Parimifasor elicits broad-spectrum antitumor effects by suppressing the expression of oncogenic genes

Introduction

Cancer remains a primary cause of mortality worldwide, characterized by uncontrolled cell proliferation, metastatic dissemination, and therapeutic resistance. Despite advances in surgery, radiotherapy, chemotherapy, and targeted therapies, issues such as systemic toxicity, narrow therapeutic windows, and acquired drug resistance highlight the pressing need for novel agents with improved efficacy and safety profiles (1,2). Immunotherapies, such as immune checkpoint inhibitors, represent a paradigm shift but exhibit variable response rates across cancer types and are associated with risks of immune-related adverse events (3).

Conventional chemotherapeutics (e.g., platinum agents and taxanes) primarily interrupt DNA synthesis or microtubule dynamics but lack specificity (4,5). The clinical utility of these agents is frequently constrained by dose-limiting toxicities affecting multiple organ systems. Chemotherapy-induced multi-organ toxicity, including cardiotoxicity, neurotoxicity, and nephrotoxicity, affects 40–80% of patients, interrupts 20–30% of treatment cycles, and doubles long-term mortality (6). Additionally, gastrointestinal toxicities such as mucositis, diarrhea, and emesis are common with antimetabolites and DNA intercalators, often necessitating dose reductions or treatment delays (7). The concept of the therapeutic window, the range between maximum tolerated dose and minimum effective dose, remains a critical consideration. Although novel modalities such as antibody-drug conjugates were initially theorized to expand this window by enhancing tumor specificity, mounting clinical evidence suggests that the maximum tolerated dose of antibody-drug conjugates, when normalized for cytotoxin content, is not substantially different from that of unconjugated small molecules (8).

Targeted therapies inhibit oncogenic drivers: tyrosine kinase inhibitors (e.g., epidermal growth factor receptor inhibitors such as erlotinib) block aberrant signaling pathways (9,10), whereas poly(ADP-ribose) polymerase (PARP) inhibitors (e.g., olaparib) exploit DNA repair deficiencies in BRCA-mutant cancers (11,12). Despite their precision, acquired resistance inevitably emerges through diverse mechanisms. These include genetic alterations such as target site mutations, molecular adaptations involving activation of compensatory pathways, and contributions from the tumor microenvironment (13,14). Drug resistance is a property exhibited by almost all cancer types, including carcinomas, leukemias, sarcomas, and lymphomas, and remains a major contributor to poor prognosis and therapeutic failure (15).

Parimifasor (CAS: 1796641-10-5), a small-molecule compound featuring a dichloro-, tetrafluoro-, nitrogen-containing heterocyclic structure, is currently classified primarily as an immunomodulatory and exhibits anti-inflammatory activity by suppressing inflammatory responses. However, its specific molecular target(s) remain uncharacterized. Patented evidence demonstrates that parimifasor significantly inhibits severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication in a dose-dependent manner in trans-complemented cell culture systems (CN117482086B) (16). Our preliminary study further revealed potent inhibitory activity of parimifasor against 15 human tumor cell lines (CN116570589A) (17).

This study aimed to elucidate the molecular basis of the antitumor effects of parimifasor by employing RNA-sequencing (RNA-seq) approach. This approach integrated in vitro cytotoxicity assays and RNA-seq analysis of parimifasor-treated KYSE140 and LN229 cells to delineate the transcriptional landscape altered by parimifasor by identifying critical differentially expressed genes (DEGs) and enriched biological pathways underlying its mechanism. This approach seeks to provide a foundational understanding of the molecular mechanism through which parimifasor inhibits tumor and biomarker discovery. We present this article in accordance with the ARRIVE and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0256/rc).

Methods

Cells and culture conditions

This study utilized the following 12 human tumor cell lines: NCI-H460, 143B, KYSE140, HCCC-9810, 786-O, HCT-116, A375, LN229, H1299, Hela, PANC-1, and HepG2. The culture media (Gibco, NY, USA) were as follows: HCT-116 cells were maintained in McCOY’s 5A medium; KYSE140, H1299, 786-O, NCI-H460, HCCC-9810, and 143B in RPMI-1640; Hela and HepG2 in MEM; and A375, LN229, and PANC-1 in Dulbecco’s Modified Eagle Medium. All media were supplemented with 10% fetal bovine serum, 1% penicillin-streptomycin solution, and additional components for specific cell lines: 1% sodium pyruvate + glutamax (Hela, HepG2, and HCCC-9810) or 0.015 mg/mL 5-bromo-2’-deoxyuridine (143B). Cells were cultured at 37 ℃ under 5% CO₂ and 95% humidity in a CO₂ incubator (Thermo Fisher Scientific, MA, USA). Subculturing was performed using 0.25% trypsin-EDTA when cells reached 80–90% confluence. Following centrifugation, cells were resuspended and seeded into new dishes. A Tecan SPARK multimode microplate reader was used in absorbance measurement.

Parimifasor treatment and cell viability assay

Parimifasor (Shanghai Taoshu) was dissolved in dimethyl sulfoxide (DMSO) to prepare a 10 mM stock solution, serially diluted to generate nine concentration points [highest concentration: 500× estimated 50% inhibitory concentration (IC₅₀) for each cell line], with a final DMSO concentration of 0.2%. Cells were seeded into 96-well plates in the following densities: HCT-116, 3,200 cells/well; KYSE140, 16,000 cells/well; Hela, 1,800 cells/well; A375, 1,600 cells/well; Panc-1, 3,200 cells/well; H1299, 2,500 cells/well; LN229, 2,400 cells/well; HepG2, 6,000 cells/well; 786-O, 3,000 cells/well; NCI-H460, 14,000 cells/well; HCCC-9810, 1,500 cells/well; and 143B, 1,600 cells/well. After adherence, 30 µL of 2× parimifasor working solution was added to each well, which was then incubated for 72 h. Cell viability was assessed using the Cell Counting Kit-8 (CCK-8) assay kit for all cell lines, except HepG2, which was analyzed using the CTG method: 10 µL of the CCK8 reagent was added per well and then incubated for 2–4 h, and absorbance at 450 nm was measured. The inhibition rate was calculated as follows:

Inhibition (%) = [1 − (ODsample − ODblank)/(ODnegative control – ODblank)] × 100%

where negative controls contained cells treated with the solvent, and blank controls comprised cell-free medium. Experiments were performed in triplicate. Dose-response curves were fitted using PRISM software to determine IC₅₀ values.

Tumorigenicity assay in nu/nu mice

Female BALB/c nude mice (aged 6–8 weeks) were used in the animal experiments. KYSE140 (esophageal squamous cell carcinoma; 1×10⁷ cells per mouse) or LN229 (glioblastoma; 1×10⁷ cells per mouse) cells were subcutaneously injected into the right dorsal flank of the mice. One week later, the injected mice were randomly divided into three groups (n=10 per group), and those injected with LN229 cells were divided into three groups (n=11 per group). The treatment regimens were as follows: the control group received intraperitoneal injection of 0.5% CMC-Na solution every 2 days, the 2 mg/kg parimifasor group received intraperitoneal injection of parimifasor dissolved in 0.5% CMC-Na solution (2 mg/kg) every 2 days, and the 4 mg/kg parimifasor group received intraperitoneal injection of parimifasor dissolved in 0.5% CMC-Na solution (4 mg/kg) every 2 days. Tumor size was measured every 4 days starting from the first day of treatment. Tumor volume (mm³) was calculated as follows: Volume = (length × width × width × 0.52). After 16 days of parimifasor treatment, the mice were euthanized, and tumors were excised for further analysis. Experiments were performed under a project license (No. NYISTIRB2024-037) granted by the Ethics Committee of Nanyang Institute of Technology, in compliance with institutional guidelines for the care and use of animals. A protocol was prepared before the study without registration.

RNA-seq

KYSE140 and LN229 cells, cultured using the aforementioned protocol, were treated with 0.5 µM parimifasor or 0.2% DMSO (control) for 36 h, with three biological replicates per group. Total RNA was isolated from cells using TRIzol^® Reagent (Magen, China) following the manufacturer’s instructions. RNA quality was assessed by measuring the A260/A280 ratio on a Nanodrop ND-2000 spectrophotometer (Thermo Fisher Scientific) and determining the RNA Integrity Number using an Agilent Bioanalyzer 4,150 system (Agilent Technologies, CA, USA). Paired-end sequencing libraries were constructed with the ABclonal mRNA-seq Library Prep Kit (ABclonal, China) according to the manufacturer’s protocol. High-throughput sequencing was performed on an Illumina NovaSeq 6,000 platform.

Data analysis

Raw sequencing data were processed using Fastp (v0.23.2) to remove adapter sequences and low-quality reads (18). The human reference genome GRCh38(hg38) was downloaded from the Ensembl Release 104 archive (May 2021 version, http://may2021.archive.ensembl.org/Homo_sapiens/Info/Index), and cleaned reads were aligned to the genome using HISAT2 (v2.2.1) (19). Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads via FeatureCounts (v2.0.3) (20). DEGs were identified using DESeq2 (v1.34.0) with the following thresholds: |log₂(fold change)| >1 and adjusted P value <0.05 (21). Functional enrichment analysis of DEGs was performed for Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the clusterProfiler R package (v 3.8) (22-24). Terms or pathways with P<0.05 were considered significant. The DEGs were further analyzed using TCGA data (https://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-320) and clinical data (https://xenabrowser.net/datapages/?host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=http%3A%2F%2F127.0.0.1%3A7222). Genes showing statistically significant differential expression between tumor and normal tissues across multiple cancer types were prioritized. The literature review confirmed their previously documented roles in oncogenic processes. The genes meeting these criteria were defined as candidate oncogenic genes.

RNA extraction, cDNA synthesis, and real-time polymerase chain reaction (PCR)

Total RNA was extracted using the RNA Isolation Total RNA Extraction Reagent (Vazyme, China). cDNA was synthesized using the HiScript II Q RT SuperMix for qPCR (+gDNA wiper) kit (Vazyme) and used for the quantitative PCR analysis. The cDNA samples were amplified in CFX Connect real-time qPCR system (Bio-Rad, USA) using Taq Pro Universal SYBR qPCR Master Mix (Vazyme) with a cycling temperature of 60 ℃ and a single peak on the melting curve to obtain a single product according to the manufacturer’s instructions. The 20-µL reaction volume consisted of forward (0.4 µL) and reverse (0.4 µL) primers, 2× Taq Pro Universal SYBR qPCR Master Mix (10 µL), ddH2O (7.2 µL), and cDNA (2 µL). For each gene, three technical replicates and three repetitions were prepared. Each mRNA expression level was normalized to that of the reference gene GAPDH. The expression level of each mRNA molecule was evaluated using the 2^−ΔΔCt method. The statistically significant differences as determined by Student’s t-test.

Results

Parimifasor inhibits multiple cell proliferation

CCK-8, a cell viability assay, was employed to evaluate the effects of parimifasor on the survival of 12 human tumor cell lines. Parimifasor exerted concentration-dependent inhibitory effects on all 12 tested cell lines (Figure 1, Table S1). Analysis of IC₅₀ values revealed that parimifasor inhibited the remaining tumor cell lines at concentrations <1 µM, except for HepG2, NCI-H460, and HGC-27, indicating its highly effective inhibitory activity, broad-spectrum antitumor potential, and justification for further investigation (Table S2).

Figure 1 Parimifasor treatment inhibits cell viability in multiple human tumor cells. Cell viability was then measured by CCK-8 assay. CCK-8, Cell Counting Kit-8.

Parimifasor inhibits tumor growth

To investigate the antitumor activity of parimifasor in vivo, tumor-bearing models were established in NU/NU mice using KYSE140 and LN229 cells. Tumor volume changes were analyzed in the treatment groups administered with 2 and 4 mg/kg parimifasor. In KYSE140 xenografts, parimifasor-treated groups exhibited a significant reduction in tumor volume compared with the control group. Specifically, tumor growth was markedly suppressed in the parimifasor-treated group, with higher-dose regimens (4 mg/kg) showing superior efficacy in inhibiting tumor progression and achieving smaller final tumor volumes (Figure 2A,2B). Similarly, in LN229 xenografts, parimifasor administration led to comparable antitumor effects, as evidenced by small tumor volumes and low growth rates (Figure 2C,2D). Thus, parimifasor possesses robust in vivo antitumor activity. The observed efficacy across KYSE140 and LN229 models further indicates its broad-spectrum antitumor potential. Additionally, the dose-dependent suppression of tumor growth highlights that increasing the dosage within the tested range improves therapeutic outcomes, underscoring the potential of the compound for further preclinical optimization.

Figure 2 Parimifasor inhibits the growth of esophageal squamous cell carcinoma and glioma in vivo. (A) Representative tumors isolated from the control, 2 mg/kg parimifasor, and 4 mg/kg parimifasor treated groups of subcutaneous esophageal squamous cell carcinoma tumor model. (B) Esophageal squamous cell carcinoma tumor volume was recorded every 4 days. (C) Representative tumors isolated from the control, 2 mg/kg parimifasor, and 4 mg/kg parimifasor treated groups of subcutaneous glioma tumor model. (D) Glioma tumor volume was recorded every 4 days.

Transcriptome difference between the control and parimifasor-treated groups

To elucidate the molecular mechanism by which parimifasor inhibits tumors, KYSE140 and LN229 cells were treated with 0.5 µM parimifasor for 36 h (designated as KD and LD groups, respectively), RNAs were extracted from these treated cells and their corresponding control groups (KC and LC), and RNA-seq was performed. Each sample yielded over 63 million reads (approximately 8 GB of data) (Table S3). Clean reads from all samples exhibited a mapping rate of ≥89% to the reference genome (Table S4).

The correlation analysis demonstrated high correlations between the treatment and control groups within the samples and among multiple replicates, whereas a relatively lower correlation was observed between the two distinct samples of KYSE140 and LN229 cells. Additionally, the principal component analysis of the samples revealed a similar pattern (Figure 3A,3B). Consistently, the heatmap of expression data from a total of 12 samples (treatment and control groups for both cell lines) showed that cells of the same type exhibited more similar expression profiles. However, following treatment with parimifasor, differences in the expression patterns of certain genes became apparent (Figure 3C).

Figure 3 Transcriptomic profiling of KYSE140 and LN229 cells following parimifasor exposure. (A) Pairwise correlation matrix of transcript abundance across all 12 samples (three biological replicates per condition). (B) PCA plot showing sample clustering based on cell type and treatment. (C) Heatmap visualizing expression patterns of selected genes across control and parimifasor-treated cells (rows: genes; columns: individual samples). PCA, principal component analysis.

DEGs between the control and parimifasor-treated groups

To investigate the gene expression patterns in the two cell types following parimifasor treatment, DEGs were compared between the treatment and control groups. As a result, 850 DEGs were identified in KYSE140 cells, comprising 522 upregulated (e.g., HOTAIRM1 and MALAT1) and 328 downregulated (e.g., IFNL1 and MANF) genes (Figure 4A, available online: https://cdn.amegroups.cn/static/public/tcr-2026-1-0256-1.xlsx). In LN229 cells, 425 DEGs were identified, including 162 upregulated (such as DDIT4 and HRAT92) and 263 downregulated (including CSRNP1 and HES1) genes (Figure 4B, available online: https://cdn.amegroups.cn/static/public/tcr-2026-1-0256-2.xlsx). Notably, KYSE140 cells exhibited twice as many DEGs as LN229 cells, implying the increased sensitivity of KYSE140 cells to parimifasor (Figure 4C).

Figure 4 Identification and functional annotation of DEGs. (A) Volcano plot of DEGs in KYSE140 cells (parimifasor vs. control). Significantly upregulated (red) and downregulated (blue) genes are marked. (B) Corresponding volcano plot for LN229 cells. (C) Bar chart summarizing the number of DEGs identified in each cell line. (D) KEGG pathway enrichment analysis of DEGs from KYSE140 cells. (E) KEGG pathway enrichment analysis of DEGs from LN229 cells. Bubble size represents gene count; color indicates significance level. DEG, differentially expressed gene; FC, fold change; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes; NOD, nucleotide-binding oligomerization domain.

Enrichment analysis of DEGs in KYSE140 cells demonstrated predominant involvement in pathways including cytokine-cytokine receptor interaction, interleukin (IL)-17, influenza A, nucleotide-binding oligomerization domain (NOD)-like receptor, Toll-like receptor (TLR), and NF-kappa B signaling pathways (Figure 4D). In contrast, DEGs in LN229 cells were primarily enriched in pathways such as influenza A, COVID-19, hepatitis C, measles, NOD-like receptor, Epstein-Barr virus infection, RIG-I-like receptor (RLR), cytosolic DNA-sensing, and TLR signaling pathways (Figure 4E).

Parimifasor induces DEG function

Given the significant inhibitory effects of parimifasor on both KYSE140 and LN229 cells, their DEGs were analyzed to elucidate potential shared regulatory pathways. This revealed 81 overlapping DEGs between the two cell lines, with 769 and 344 DEGs specific to KYSE140 and LN229 cells, respectively (Figure 5A). In a pathway enrichment analysis, 81 overlapping genes were predominantly associated with RLR, NOD-like, TLR signaling pathways, among others (Figure 5B).

Figure 5 Overlap and trend analysis of parimifasor-regulated genes across both cell lines. (A) Venn diagram showing the intersection of DEGs between KYSE140 and LN229 cells (81 common genes). (B) KEGG pathway enrichment of the 81 overlapping DEGs. (C) Time-series trend analysis of all expressed genes following parimifasor treatment. Clusters 1 and 4 (highlighted) show coordinated upregulation or downregulation. (D) KEGG pathways enriched in cluster 1 genes. (E) KEGG pathways enriched in cluster 4 genes. Dot size reflects the number of genes per pathway; color intensity indicates enrichment significance. DEG, differentially expressed gene; ECM, extracellular matrix; KEGG, Kyoto Encyclopedia of Genes and Genomes; NOD, nucleotide-binding oligomerization domain.

Additionally, trend analysis using RNA-seq data revealed that genes in clusters 1 and 4 exhibited coordinated upregulation or downregulation after treatment, potentially associated with parimifasor-induced transcriptional activation or repression (Figure 5C). In the enrichment analysis, cluster 1 genes were predominantly enriched in KEGG pathways, such as the sphingolipid signaling pathway and axon guidance (Figure 5D). In cluster 4, genes were primarily enriched in pathways, including lysosome and herpes simplex virus 1 infection (Figure 5E).

DEG expression profile in tumors

To determine whether the aforementioned 81 overlapping DEGs are associated with cancer, their expression levels were further analyzed across various cancer types using TCGA database. Although only a subset of genes exhibited significant differential expression in limited cancer tissues, 10 genes [including ENSG00000178685 (PARP10), ENSG00000089127 (OAS1), ENSG00000111331 (OAS3), ENSG00000111215 (PRR4), ENSG00000124201 (ZNFX1), ENSG00000130589 (HELZ2), ENSG00000135899 (SP110), ENSG00000144655 (CSRNP1), ENSG00000163840 (DRX3L), and ENSG00000173193 (RARP14)] exhibited pronounced differences between tumor and normal tissues across multiple tumor types (Figure 6A-6J). These alterations in expression may critically influence tumor formation and progression.

Figure 6 The gene expression analysis base TCGA data. The expression level of (A) ENSG00000178685 (PARP10), (B) ENSG00000089127 (OAS1), (C) ENSG00000111331 (OAS3), (D) ENSG00000111215 (PRR4), (E) ENSG00000124201 (ZNFX1), (F) ENSG00000130589 (HELZ2), (G) ENSG00000135899 (SP110), (H) ENSG00000144655 (CSRNP1), (I) ENSG00000163840 (DRX3L), and (J) ENSG00000173193 (PARP14) in TCGA database. (K) Heatmap of the 10 candidate genes based on RNA-seq data (log₂TPM values). Blue to red indicates low to high expression. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001 by one-way analysis of variance. BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; CSRNP1, Cysteine-serine-rich nuclear protein 1; DRX3L, DEAD-box helicase 3 X-linked; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HELZ2, Helicase zinc finger 2; SP110, HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OAS1/3, 2’-5’-oligoadenylate synthetase 1/3; PAAD, pancreatic adenocarcinoma; PARP10/14, poly(ADP-ribose) polymerase 10/14; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; PRR4, Proline rich protein 4; READ, rectum adenocarcinoma; RNA-seq, RNA-sequencing; SP110 nuclear body protein; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma; TPM, transcript per million; UCEC, uterine corpus endometrial carcinoma; ZNFX1, Zinc finger nF-X1 type 1.

Analysis of expression patterns following parimifasor treatment demonstrated that, except for ENSG000001001215 (PRR4), which displayed a marginal increase in KYSE140 cells, all other genes exhibited a reduction trend in both tumor cell lines (Figure 6K). Overall, 10 candidate genes were selected for validation through quantitative reverse-transcription PCR expression pattern. The expression patterns were largely consistent with the transcriptomic data, as all genes exhibited a decreasing trend following parimifasor treatment in LN229 and KYSE140 cells, confirming the reliability and accuracy of candidate gene selection (Figure 7, Table S5). This finding suggests that parimifasor may suppress diverse tumor development by inhibiting the expression of these genes.

Figure 7 The gene relative expression level after parimifasor treatment. (A) Ten gene expression levels in LN229 cells after parimifasor treatment. (B) Ten gene expression levels in KYSE140 cells after parimifasor treatment. Expression levels were normalized to GAPDH. Data are presented as mean ± standard deviation. *, P<0.05; **, P<0.01; ***, P<0.001. qRT-PCR, quantitative real-time polymerase chain reaction.

Discussion

Cancer treatment and suppression remain significant global challenges. Despite substantial progress achieved through surgery, chemotherapy, radiotherapy, and targeted therapies, an optimal therapeutic regimen remains elusive. In this study, the broad-spectrum antitumor activity of parimifasor in vitro was identified, leading us to hypothesize that its antitumor effects might be mediated through oncogene suppression. To elucidate the underlying molecular mechanisms, RNA-seq was employed on parimifasor-treated tumor cells to identify potential molecular targets.

Conventional chemotherapy, a mainstay treatment, is limited by nonspecific toxicity, whereas targeted therapies are often hampered by drug resistance development, significantly restricting their clinical utility. Our results demonstrate that parimifasor not only exhibits antitumor activity but also possesses potent broad-spectrum inhibitory effects against multiple tumor types at low concentrations (<1 µM) (Figure 1). Currently classified primarily as an immunomodulator with anti-inflammatory activity, parimifasor has been shown in a patent by Li et al. to inhibit SARS-CoV-2 replication. However, its application as an antitumor agent has not been previously reported. Importantly, the broad-spectrum activity was further validated in vivo, where parimifasor significantly inhibited the growth of both esophageal squamous carcinoma (KYSE140) and glioblastoma (LN229) xenografts (Figure 2), suggesting that its antitumor effect extends across distinct tumor types and tissue origins.

In the present study, transcriptomic analysis revealed 81 DEGs that are potentially associated with parimifasor-induced antitumor activity. These DEGs were significantly enriched in the RLR, NOD-like, and TLR signaling pathways (Figure 5). TLRs and RLRs are critical initiators of signaling cascades that play pivotal roles in both innate and adaptive immune responses (25,26). Notably, the modulation of these pathways was observed in pure tumor cell cultures devoid of immune cells, indicating that parimifasor may act directly on tumor cells by targeting cancer cell-intrinsic innate immune pathways. Recent evidence indicates that the RLR pathway can also induce antitumor immune responses or sensitize immunologically cold tumors to immune checkpoint inhibitors (27). Moreover, components of these pathways are increasingly recognized to function within tumor cells themselves, where they regulate proliferation, apoptosis, metastasis, and chemoresistance. Similarly, the NOD-like receptor and TLR signaling pathways are intimately linked to the development and treatment of immune-related and gastrointestinal cancers (28-30). These collective findings suggest that parimifasor may exert its tumor-suppressive effects by modulating key genes within these critical immune and inflammatory pathways.

Among these 81 DEGs, expression pattern analysis in TCGA database further demonstrated that the majority exhibited significant differences between cancerous and normal tissues. Notably, ENSG00000178685 (PARP10), ENSG00000089127 (OAS1), ENSG00000111331 (OAS3), ENSG00000111215 (PRR4), ENSG00000124201 (ZNFX1), ENSG00000130589 (HELZ2), ENSG00000135899 (SP110), ENSG00000144655 (CSRNP1), ENSG00000163840 (DRX3L), and ENSG00000173193 (RARP14) showed marked differential expression across multiple cancer types compared with normal tissues (Figure 6). PARP10 catalyzes mono-ADP-ribosylation of target proteins, influencing mitochondrial oxidative metabolism. Its low expression is associated with improved survival rates and affects tumorigenesis and metastasis in various cancers (11,31-33). OAS1/OAS3 not only correlates with prognosis in cancers such as lung and pancreatic cancer but are also implicated in cancer cell proliferation and metastasis under the regulation of terminal differentiation-induced noncoding RNA, a long noncoding RNA, establishing them as biomarkers for multiple malignancies (34-37). PRR4, an antimicrobial protein protecting the ocular surface and oral cavity, serves as a marker gene for the development of laryngeal cancer. Additionally, it suppresses the progression of non-small-cell lung cancer via the miR-877-5p/RAB3D pathway (38,39). ZNFX1 functions as a tumor suppressor by recognizing aberrant dsRNA derived from tumor cells, thereby initiating potent innate immune and inflammatory responses that inhibit tumorigenesis and progression (40). Conversely, ZNFX1-AS1, a long noncoding RNA transcribed antisense to ZNFX1, acts as an oncogene. Its high expression in various cancers (e.g., colorectal, prostate, and bladder) promotes tumor cell proliferation, migration, invasion, and metastasis (41-43). HELZ2 promotes c-Myc polyubiquitination, contributing to retinoblastoma tumorigenesis (44). Reports identifying SP110 and CSRNP1 as biomarkers for oral cancer and renal cell carcinoma, respectively (45-47), further support the credibility of these candidate genes for cancer detection and prognostic applications. Consistently, parimifasor treatment led to significant downregulation of all 10 genes in both KYSE140 and LN229 cells (Figure 7), reinforcing their role as a convergent node of its antitumor activity. The ability of parimifasor to simultaneously suppress this panel of core oncogenic drivers, which are exploited by multiple cancer types, provides a mechanistic explanation for its observed broad-spectrum efficacy.

In this study, the association between parimifasor and antitumor activity was identified for the first time. Nevertheless, current research lacks more in-depth experimental validation to confirm whether parimifasor directly regulates the expression of these target genes. Thus, further investigations should comprehensively evaluate toxicological and organ pathological profiles in animal models. This deeper analysis will provide essential data to explore the effects of combination therapies with standard treatments in future studies.

Conclusions

This study demonstrates that parimifasor effectively inhibits tumor cell viability. Transcriptome sequencing revealed that the modulation of key signaling pathways mediated its antitumor effects, including the RLR, NOD-like receptor, and TLR signaling pathways, which govern tumor cell proliferation and development. Furthermore, the integration with TCGA data identified 10 candidate target genes potentially responsible for its therapeutic activity. These findings provide a foundation for further elucidation of the molecular mechanisms underlying the antitumor effects of parimifasor and support its future clinical development.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the ARRIVE and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0256/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0256/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0256/prf

Funding: The Natural Science Foundation of Henan Province (No. 252300421390) and National Natural Science Foundation of China (No. 82273058) supported this investigation.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0256/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Experiments were performed under a project license (No. NYISTIRB2024-037) granted by the Ethics Committee of Nanyang Institute of Technology, in compliance with institutional guidelines for the care and use of animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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