Exploring CISD1 as a multifaceted biomarker in cancer: Implications for diagnosis, prognosis, and immunotherapeutic response

Abstract

CISD1, an outer mitochondrial membrane iron-sulfur cluster protein, regulates intracellular iron levels, oxidative stress, and mitochondrial dynamics, playing critical roles in cellular bioenergetics and redox homeostasis. Although CISD1 has been identified as a prognostic biomarker in specific cancers, its broader implications in tumorigenesis, cancer progression, and immunotherapy remain unclear. Given the heterogeneity of cancer and the need for robust biomarkers across cancers, this study conducts the first comprehensive pan-cancer analysis of CISD1 by evaluating its roles in cancer and treatment. We obtained and analyzed data from databases including TCGA, GTEx, THPA, GEPIA2.0, SangerBox, cBioPortal, TIMER2.0, CAMOIP, DAVID, SRPLOT, and TISIDB. Our findings reveal significant alterations in CISD1 expression at both transcriptional and translational levels, as well as gene mutations across multiple cancers, indicating its potential as a diagnostic biomarker and its involvement in cancer development and progression. CISD1 dysregulation is linked to poor clinical outcomes, as shown through its impact on patient prognosis. GO and KEGG analyses show that CISD1 plays critical roles in cellular bioenergetics. Notably, CISD1 expression is significantly correlated with tumor stemness indices, tumor mutation burden, microsatellite instability, and immune checkpoint proteins in multiple cancers, and altered CISD1 levels are also observed in patients responding to immunotherapy, further supporting its role not only in prognosis but also as a key predictor in immunotherapy responses and outcomes. Our findings demonstrate CISD1 as a reliable and promising diagnostic, prognostic, and immunotherapeutic biomarker for multiple cancers, emphasizing its crucial role in cancer biology and potential to guide personalized cancer therapies.

Keywords: CISD1; Diagnostic biomarker; Immunotherapeutic biomarker; MitoNEET; Pan-cancer analysis; Prognostic biomarker.

Figure 1

The mRNA expression levels of CISD1 in human pan-cancer. (A) CISD1 mRNA expression in various human normal tissues. Genotype Tissue Expression (GTEx) data for CISD1 mRNA in human normal tissues were downloaded from The Human Protein Atlas (THPA) and analyzed using GraphPad Prism. (B) CISD1 mRNA expression in various human cancer tissues. The Cancer Genome Atlas (TCGA) data for CISD1 mRNA expression across different cancer types were retrieved from THPA and analyzed using GraphPad Prism. (C) CISD1 mRNA expression levels in tumor tissues were compared with normal tissues across 33 cancer types. Differential expression analysis was conducted using GEPIA2, with a significance threshold of Q < 0.05 (Benjamini-Hochberg correction for multiple testing). Cancer types with significantly increased CISD1 expression are indicated in brown font, while those with significantly decreased expression are in blue font. N = normal tissue, and T = tumor tissue.

Figure 2

Protein expression levels of CISD1 in human pan-cancer. Immunohistochemical staining images show CISD1 protein expression in various normal human tissues and corresponding cancer tissues. The data for these images were downloaded from The Human Protein Atlas (THPA). For each type of tissue, the normal tissue is displayed on the left, and the corresponding cancer tissue is on the right. CISD1 protein was stained brown.

Figure 3

Distinct genomic profile analysis of CISD1 in various cancers. (A) Alteration frequency of CISD1 across various cancer types. The data were obtained from the cBio Cancer Genomics Portal (cBioPortal) database, showing the percentage of samples with mutations, structural variants, amplifications, and deep deletions in CISD1 across different cancer types. (B) Mutation frequency of CISD1 in different cancer types. The data from Tumor Immune Estimation Resource 2.0 (TIMER2.0) reveals the percentages of mutations of CISD1 in various cancers. (C) Mutation types and distribution in CISD1 across cancers. The data was analyzed using the SangerBox portal, showing the percentages of missense mutations, nonsense mutations, and splice sites across cancer types. (D) Protein domain analysis of CISD1 mutations. The mutation map generated from cBioPortal shows mutation types in the MitoNEET_N and zf-CDGSH domains. (E) mRNA expression analysis of CISD1 alterations in different cancer types. The results were obtained from SangerBox comparing neutral, gain, and loss alterations in various cancers. (F) The relationship between CISD1 expression and somatic mutations in BRCA (top) and LGG (bottom) cancers. The data from Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer (CAMOIP) show the percentages of oncogene, tumor suppressor gene, and unknown gene types, and show the numbers of different mutation types such as splice site, missense, frameshift, inframe insertion/deletion, and nonsense mutations in the CISD1-high and CISD1-low groups.

Figure 4

Correlation analysis between CISD1 expression and overall survival in different cancers. (A) The forest plot illustrates the hazard ratios (HR) of CISD1 expression in various cancers. The data were obtained from SangerBox and showed the correlation between CISD1 expression and overall survival across multiple cancer types. A p-value less than 0.05 is considered significant. (B) Survival map and Kaplan–Meier survival curves for overall survival in various cancer types. The results were generated using GEPIA2.0 online tools. The survival heatmap above represents log10(HR) values, with red indicating higher HR and blue indicating lower HR; cancer types highlighted with a red box on the heatmap indicate statistical significance, with a p-value less than 0.05. Blue and red lines in the Kaplan–Meier plots represent patients with low and high CISD1 expression, respectively. Log-rank p-values and hazard ratios (HR) are displayed for each plot.

Figure 5

The correlation analysis between CISD1 and cancer stemness or RNA modifications. (A–F) The radar plots display the correlation between CISD1 expression and various cancer stemness scores in different cancers. The data were downloaded from SangerBox. The following stemness indices are presented: DNAss (A), EREG-METHss (B), DMPss (C), ENHss (D), RNAss (E), and EREG.EXPss (F). Each plot shows the correlation coefficient, with cancer types labeled around the plot perimeter. Cancer types with significant correlations (p < 0.05) are highlighted in red. The blue line represents the correlation values, with a negative correlation coefficient shown inside the plot. (G) The heatmap illustrates the correlation between CISD1 expression and various RNA modification-related genes (writers, readers, and erasers) across different cancer types. The data were presented as correlation coefficients, with colors ranging from red (positive correlation) to blue (negative correlation). The types of RNA modifications (m1A, m5C, and m6A) are indicated by the colored bars on the right. The p-values for each correlation are shown on the heatmap, with significant correlations highlighted by asterisks, and dark green means no significant correlation.

Figure 6

CISD1 coexpression gene enrichment analysis in various tumors. (A) A Venn diagram was generated from SangerBox showing the overlap of CISD1 coexpression genes in five different tumor types: BRCA (green), THYM (orange), LUAD (yellow), LIHC (red), and KICH (blue). The numbers inside the diagram indicate the count of common and unique genes coexpressed with CISD1 across these tumors. (B) A heatmap was generated from Gene Expression Profiling Interactive Analysis 2.0 (GEPIA2.0), representing the expression levels of the 26 CISD1 coexpressed genes across various cancers. The color gradient from light to dark blue indicates increasing levels of gene expression. (C) A Gene Ontology (GO) Chord plot was generated from Science and Research Online Plot (SRPLOT), displaying the enrichment of the 26 CISD1 coexpressed genes. Biological process pathways are highlighted with colored bands, with the width corresponding to the number of enriched genes. (D–G) The bubble plots were generated from SRPLOT, representing the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the 26 CISD1 coexpressed genes in tumors. (D) Biological processes related to CISD1 coexpressed genes. (E) Cellular components associated with these genes. (F) Molecular functions of CISD1 coexpressed genes. (G) KEGG pathway analysis shows significant enrichment. Each bubble's size corresponds to the number of genes involved, while color intensity indicates the significance level (P).

Figure 7

Correlation analysis between CISD1 and immune infiltration in multiple cancers. (A–C) The radar plots depict the correlation between CISD1 expression and various immune-related scores, including the StromalScore (A), ImmuneScore (B), and ESTIMATEScore (C) across different cancers. These analyses were performed using SangerBox. Each plot shows the correlation coefficients, with cancer types labeled around the perimeter. Significant correlations (p < 0.05) are marked in red. The red lines indicate the correlation levels, and the values of negative correlation coefficients are shown inside the plot. (D) The heatmap generated from SangerBox displays the correlation coefficients between CISD1 expression and the infiltration levels of various immune cell types, including B cells, T cells (CD4 and CD8), neutrophils, macrophages, and dendritic cells (DC), across multiple cancer types. Positive correlations are indicated in red, and negative correlations are in blue. The color intensity corresponds to the strength of the correlation, and significant correlations (p < 0.05) are indicated by asterisks, and dark green means no significant correlation.

Figure 8

Correlation analysis between CISD1 and immune checkpoint blockade proteins. (A–C) The radar plots generated from SangerBox illustrate the correlation between CISD1 expression and tumor mutation burden (TMB) (A), microsatellite instability (MSI) (B), and neoantigen burden (NEO) (C) across various cancers. The correlation coefficients are shown, with cancer types labeled around the plot perimeter. Significant correlations (p < 0.05) are highlighted in red, and the blue lines indicate the correlation values. Negative correlation coefficients are presented inside the plots. (D) The heatmap generated from SangerBox shows the correlation between CISD1 expression and various immune checkpoint blockade-related genes, including PD1, PD-L1, and CTLA4, across multiple cancer types. The color gradient represents correlation coefficients, with red indicating positive correlations and blue indicating negative correlations. Significant correlations are marked by stars (∗), and dark green means no significant. (E) The scatter plot generated from TISIDB shows the log₂ fold change between responders and non-responders to immune checkpoint blockade therapy (e.g., PD-1, PD-L1) in various cancer types. The position on the X-axis represents the fold change, while the Y-axis shows the moderated t-test p-values. (F) CISD1 is a diagnostic, prognostic, and immunotherapeutic biomarker in multiple cancers. This picture simply summarizes the evidence that CISD1 is a reliable and promising biomarker. CISD1 plays a critical role in cellular energetics; it participates in many biological processes related to energy and metabolism. Its expression levels are elevated in multiple cancers, and it undergoes genetic alterations in various cancers, which enable it to aid in cancer early diagnosis. Patients with increased expression levels of CISD1 have relatively lower survival rates; moreover, its positive correlation with tumor stemness indices and RNA modifications indicates that it can be used to predict prognosis. It is positively correlated with tumor infiltration and immune checkpoint genes, and it exhibits higher expression levels in patients who respond to tumor immunotherapy, suggesting that it can be used to predict the outcome of cancer immunotherapy.

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