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. 2025 Nov 11;44:102287. doi: 10.1016/j.bbrep.2025.102287

Comprehensive analysis on the implications of IAH1 in cancer development and progression

Chuanchuan Sun a,b,c, Xianghong Wang d, Yeye Yu b,c, Heng Shi e, Fanna Liu c, Shiping Zhu b,∗, Shengyun Sun b,c,∗∗
PMCID: PMC12650796  PMID: 41311370

Abstract

Objective

This study aimed to elucidate the role of the IAH1 gene across multiple cancer types.

Methods

Using multi-omics data from public databases including TCGA, GTEx, TIMER, and cBioPortal, we analyzed the expression patterns of IAH1 and their correlations with prognostic outcomes, immune cell infiltration, immune checkpoint markers, tumor mutational burden (TMB), microsatellite instability (MSI), response to immunotherapy, and drug sensitivity through bioinformatic tools and R software.

Results

IAH1 expression was significantly upregulated in most cancers and associated with adverse prognosis. Elevated IAH1 levels correlated strongly with immune-related gene expression, immune checkpoint levels, TMB, MSI, and immune infiltration. Moreover, IAH1 expression was linked to sensitivity to anticancer drugs and survival outcomes following immune checkpoint inhibitor (ICI) treatment.

Conclusions

IAH1 may serve as a promising immunoregulatory target and potential biomarker for diagnosis and prognosis in specific cancers.

Keywords: IAH1, Pan-cancer, Prognosis, Immune infiltration, Immunotherapy, Drug sensitivity

Highlights

  • •

    Analyze the underexplored IAH1 comprehensively in cancer.

  • •

    Reveal its role in cancer - related signaling and epigenetic mechanisms.

  • •

    Link IAH1 to metastasis and drug - resistance, proposing it as a therapeutic target.

  • •

    Highlight its clinical utility for patient stratification and prognosis.

1. Introduction

Cancer poses a major global public health challenge and accounts for a substantial portion of the worldwide disease burden [1,2]. In 2020, there were an estimated 19.3 million new cancer cases and approximately 10 million cancer-related deaths globally. This number is projected to rise to 28.4 million new cases by 2040 [1]. China alone accounted for 30.15 % of global cancer-related deaths, with cancer being the leading cause of mortality in the country since 2010. The crisis continues to escalate, marked by annual increases in morbidity, mortality, and economic burden [1]. Between 2005 and 2020, cancer-related deaths increased by 21.6 % [3]. Key mechanisms driving cancer development include aberrant proliferation, differentiation, senescence—often mediated by oncogenes, tumor suppressor genes, and genomic instability [4]. Additionally, dysregulated lipid metabolism, particularly resulting from alterations in lipid metabolism-related genes, is a recognized hallmark of cancer progression [[5], [6], [7]]. Growing evidence implicates adipose tissue metabolism in various cancers, including postmenopausal breast cancer, gastrointestinal cancers, lung cancer, and endometrial carcinoma [[8], [9], [10]].

IAH1 is a key gene regulating lipid metabolism [11,12], and has been reported to be upregulated in lung cancer [[13], [14], [15], [16]]. Elevated IAH1 expression has also been observed in ovarian cancer and other malignancies, where it correlates with poor prognosis [[17], [18], [19]]. Although associations between high IAH1 expression and certain cancers have been established, its pan-cancer role remains insufficiently systematically characterized.

In this study, we employed comprehensive bioinformatic approaches to conduct a pan-cancer analysis of IAH1, integrating genomic alterations, prognostic significance, immune cell infiltration, therapy response, and other multidimensional data. Our findings underscore a strong association between IAH1 and immune regulation, suggesting its potential as a prognostic biomarker and therapeutic target across multiple cancer types.

2. Materials and methods

2.1. Collection and analysis of datasets

The baseline mRNA and protein expression levels of IAH1 across a range of human tissues were obtained from the GTEx (www.gtexportal.org) and the Human Protein Atlas (HPA; https://www.proteinatlas.org/). Gene expression data for IAH1 in various cancer types were acquired from the TCGA (http://cancergenome.nih.gov) and GTEx databases via the Xena platform (https://xena.ucsc.edu/) at UCSC [20]. Analytical procedures were conducted using the GEPIA (http://gepia2.cancer-pku.cn/) [21] and the TIMER2 website (http://timer.cistrome.org/) [[22], [23], [24]]. Tumors represented by fewer than three samples within a single cancer type were excluded from analysis. Immunohistochemistry images of both normal and malignant tissues, along with immunofluorescence images of cells, were retrieved from the HPA.

2.2. Clinical staging and prognosis of IAH1 in tumors

We performed an unpaired Student's t-test and one-way ANOVA to assess the association between IAH1 expression and clinical stages across different cancer types, using R software (version 3.6.4). Additionally, Cox regression analysis was conducted to evaluate its correlation with prognostic outcomes, including overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) [25,26].

2.3. Correlation between IAH1 and immunity

We obtained a standardized pan-cancer dataset from UCSC and applied the deconvo_CIBERSORT method from the IOBR R package (v.0.99.9) [27,28] to estimate immune cell infiltration levels and compute Pearson correlation coefficients between IAH1 expression and each immune cell type. We also employed the ESTIMATE R package (v.1.0.13) [29] to calculate stromal, immune, and ESTIMATE scores for each tumor sample based on transcriptomic data. Given the close interplay between tumorigenesis and immunity, we further analyzed the relationships between IAH1 expression and immune checkpoint blockers (ICBs) [30], immune regulatory genes, and tumor stemness scores [31].

2.4. Drug sensitivity and immunotherapy analysis

We obtained data on IAH1 sensitivity to common chemotherapeutic agents from CellMinerTM [32] and used R 4.3.1 to select FDA-approved or clinically trialed drugs for further analysis. Additionally, we utilized the kmplot tool (https://kmplot.com/analysis/) and the TISMO database [33] to evaluate the relationship between IAH1 expression and response to ICB and cytokine-based therapies.

2.5. The relationship between IAH1 gene mutation, RNA modification, and cancer cell functions

To obtain nucleotide variation data, we analyzed samples using MuTect2 via the GDC portal (https://portal.gdc.cancer.gov/), and processed the results with the R package maftools (v.2.2.10) to identify protein domain alterations. Mutation frequencies of IAH1 across cancers were assessed using TIMER 2.0 and cBioPortal (http://www.cbioportal.org/). We further performed Pearson correlation analysis to evaluate the associations between IAH1 expression and tumor mutation burden (TMB) [34], microsatellite instability(MSI) [35], and tumor purity [30]. RNA modification profiles of IAH1 were analyzed using R 4.3.1, and functional insights in tumor contexts were explored via the CancerSEA database (http://biocc.hrbmu.edu.cn/CancerSEA/).

2.6. PPI network, GO, and KEGG analysis

We used GEPIA2 to identify genes correlated with IAH1 and constructed a protein-protein interaction (PPI) network using the STRING database. The network was visualized with Cytoscape (v.3.9.1). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID database [36] and results were visualized with Sangerbox 3.0 [37] along with online tools (https://www.bioinformatics.com.cn/).

2.7. Cell culture and qPCR

The MCF-7, MCF-10A, A549, BEAS-2B, 786-O, and HK-2 cell lines were acquired from the Cell Bank of the Chinese Academy of Sciences. Cells were cultured in DMEM complete medium containing 10 % fetal bovine serum and 1 % penicillin–streptomycin at 37 °C under sterile conditions.

Total RNA was isolated using the Total RNA Extraction Kit (RE-03113, Foregene, China) following the manufacturer's instructions. cDNA was synthesized with a Takara reverse transcription kit (RR047A, TaKaRa, Japan) and subsequently subjected to quantitative PCR ((RR820A, TaKaRa, Japan)). Reaction conditions were optimized in accordance with the manufacturer's protocols. GAPDH served as the internal reference gene, and relative gene expression levels were determined using the 2∧–ΔΔCt method. Primers were shown in Table 1.

Table 1.

Primers used for qPCR.

Gene Name Forward Reverse
GAPDH AGGTCGGTGTGAACGGATTTG TGTAGACCATGTAGTTGAGGTCA
IAH1 GGCCTCGCTTGTTGCTCTTC TCACATTTTCTGACCAGCCTGT
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2.8. Statistical analysis

In this study, statistical analyses were conducted using the aforementioned tools. Specifically, the Wilcoxon test was applied to compare IAH1 expression levels between tumor tissues and adjacent normal tissues via TIMER. Survival outcomes were assessed using Kaplan–Meier analysis, and differences between survival curves were evaluated with the log-rank test implemented in the “survival” R package. Associations between gene expression profiles were examined using Spearman correlation analysis.

3. Results

3.1. The IAH1 gene exhibits variable expression patterns in both normal and malignant tissues

The results indicated substantial variation in IAH1 expression across tissue types. Relatively high mRNA levels were observed in the tongue, testis, heart muscle, choroid plexus, adrenal gland, liver, parathyroid gland, skeletal muscle, colon, adipose tissue, and kidney. In contrast, tissues such as the pancreas, gallbladder, skin, salivary gland, retina, duodenum, and appendix showed comparatively low expression (Fig. 1A and B). At the protein level, IAH1 was moderately expressed in most tissues—including the thyroid gland, duodenum, small intestine, colon, rectum, liver, gallbladder, pancreas, kidney, urinary bladder, testis, and appendix. Notably elevated protein expression was detected in the hippocampus, lung, oral mucosa, and esophagus, whereas the adrenal gland, heart muscle, cerebral cortex, cerebellum, caudate, endometrium, cervix, breast, and appendix exhibited lower expression. No detectable protein levels were found in the vagina, ovary, smooth muscle, soft tissue, adipose tissue, or bone marrow (Fig. 1C).

Fig. 1.

Fig. 1

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IAH1 expression in various normal human tissues. The mRNA levels of IAH1 across different tissues obtained from the GTEx and HPA databases (A, B). (C) HPA database-derived profiles of IAH1 protein expression in multiple human tissues.

Evaluation of IAH1 expression across multiple cancer types revealed elevated expression in BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, STAD, THCA, and UCEC, with significant underexpression observed only in KICH (Fig. 2A and B, Fig. 3). Immunofluorescence data from the HPA database indicated that IAH1 is primarily localized to the nucleoplasm (Fig. 2C).

Fig. 2.

Fig. 2

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IAH1 profiles in assorted human carcinomas. (A) IAH1 expression levels in pan-cancer samples from the TCGA database were evaluated by TIMER2.0. (B) Box plot illustrations of IAH1 expression in tumors of CHOL, DLBC, LAML, PAAD, TGCT, THYM(Data derived from TCGA and GTEx databases, shown only malignancies where the expression was statistically significant). (C) Captures of immunofluorescence staining showing IAH1 protein in the nucleus, endoplasmic reticulum (ER), and microtubules for MCF-7 and U2OS cell lines, provided by the HPA database.∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

Fig. 3.

Fig. 3

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Immunohistochemistry pictures showing IAH1 gene expression profiles in healthy (left) versus cancerous (right) tissues (A–P).

3.2. Linkage of IAH1 protein levels with tumor stage and survival rates

We observed a significant positive correlation between IAH1 expression and advanced clinical stage in BLCA, KIRC, and LUAD. In contrast, no notable association was found in ACC, BRCA, CESC, CHOL, KICH, KIRP, PAAD, SKCM, or UCEC (Fig. 4A–L). Furthermore, clinical stage was significantly correlated with IAH1 expression in LUAD, STES, KIPAN, KIRC, and BLCA (Fig. 4M). Elevated IAH1 mRNA levels were associated with worse overall survival (OS) in patients with ACC (HR = 2.3), CESC (HR = 1.6), KICH (HR = 5), LGG (HR = 1.7), SKCM (HR = 1.4), and UVM (HR = 7.1) (Fig. 5A–G). Similarly, shortened disease-free survival (DFS) was observed in ACC (HR = 3.5), KIRC (HR = 1.6), PAAD (HR = 1.7), PRAD (HR = 1.7), and THYM (HR = 4.4) among patients with high IAH1 expression (Fig. 5H–M).

Fig. 4.

Fig. 4

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Correlation analysis of IAH1 and tumor pathological stages. Correlation between IAH1 expression and pathological stages of ACC, BLAC, BRCA, CESC, CHOL, KICH, KIRC, KIRP, LUAD, PAAD, SKCM and UCEC from TCGA datasets. Log2 (TPM + 1) was applied for log-scale (A–L). The interplay of IAH1 and tumor pathological stages across all TCGA cancers (M). Statistical significance is indicated by ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

Fig. 5.

Fig. 5

Fig. 5

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Clinical evaluation of IAH1 in pan-cancer. (A–G) Overall survival (OS) analysis of IAH1 in diverse cancers. (H–M) Disease-free survival (DFS) analysis of IAH1 in multifarious neoplasm. Data were originated from TGCA database, examined with the GEPIA tool. Red identifiers signify positive connections, whereas blue denotes signify negative connections. (N-Q)The examination of the bond between IAH1 expression and OS, DSS, DFI, PFI in pan-cancer.

Further analysis revealed that high IAH1 expression was linked to poor prognosis in eight cancer types: GBMLGG, LGG, TARGET-LAML, LIHC, BLCA, UVM, LAML, and ACC, whereas low expression was associated with unfavorable outcomes in OV (Fig. 5N). Using established methods, we also evaluated the relationship between IAH1 expression and disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Elevated IAH1 expression correlated with reduced DSS in GBMLGG, LGG, SKCM-P, BLCA, and UVM, but with improved DSS in OV (Fig. 5O). High IAH1 expression was also associated with shorter DFI in PRAD, ESCA, PAAD, and ACC (Fig. 5P), and decreased PFI in GBMLGG, LGG, CESC, ESCA, KIPAN, PRAD, HNSC, GBM, UVM, and ACC, though it predicted longer PFI in OV (Fig. 5Q).

3.3. IAH1 exhibits strong associations with immune cell infiltration

We computed immunocyte infiltration scores for 22 immune cell types across 10,180 tumor samples spanning 44 cancer types. These included GBM, GBMLGG, LGG, UCEC, LAML, BRCA, CESC, LUAD, ESCA, STES, SARC, KIRP, KIPAN, COAD, COADREAD, PRAD, STAD, HNSC, KIRC, LUSC, THYM, LIHC, SKCM-P, SKCM, BLCA, SKCM-M, THCA, NB, MESO, READ, OV, UVM, PAAD, TGCT, ALL, PCPG, ACC, ALL-R, DLBC, KICH, and CHOL. A pronounced correlation was observed between IAH1 expression and immune infiltration levels (Fig. 6A).

Fig. 6.

Fig. 6

Fig. 6

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The interrelationship between IAH1 expression and immune penetration. (A) Association of IAH1 expression with diverse immune cell infiltration across various cancers, as indicated by infiltration scores depending on multiple immune cell populations. (B) Interdependence of IAH1 expression and StromalScore in distinct malignancies. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.

Subsequent analysis revealed a significant positive correlation in 11 cancer types: GBM, GBMLGG, LGG, TARGET-LAML, STAD, BLCA, UVM, PAAD, TGCT, LAML, and KICH. Conversely, a notable negative correlation was identified in 13 others: UCEC, BRCA, CESC, SARC, KIRP, KIRC, LUSC, THYM, THCA, TARGET-NB, MESO, OV, and PCPG (Fig. 6B).

3.4. The expression levels of IAH1 exhibited significant correlations with immunomodulatory and tumor stemness

The results revealed a significant association between IAH1 expression and immune checkpoints (Fig. 7A). Notably, IAH1 levels were positively correlated with most immunomodulatory genes examined (Fig. 7B). Furthermore, a substantial correlation was observed between IAH1 and tumor stemness scores in 14 cancer types. Among these, GBMLGG, LGG, HNSC, and UVM showed a strong positive correlation, whereas BRCA, KIRP, KIPAN, THYM, LIHC, THCA, MESO, TGCT, BLCA, and CHOL exhibited a significant negative correlation (Fig. 7C).

Fig. 7.

Fig. 7

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Correlation analysis of IAH1 with immune regulatory genes, immunological checkpoints, and tumor stemness score. (A) The association between IAH1 and recognized immunological checkpoints in all TCGA malignancies. (B) IAH1 expression correlates with major immune regulatory genes. (C) The IAH1 tumor stemness score.

3.5. Drug sensitivity and immunotherapeutic analysis of IAH1

Enhancing therapeutic responsiveness is critical for overcoming drug resistance in cancer. Our results demonstrated a strong positive association between IAH1 expression and sensitivity to asparaginase and GSK-2126458, and a negative correlation with response to Nilotinib and Imatinib (Fig. 8A–D). However, no significant difference in IC50 values for these agents was observed between high- and low-risk patient groups (Fig. 8E–H). These findings suggest that IAH1 may influence resistance to certain FDA-approved chemotherapeutics and underscore its complex role in drug susceptibility across cancers, highlighting its potential as a target for immunotherapy.

Fig. 8.

Fig. 8

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Chemosensitivity analysis of IAH1. (A–D) Expression of IAH1 and responsiveness to Asparaginase, GSK-2126458, Nilotinib, Imatinib. (E–H) Violin plots for analysis of differences in IC50 of chemotherapeutic agents between high-risk and low-risk groups.

To evaluate IAH1 as a candidate for immunotherapy, we analyzed overall survival in patients with high or low IAH1 expression receiving immune checkpoint blockade (ICB), including anti-PD-1, anti-PD-L1, or anti-CTLA4 agents. In the absence of ICB, and in contexts involving any anti-PD-1 therapy, Pembrolizumab alone, or Atezolizumab alone, high IAH1 expression was associated with significantly better survival (Fig. 9A, B, D, F). Treatment with Nivolumab alone also showed a survival advantage for the high-expression group, though this was not statistically significant (Fig. 9C). Under anti-PD-L1 regimens, a pronounced survival decline occurred after 40 months in the high-expression group, eventually falling below that of the low-expression group (Fig. 9E). Conversely, low IAH1 expression was correlated with superior survival in patients receiving any anti-CTLA4, Ipilimumab alone, any anti-PD-1 plus anti-CTLA4, or Pembrolizumab plus Ipilimumab, with all differences being statistically significant (Fig. 9G–J). Although low expression was associated with improved survival in patients treated with Nivolumab plus Ipilimumab, this result was not significant (Fig. 9K).

Fig. 9.

Fig. 9

Fig. 9

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Detection of associations between IAH1expression and ICB treatments or Cytokine treatments in cancer. (A) Overall survival without any immunotherapy. (B) Overall survival using all anti-PD-1. (C) Overall survival using Nivolumab only (anti-PD-1). (D) Overall survival using all Pembrolizumab only (anti-PD-1). (E) Overall survival using all anti-PD-L1. (F) Overall survival using Atezolizumab only (anti-PD-L1). (G) Overall survival using all anti-CTLA-4. (H) Overall survival using Ipilimumab only (anti-CTLA-4). (I) Overall survival using all anti-PD-1 and all anti-CTLA-4. (J) Overall survival using Pembrolizumab (anti-PD-1) and Ipilimumab (anti-CTLA-4). (K) Overall survival using Nivolumab (anti-PD-1) and Ipilimumab (anti-CTLA-4). (L) Compare IAH1 gene expression levels across different tumor models and ICB treatments, between pre- and post-ICB treatment and responders and non-responders. (M) Compare IAH1 gene expression levels across cell-lines between pre- and post-cytokine treated samples.

We further correlated IAH1 expression with ICB response using the TISMO database, which includes data from models treated with anti-PD1, anti-PDL1, anti-PDL2, and anti-CTLA4. In the 4T1_GSE130472_old_antiPDL1 model (n = 15), non-responders showed reduced IAH1 expression, whereas responders in T11_GSE124821_Apobec_day7_antiCTLA4&antiPD1 (n = 8) and YTN16_GSE146027_day21_antiCTLA4 (n = 10) exhibited elevated IAH1. No significant differences were observed in other ICB contexts (Fig. 9L).

Additionally, we assessed IAH1 expression changes following cytokine treatment (IFNγ, IFNβ, TNFα, TGFβ1). Reduced IAH1 levels were observed after IFNβ treatment in 4T1_RTM28723893 (n = 12), B16_GSE110708 (n = 6), MC38_RTM28723893 (n = 48), and Panc02_RTM28723893 (n = 12). Decreases were also noted under IFNγ exposure in 4T1_RTM28723893 (n = 12), B16_SSG33589424 (n = 16), B16_GSE106390 (n = 6), MC38_RTM28723893 (n = 48), MOC2_RU31562203 (n = 7), Panc02_RTM28723893 (n = 12), and Renca_RTM28723893 (n = 11). Lower IAH1 expression was seen with TNFα only in Panc02_RTM28723893 (n = 12), while TGFβ1 treatment showed no effect (Fig. 9M).

3.6. IAH1 mutation characteristics and cell function in pan-cancer cohort

Analysis revealed that Missense_Mutation was the most frequent mutation type, consistent with cBioPortal findings (Fig. 10A–D). To better understand tumorigenesis, we examined mutations, structural variations, amplifications, and deep deletions in key tumor-related genes [38]. Using cBioPortal's TCGA tumor cohort, we found that IAH1 alterations were primarily amplifications in endometrial, bladder, ovarian epithelial, and hepatobiliary cancers, and deep deletions in mature B-cell neoplasms (Fig. 10B). According to TIMER, UCEC, ESCA, and SKCM showed the highest IAH1 mutation rates, with ratios of 7/531, 2/185, and 4/468, respectively (Fig. 10C).

Fig. 10.

Fig. 10

Fig. 10

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Genomic profiling of numerous cancerous conditions associated with IAH1 expression. (A) Genetic mutation landscape of IAH1. (B) Mutations, amplifications, deep deletions and multiple alterations of IAH1 in 30 cancers from the TCGA database investigated by cBioPortal. Tumors with high amplification levels are labeled red. Green labels reveal cancers characterized by maximal mutation frequencies, whereas blue labels indicate neoplasms exhibiting the most profound deletions. Gray signifies tumors with multiple alterations. (C) Mutation rate of IAH1 gene in different tumors analyzed by TIMER portal. (D) Mutation sites of IAH1 gene in multiple tumors analyzed by cBioPortal tool. (E) Correlation between IAH1 expression and tumor mutation burden in pan-cancer. (F) Correlation between IAH1 expression and microsatellite instability in pan-cancer. (G) Correlation between IAH1 expression and tumor purity in pan-cancer. (H) Expression of IAH1 gene and 44 marker genes of class III RNA-modified genes (m1A(10), m5C(13), m6A(21)) in each sample.

To further elucidate the role of IAH1 in cancer, we assessed its relationship with genomic heterogeneity. Analysis across 37 tumors revealed a significant positive correlation between IAH1 expression and TMB in KIPAN, STAD, KIRC, and BLCA, and a negative correlation in CHOL and DLBC (Fig. 10E). MSI was positively correlated with IAH1 in BRCA, STAD, and LIHC, but negatively correlated in eight cancers including GBMLGG, LUAD, COAD, COADREAD, KIPAN, HNSC, ACC, and KICH (Fig. 10F). Additionally, tumor purity was positively associated with IAH1 expression in 16 cancer types—such as CESC, LUAD, BRCA, ESCA, STES, SARC, KIRP, STAD, PRAD, HNSC, KIRC, LUSC, THCA, PCPG, SKCM, and CHOL—and negatively correlated in four (THYM, PAAD, BLCA, KICH) (Fig. 10G).

RNA modification analysis indicated that IAH1 was associated with m1A (10 genes), m5C (13 genes), and m6A (21 genes) regulators (Fig. 10H). These findings enhance our understanding of the molecular landscape of cancer and suggest potential therapeutic strategies.

Subsequently, using single-cell sequencing data from CancerSEA, we evaluated the relationship between IAH1 and 14 functional states in cancer. IAH1 expression was linked to angiogenesis, apoptosis, DNA damage, epithelial-mesenchymal transition (EMT), invasion, and other processes (Fig. 11A). For example, in RD cells, IAH1 correlated positively with differentiation, angiogenesis, and inflammation, but negatively with DNA repair, DNA damage, and the cell cycle (Fig. 11B). T-SNE visualization illustrated the distribution and expression levels of IAH1 in RB cells (Fig. 11C).

Fig. 11.

Fig. 11

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IAH1 gene is associated with the functional status of different cancer cells. (A) Correlation between IAH1 gene and functional status of various cancers. (B) The expression of IAH1correlates with the functional status of RB cells.(C) T-SNE describes the distribution of cells, every point represents a single cell, and the color of the point represents the expression level of IAH1 gene in the RB cell.

3.7. Inquiry into IAH1-affiliated genes and pathways

To investigate the role of IAH1 in tumorigenesis, we performed an integrated analysis of IAH1-associated genes and proteins, followed by pathway enrichment studies. First, we identified 50 IAH1-interacting proteins using the STRING database (Fig. 12A; Supplementary Table 1). We then identified the top 100 genes correlated with IAH1 expression using GEPIA2 (Supplementary Table 2). The ten most strongly associated genes showed consistently positive correlations with IAH1 across multiple cancer types (Fig. 12B). Subsequent KEGG and GO enrichment analyses were performed on these gene sets. KEGG pathway analysis indicated that IAH1 may facilitate tumorigenesis through several cancer-related pathways (Fig. 12C, top 10 shown). GO analysis revealed that IAH1-linked genes are primarily involved in specific biological processes and molecular functions (Fig. 12D). Together, these results offer insight into the potential mechanisms by which IAH1 promotes carcinogenesis and its role in various biological functions.

Fig. 12.

Fig. 12

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IAH1-related gene enrichment analysis. (A) IAH1-interacted proteins. (B) The expression of the top 10 IAH1-related target genes in carcinoma. (C) KEGG pathway analysis involved in IAH1-interacted and collaborated genes. (D) GO analysis of genes that interacted and engaged with IAH1.

3.8. Validation of cell lines and analysis of IAH1 expression

To investigate the expression pattern of IAH1 in tumorigenesis, we selected normal epithelial cell lines and classical cancer cell lines derived from three tissue origins (breast, lung, and kidney) for qPCR analysis. Results showed that the expression level of the IAH1 gene was significantly upregulated in cancer cell lines compared to their corresponding normal counterparts, with statistical significance (P < 0.05, Fig. 13). This finding suggests that IAH1 may serve as a shared molecular marker across multiple cancer types originating from these tissues.

Fig. 13.

Fig. 13

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Expression of IAH1 in cell lines. (A–C) Comparison of IAH1 expression between normal epithelial cell lines and cancer cell lines derived from breast, lung, and kidney tissues.

4. Discussion

In light of the intricate and multifaceted nature of cancer, the pursuit of novel molecular targets and therapeutic approaches is essential. IAH1 possesses esterase activity and is extensively expressed across a myriad of tissues and organs throughout the body. There existed a paucity of research exploring the association between IAH1 and cancer. This study marked the inaugural extensive exploration utilizing bioinformatics analysis to probe the role of IAH1 across multiple tumors. The objective was to methodically evaluate the expression of IAH1 in diverse cancer tissues and to shed light on its biological functions, immune attributes, and prognostic relevance in a pan-cancer context, offering valuable clinical insights and crucial guidance.

Our study has revealed that IAH1 is implicated in the majority of tumors, which is consistent with previous reports [12,13,[17], [18], [19]], which underscored the significance of IAH1 in various tumors. It is noteworthy that the expression level of IAH1 exhibited a significant correlation with patient survival across various tumor types. In particular, IAH1 expression was notably associated with the tumor staging of BLAC, KIRC, LUAD, which suggested IAH1 could serve as a valuable metric in evaluating the stage of these cancers. Prognostic analysis further reveals that elevated IAH1 expression was markedly linked to a worse prognosis in most tumors, implying its potential as a prognostic biomarker for multiple tumor types.

Immunological evaluations had uncovered the involvement of the IAH1 protein in widespread cancer immune cell infiltration like B_cells_naive, T_cells_CD8, NK_cells, Monocytes, Eosinophils, Neutrophils etc., suggesting that IAH1 may be essential in the infiltration of tumor-related immune cells. Further in-depth research had revealed a close association between IAH1 and immune regulatory genes, tumor stem cells, and immune checkpoints, highlighting the importance of PPFIA3 as a potential target for cancer immunotherapy. Functional cellular analyses had elucidated the IAH1 protein's significant roles in angiogenesis, apoptosis, cellular differentiation, EMT, hypoxia, inflammation, and tumor metastasis. The strong association between IAH1 and EMT suggests a role in facilitating metastasis. IAH1 likely contributes to cytoskeletal remodeling, loss of cell adhesion, and acquisition of migratory capabilities—hallmarks of aggressive disease. Its involvement in hypoxia and angiogenesis further supports its role in creating a permissive environment for metastatic spread. These findings underscored the multifaceted functions of the lipid metabolism-associated gene——IAH1, in both non-cancerous and cancerous tissues. The more profound implication lied in its potential contribution to the formation of the tumor microenvironment in cancerous states, thereby fostering the initiation, progression, and metastatic dissemination of cancer.

Immunotherapy signifies a monumental advancement in cancer therapeutics, rekindling hope for numerous patients [38]. Nevertheless, this promising treatment modality is confronted with challenges, such as heterogeneous treatment efficacy and potential adverse effects, necessitating perpetual research and innovative endeavors to harness its full potential in combating cancer [38]. Unraveling the part played by IAH1 in cancer might provide deep perceptions into the malady's intrinsic processes and enhance IAH1's status as a crucial indicator for cancer identification and intervention. The results conveyed that the delivery of PD-1 or PD-L1 blocking agents as monotherapies tended to benefit patients with higher IAH1 expression, leading to enhanced survival rates. Conversely, the use of anti-CTLA-4, either as a standalone treatment or in combination with an anti-PD-1 agent, appeared to be more advantageous for patients with lower IAH1 expression, providing them with a comparatively greater chance of survival. These observations had significant implications for personalized medicine, implying that the quantification of IAH1 expression levels in patients allowed healthcare practitioners to make more precise and informed decisions regarding immunotherapy options, thus possibly increasing patient longevity and optimizing therapy effectiveness By scrutinizing the IAH1 gene expression levels in those undergoing ICB or cytokine medical intervention, we unraveled the potential interplay with immunotherapy potency. Moreover, this research could potentially uncover IAH1 as a signpost—a potential biological marker—for predicting treatment reactions. This could improve our comprehension of the role of IAH1 in cancer management and provide invaluable insights for developing personalized treatment plans.

The drug sensitivity analysis revealed correlations between IAH1 and different chemotherapy drugs, suggesting that it may impact the efficacy of chemotherapeutic agents. Moving forward, assessing IAH1 levels could be instrumental in determining the optimal choice of chemotherapy drugs for various tumors.

5. Conclusion

In conclusion, this study elucidated the multifaceted influence of IAH1 on clinical outcomes, immune infiltration profiles, immunoregulatory processes, genetic mutations, drug susceptibility etc. Across pan-cancers.

However, our study faced several limitations. Primarily, additional foundational experimental research was necessitated to elucidate the biological processes and molecular mechanisms through which IAH1 influenced tumor progression. Secondly, the drugs that had been screened for their association with pan-cancer and IAH1 necessitated further experimental validation. A more comprehensive analysis of the relationship between IAH1 and the tumor microenvironment in cancer is urgently required.

CRediT authorship contribution statement

Chuanchuan Sun: Formal analysis, Data curation, Conceptualization. Xianghong Wang: Software, Methodology, Investigation. Yeye Yu: Writing – original draft, Visualization, Validation. Heng Shi: Writing – original draft, Visualization. Fanna Liu: Writing – review & editing, Validation, Supervision. Shiping Zhu: Project administration, Investigation, Funding acquisition. Shengyun Sun: Writing – review & editing, Validation, Supervision, Funding acquisition.

Data source indication

The collections of data that support this inquiry are downloadable from internet archives. For the precise repository or repositories and its accession number(s), please see the main text or Supplementary Section.

Disclosure of potential interest conflicts

The researchers affirm that the study was conducted free from any business or monetary ties that might be seen as a possible interest conflict.

Funding

This work was supported by the Guangdong Provincial Administration of Traditional Chinese Medicine (No. 20232026), the Guangzhou Municipal Science and Technology Program (No. 2024A03J0832), and the Fundamental Research Funds for the Central Universities (No. 21625405).

Declaration of competing interest

The authors declare that the research was carried out without any commercial or financial relationships that could be perceived as a potential conflict of interest.

Tumor abbreviation

Cohort Full name

TCGA-ACC

Adrenocortical carcinoma

TCGA-BLCA

Bladder Urothelial Carcinoma

TCGA-BRCA

Breast invasive carcinoma

TCGA-CESC

Cervical squamous cell carcinoma and endocervical adenocarcinoma

TCGA-CHOL

Cholangiocarcinoma

TCGA-COAD

Colon adenocarcinoma

TCGA-COADREAD

Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma

TCGA-DLBC

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

TCGA-ESCA

Esophageal carcinoma

TCGA-GBM

Glioblastoma multiforme

TCGA-GBMLGG

Glioma

TCGA-HNSC

Head and Neck squamous cell carcinoma

TCGA-KICH

Kidney Chromophobe

TCGA-KIPAN

Pan-kidney cohort (KICH + KIRC + KIRP)

TCGA-KIRC

Kidney renal clear cell carcinoma

TCGA-KIRP

Kidney renal papillary cell carcinoma

TCGA-LAML

Acute Myeloid Leukemia

TCGA-LGG

Brain Lower Grade Glioma

TCGA-LIHC

Liver hepatocellular carcinoma

TCGA-LUAD

Lung adenocarcinoma

TCGA-LUSC

Lung squamous cell carcinoma

TCGA-MESO

Mesothelioma

TCGA-OV

Ovarian serous cystadenocarcinoma

TCGA-PAAD

Pancreatic adenocarcinoma

TCGA-PCPG

Pheochromocytoma and Paraganglioma

TCGA-PRAD

Prostate adenocarcinoma

TCGA-READ

Rectum adenocarcinoma

TCGA-SARC

Sarcoma

TCGA-STAD

Stomach adenocarcinoma

TCGA-SKCM

Skin Cutaneous Melanoma

TCGA-STES

Stomach and Esophageal carcinoma

TCGA-TGCT

Testicular Germ Cell Tumors

TCGA-THCA

Thyroid carcinoma

TCGA-THYM

Thymoma

TCGA-UCEC

Uterine Corpus Endometrial Carcinoma

TCGA-UCS

Uterine Carcinosarcoma

TCGA-UVM

Uveal Melanoma

TARGET-ALL

Acute Lymphoblastic Leukemia

TARGET-NB

Neuroblastoma

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbrep.2025.102287.

Contributor Information

Shiping Zhu, Email: zhushiping@jnu.edu.cn.

Shengyun Sun, Email: shengyunsun2020@163.com.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (16.8KB, docx)

Data availability

Data will be made available on request.

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Supplementary Materials

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Data Availability Statement

Data will be made available on request.

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