A, and HLA-C was detected by using qRT-PCR in HT29 cancer cells treated with Rh2 (10 μM) and IFNγ (10 ng/mL) for 24 h.

Inhibition of JAK1/2 rescued Rh2 + IFNγ-induced cancer cell death

Given that Rh2 enhanced cancer cells’ sensitivity to IFNγ, we sought to determine whether the IFNγ signaling pathway was the key factor in triggering enhanced cancer cell death. It is well known that JAK1/JAK2 and STAT1 are crucial downstream proteins in the IFNγ signaling pathway [32]. Cancer cells were treated with JAK1/2 inhibitor ruxolitinib and STAT1 inhibitor fludarabine, respectively. The results showed that only ruxolitinib significantly reversed the morphological changes associated with Rh2+IFNγ-induced cell death and reversed the decrease in the cell viability (Fig. 5a, b); however, no similar phenomenon was observed with the STAT1 inhibitor fludarabine (Fig. 5b). This interesting finding indicated that Rh2 augmented cancer cells’ responsiveness to IFNγ through JAK1/2, but the downstream of JAK1/2 remained unclear, and it was not attributed to STAT1. Western blot analysis in Fig. 5c revealed that ruxolitinib significantly rescued the cleavage of caspase-3 and PARP. These results also suggested that further activation of the IFNγ signal is a crucial factor in inducing cancer cell death by the combo treatment.

Fig. 5. JAK1/2 inhibitor ruxolitinib rescued Rh2 + IFNγ-induced cell death.

a The cellular morphology of HT29 cancer cells after being treated with Rh2 (10 μM) plus IFNγ (10 ng/mL) for 24 h, with or without the pre-treatment of ruxolitinib (1 μM). Scale bar: 100 μm. b With or without the pre-treatment of ruxolitinib (1 μM) or fludarabine (1 μM), HT29 cancer cells were treated with Rh2 (10 μM), IFNγ (10 ng/mL), or both. Then the cytotoxicity was assayed by MTT. c The protein level of caspase-3, cleaved caspase-3, PARP, cleaved PARP and GAPDH were detected by Western blot in HT29 by the indicated treatment. ****P < 0.0001.

Rh2 + IFNγ signature gene set had potentials to predict the clinical outcomes

Subsequently, we aimed to explore whether the combination strategy of Rh2 and IFNγ had the potential clinical translation value. Therefore, based on the RNA-seq data, top 20 upregulated genes in the Rh2+IFNγ group were selected to form a gene set (Fig. 6a), which was named with Rh2+IFNγ signature genes (RISG) by us. Notably, most of these genes were the downstream of IFNγ signaling pathway. The heatmap in Fig. 6a showed transcription levels of these genes varying between IFNγ and combo treatment groups. It could be seen that the combination with Rh2 further upregulated the expression of these genes. Meanwhile, the RISG was input into the metascape for performing the OGNetwork analysis and the result revealed that those genes were highly associated with the signaling pathway related to “Response to virus”, “Antiviral innate immune response”, “Response to type interferon”, and “Response to interferon-beta”, etc. (Fig. 6b). Then, we submitted this RISG gene set to the online analysis platform BEST to evaluate the expression of this gene set across the different progression stage. A declining tendency of the RISG expression was observed with the disease progression (from stage I to IV) of colorectal cancer (CRC) and skin cutaneous melanoma (SKCM), based on the data from The Cancer Genome Atlas (TCGA) database (Fig. 6c). What’s more, microsatellite instability-high (MSI-H) CRC tumors exhibited a higher expression of RISG than those with microsatellite stability (MSS) or microsatellite instability-low (MSI-L) (Supplementary Fig. S2).

Fig. 6. The identification of a signature gene set for Rh2 + IFNγ.

a Heatmap showing the Top 20 upregulated gene list in Rh2+IFNγ group according to the differentially expression identified by RNA-seq. These 20 genes were defined with Rh2+IFNγ signature genes (RISG). The expression level of each gene was normalized using z-score, with red to blue representing high to low expression levels of the genes. n = 3, P value < 0.05. b GONetwork diagram by metascape analysis showing the enriched ontology clusters associated with RISG. c BEST analysis showing the RISG expression level across the different disease stage of CRC and SKCM. The analysis was based on the dataset including TCGA_CRC and TCGA_SKCM. The P values were calculated by one-way ANOVA, multiple comparison test. d, e Heatmap profiling the correlation between RISG expression and immune cell infiltration in CRC tumor or SKCM tumor was obtained from BEST. The correlation between the expression of the RISG gene set and the infiltration of various immune cells was represented by 1 (red) to -1 (blue). Correlation score>1, positively. Correlation score<1, negatively. f The expression of RISG in tumors of cancer patients who responded or did not respond to anti-PD-1, anti-MAGE-A3, or CAR-T treatment is derived from the BEST analysis. g Progress-free survival of CRC patients taken from BEST analysis on TCGA_CRC dataset. h Disease-free survival of CRC patients taken from BEST analysis on TCGA_CRC dataset. i Disease-specific survival of CRC patient taken from BEST analysis on GSE87211 dataset. j Relapse-free survival of CRC patient taken from BEST analysis on GSE106584 dataset. k Progress-free survival of SKCM patients taken from BEST analysis on GSE65904 dataset. l Overall survival of SKCM patients taken from BEST analysis on TCGA_SKCM dataset. m Overall survival of SKCM patients taken from BEST analysis on GSE190113 dataset. n Disease-specific survival of SKCM patient taken from BEST analysis on GSE65904 dataset. The P values were calculated by the log-rank test.

Furthermore, the expression of RISG also demonstrated a correlation with the intratumoral infiltration of immune cells. Specifically, the expression of RISG in CRC and SKCM tumors was significantly and highly positively correlated with immune cell phenotypes related to antigen presentation, such as “Dendritic cells_activated” and “Macrophages_M1” (Fig. 6d, e). This finding matched with the previously observed enrichment of type I interferon signals and antigen presentation signals in the Rh2+IFNγ treatment. Additionally, the activation phenotype of T cells is also positively correlated with RISG expression (Fig. 6d, e). To our surprise, tumors of patients who had response to the immunotherapy like anti-PD-L1, anti-melanoma antigen family A3 (MAGE-A3), and chimeric antigen receptor T cell (CAR-T) therapy, had higher RISG expression than those not (Fig. 6f). This indicated the potential clinical value of RISG as a predicted gene set for choosing the responders to immunotherapy. Moreover, CRC patients with high expression of RISG in tumor showed better progress-free survival (TCGA_CRC), disease-free survival (TCGA_CRC), disease-specific survival (GSE87211), and relapse-free survival (GSE106584), by comparing with patients with lower expression of RISG (Fig. 6g–j). Similarly, melanoma patients with high RISG expression showed significantly prolonged progress-free survival (GSE106584), overall survival (TCGA_SKCM and GSE190113), and disease-specific survival (GSE65904), compared with those with low expression of RISG (Fig. 6k–n).

Discussion

Our results showed that Rh2 remarkably increased the sensitivity of cancer cells to IFNγ treatment, which resulted in an enhancement of cancer cell death and significantly inhibited the tumor growth in vivo, providing a potential combination strategy for cancer treatment. Clinically, IFNγ has been reported for use in treating adult T cell leukemia [9], and another clinical study showed that ovarian cancer patients receiving chemotherapy combined with IFNγ treatment had better progression-free survival [10]. Despite these positive findings and ongoing clinical trials exploring its potential in cancer treatment, IFNγ has not been widely used in clinical practice. Recently, there have also been some clinical trials on IFNγ-based combination immunotherapy, such as combined with nivolumab (NCT02614456) and pembrolizumab (NCT03063632). However, most of these trials are still in the early stages of development, at clinical phase I or II. One clinical trial result showed that in the combination treatment of nivolumab with IFNγ, a dose of 50 μg/m² of IFNγ was relatively safe [33]. Moreover, 1 patient out of a total of 26 enrolled achieved complete response, and 3 out of 26 achieved stable disease [33]. Although additional clinical data are required to comprehensively support the use of IFNγ in cancer therapy or as a combinatorial strategy, the existing clinical trials have at least shown that IFNγ retains potential value in treating cancer. Indeed, there exist certain limitation in the application of IFNγ, which consequently restrict its clinical utilization. Similar with most of cytokines, IFNγ has a poor pharmacokinetics in vivo, characterized by a short half-life, indicating poor drug retention and rapid elimination from the body [34]. Furthermore, systemic administration of IFNγ frequently elicited adverse effects [35]. In the future, it may become feasible to improve the druggablity of IFNγ through pharmaceutical approach. And it is meaningful to explore combinational strategies that, on the one hand, can sensitize cancer cells to IFNγ treatment, on the other hand, can reduce the dosage to mitigate the side effects. Therefore, we believe that anti-cancer strategies that potentiate the sensitivity of cancer cells to IFNγ treatment, such as Rh2 used in our study, are of research significance and provide valuable insights into clinical research.

Concurrently, our results showed that the combination of Rh2 with IFNγ not only induced cancer cell death, but also activated the STING signaling pathway. In our previously published study, we reported that the combination of Rh2 and IFNγ triggered CXCL10 production by activating the TBK1-IRF3 signaling pathway in cancer cells [19]. In the current study, we further found that this combination also increased STING phosphorylation, an essential upstream activator of TBK1-IRF3. Furthermore, the combo treatment significantly upregulated the transcription levels of downstream IFNα and IFNβ, further confirming the activation of the STING-TBK1-IRF3 signaling pathway in cancer cells. Within the tumor microenvironment, activation of the cGAS-STING pathway promoted the maturation of dendritic cells and infiltration of T cells, resulting in an enhancement of anti-tumor immunity [36]. For instance, chemotherapeutic agents had the potential to trigger the release of cytosolic DNA, subsequently activating the cGAS-STING pathways. This activation not only enhanced the production of type one interferons but also augmented anti-tumor responses by T cells, potentially enhancing the efficacy of cancer treatment [36, 37]. However, in our study, it remains unclear whether Rh2+IFNγ-mediated STING activation reshaped the tumor microenvironment to enhance anti-tumor immune responses or not. We have not yet identified any murine colorectal cancer cell lines, including MC38 and CT26 cell lines, that are sensitive to the combination treatment of Rh2 plus IFNγ in vitro (data not shown). Consequently, thus far, it is difficult to choose an immunocompetent mice model to evaluate the effect of combination treatment in vivo.

Mechanistically, RNA-seq data suggested that Rh2 further amplified the activation of interferon signaling when combined with IFNγ, and the use of JAK1/2 inhibitors could reverse the combo treatment-induced cancer cell death. We also examined STAT1, a crucial transcription factor as the downstream of interferon signaling [1]. However, we found that the combination treatment did not further augment STAT1 activation compared to IFNγ alone (data not show). Additionally, the use of a STAT1 inhibitor fludarabine failed to reverse the cell death induced by the combination treatment (Fig. 5b). This indicated that Rh2 might also activate other unclear downstream pathways of JAK1/2, which warrants further investigation. RNA-seq analysis revealed that compared to the monotherapy, Rh2 plus IFNγ significantly upregulated the expression of XAF1 (XIAP-associated factor 1). Previous literature has reported that XAF1, possesses apoptotic-inducing properties by antagonizing the anti-apoptotic effect of XIAP [38, 39]. However, knocking down the expression of XAF1 using siRNAs failed to reverse the cytotoxicity effect induced by the combination treatment (data not show). As our data indicated, the treatment resulted in a sharp increase in the expression of numerous interferon-stimulated genes (ISGs) in cancer cells. Additionally, Rh2, as a natural product, owns multi-target properties. These suggested that inhibiting a single target might not abolish the effect of combo treatment. The RISG gene set obtained from this combo treatment also indicated that the anti-cancer effect was contributed by alterations in multiple genes.

As depicted in Fig. 1a, the combination treatment only demonstrated an enhanced cytotoxicity effect in the HT29, LoVo, and T84 cancer cell lines, whereas its impact was less pronounced in other cell lines. We examined the microsatellite status of these cell lines and found that HT29 and T84 were microsatellite stable [40, 41], whereas HCT116, LoVo, and RKO were microsatellite unstable [40, 41]. Regarding oncogenic mutations, T84, LoVo, and HCT116 cells harbor KRAS mutations [42], RKO exhibits oncogenic mutations in both KRAS and BRAF [43], and HT29 has a TP53 mutation [44]. Specifically, our findings revealed that the cytotoxicity induced by the combo treatment could not be correlated with either the microsatellite status or oncogenic mutations of the cell lines. In our study, Rh2 enhanced the sensitivity of cancer cells to IFNγ, leading to a substantial amplification of interferon signaling pathways and ultimately triggering cell death. Therefore, we also considered the possibility that the sensitivity of the cell lines, including HT29, LoVo, and T84 which responded to this combo treatment, were more sensitive to the activation of interferon signaling. There is reported that these specific cell lines expressed higher levels of IFNγ receptor [45], which might explain their sensitivity to the combo treatment.

The RISG gene set, consisting of top 20 up-regulated genes in the Rh2+IFNγ treatment group, holds promise to predict patient clinical outcome and responsiveness to cancer immunotherapy. There exist well-established clinical criteria and biomarkers for selecting patients who are eligible for anti-PD-1/PD-L1 immunotherapy. For instance, the assessment of PD-L1 expression within tumor tissues, including both cancer cells and immune cells, guiding the clinical selection of patients suitable for anti-PD-1/PD-L1 immunotherapy [46, 47]. However, these biomarkers also possess certain limitations and may not be fully applicable to all clinical cases. Therefore, the development of additional immunotherapy biomarkers remains an ongoing endeavor. RISG gene set had a promising potential to assist in evaluating the effectiveness of immunotherapy and predict prognosis. On one hand, it predicted the potential clinical significance of this combination treatment; on the other hand, it provided a list of potential tumor biomarkers. Further experiments are necessary to substantiate the accuracy of this gene set in predicting clinical outcomes and immunotherapy responses, including the validation on clinical samples. Additionally, the current gene count in this RISG may be somewhat excessive for use as a biomarker, and optimizing this gene set to facilitate easier detection should be further considered in the future.

Conclusion

In summary, we have observed an interesting phenomenon that Rh2 increased the sensitivity of cancer cells to IFNγ treatment, leading an enhancement of cancer cell death. This combo treatment also exhibited an inhibitory effect on the tumor growth, providing a novelly potential combinational strategy for treating cancer. At the same time, the RISG gene set we defined possessed the capacity to predict clinical patient outcomes and responsiveness to immunotherapy, indirectly reflecting the value of Rh2 in combination with IFNγ in the field of cancer therapy.

Author contributions

MYH, TL and JJL designed the experiment. MYH, CCX, QC and ZHY performed the animal study. MYH, QC, YMZ and WYL performed the cell experiments. MYH, TL, MQH and JJL analyzed the data. MYH and JJL drafted the manuscript. All authors reviewed and revised the manuscript.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at 10.1038/s41401-025-01557-z.