CST2 Expression in Human Pan-Cancer: Clinical and Research Insights

Materials and Methods for CST2 Expression Analysis

A comprehensive evaluation of CST2 expression in pan-cancer involves the integration of large-scale genomic datasets from several well-established repositories. In our analysis, data were primarily obtained from the following sources:

• Normal Tissue Data: Expression data in 35 different normal tissues were downloaded from the Genotype-Tissue Expression (GTEx) database. This resource provides robust transcriptomic profiles from healthy individuals, which serve as a baseline reference.
• Tumor Cell Line Data: CST2 expression for 31 tumor cell lines was sourced from the Cancer Cell Line Encyclopedia (CCLE). Cell line data allow researchers to assess CST2 behavior in controlled in vitro models.
• Cancer Tissue Data: Tumor expression data were retrieved from The Cancer Genome Atlas (TCGA). By combining the genomic data available from TCGA with normal tissue data from GTEx, researchers can analyze the differential expression of CST2. In our work, data from 15 different cancer types were extracted to examine not only the overall expression differences between cancer and normal tissues but also to investigate changes across clinical stages.

Data integration involves preprocessing, normalization, and statistical analysis using tools such as R. Differential expression is typically analyzed through comparison of tumor versus normal expression levels. Furthermore, the effects of CST2 expression on specific clinical stages are evaluated to understand its role in cancer progression.

Below is a summary table of the main datasets and sources used in CST2 expression analysis:

Single-Cell Analysis and Data Integration in Pan-Cancer

Recent advances in single-cell RNA sequencing (scRNA-seq) have revolutionized the way gene expression is analyzed in complex tissues. To further delineate the cellular distribution of CST2 within tumors, researchers have utilized single-cell datasets from the Single-cell and Spatially-resolved Cancer Resources (SCAR) database. This platform provides high-dimensional single-cell data across multiple tumor types, including:

• BRCA (Breast Cancer)
• BTCC (Bladder Transitional Cell Carcinoma)
• CAC (Colon Adenocarcinoma)
• CCRCC (Clear Cell Renal Cell Carcinoma)
• CRPC (Castration-Resistant Prostate Cancer)
• ESCC (Esophageal Squamous Cell Carcinoma)
• NSCLC (Non-Small Cell Lung Cancer)
• OV (Ovarian Cancer)
• PDAC (Pancreatic Ductal Adenocarcinoma)
• STAD (Stomach Adenocarcinoma)
• UCEC (Uterine Corpus Endometrial Carcinoma)

Using these single-cell datasets, researchers can map the expression levels of CST2 across a diverse range of cell subpopulations within tumors. Data analysis typically involves integrating scRNA-seq profiles with spatial and clinical metadata to reveal heterogeneity in CST2 expression. The precise localization of CST2 within tumor microenvironments may highlight specific subpopulations with prognostic significance or unique functional roles in tumor biology.

Prognostic Assessment Based on CST2 Expression in Pan-Cancer

The prognostic significance of tumor biomarkers is essential for risk stratification and therapeutic decision-making. In our analysis, the prognostic value of CST2 was evaluated using several statistical methods:

• Univariate Cox Regression: Utilizing the R package “survival”, a univariate Cox regression model was constructed to assess the association between CST2 expression levels and overall patient outcomes. Prognostic endpoints include Overall Survival (OS), Progression-Free Interval (PFI), Disease Specific Survival (DSS), and Disease-Free Interval (DFI).

• Kaplan-Meier Survival Analysis: Survival curves were plotted to visually represent the relationship between different CST2 expression levels and survival outcomes. For instance, in stomach adenocarcinoma (STAD), the Kaplan-Meier curves illustrate how high versus low CST2 expression groups differ in OS, DSS, DFS, and PFS.

• Validation with External Datasets: To ensure that the prognostic effects observed are robust, data from the Gene Expression Omnibus (GEO) was used to validate the CST2 prognostic analysis in STAD. In addition, analyses of CST2 in immunotherapy-treated cohorts have been undertaken to determine if expression levels can predict immunotherapy response.

The prognostic evaluation is detailed by calculating the Hazard Ratio (HR), complete with 95% confidence intervals, and considering p-values to denote statistical significance (with p < 0.05 as the threshold). These methods provide clinicians and researchers with quantitative insights into how CST2 expression may modulate disease progression and patient survival in various cancer types.

Immunological Analyses in Pan-Cancer

While the expression of CST2 is analyzed for its direct involvement in tumor biology, it is also critical to understand its role within the broader context of the tumor immune microenvironment. Immunological analyses typically include:

• Immune Infiltration Analysis: By applying computational tools to TCGA and scRNA-seq datasets, the relationship between CST2 expression and the degree of immune cell infiltration (such as T cells, B cells, macrophages, and other lymphocytes) can be assessed.
• Correlation with Immunotherapy Outcomes: Researchers are investigating whether CST2 levels are associated with favorable or unfavorable responses to immunotherapy. This includes validation in cohorts that have received immune checkpoint inhibitors.
• Integration with Prognostic Models: The immune profile data can be integrated into prognostic models to refine risk stratification further. The interplay between CST2 expression and immune modulators may reveal novel mechanisms of tumor-immune system interactions.

By combining expression data with immunological markers, the analyses provide deeper insights into cancer pathogenesis and help identify potential avenues for therapeutic interventions targeting both tumor cells and immune cells.

Data Tables and Visual Summaries

Below is an example table summarizing the datasets utilized for CST2 expression analysis:

In addition, survival analysis plots (such as Kaplan–Meier curves) and forest plots from Cox regression are typically used as visual summaries to depict the prognostic impact of CST2 expression. Integrating these plots with detailed statistical parameters further reinforces the robustness of the analytical approach.

Conclusion

Integrative analysis of CST2 expression across normal tissues, tumor cell lines, and a variety of cancer types provides valuable insights into its role in cancer biology. By combining bulk RNA-seq data with single-cell resolution data, along with rigorous prognostic and immunological evaluations, researchers can better understand CST2’s contribution to tumor progression and patient survival. These multi-layered analyses not only enhance our biological understanding but also open new doors for the development of targeted therapies and personalized medicine approaches in oncology. Continued research in this area holds promise for improving both diagnostic and therapeutic strategies across the diverse spectrum of cancers.

FAQ

What is CST2 and why is it important in cancer research?
CST2 is a gene belonging to the cystatin family that encodes a cysteine protease inhibitor. Its differential expression in cancer tissues versus normal tissues and its association with patient prognosis make it a potential biomarker for tumor progression and therapeutic response.

Which databases are used to analyze CST2 expression in pan-cancer studies?
The primary databases include GTEx (for normal tissue expression), CCLE (for tumor cell lines), and TCGA (for clinical tumor data). Additional validation is conducted using external datasets from GEO and single-cell analyses from the SCAR database.

How is the prognostic value of CST2 assessed?
Prognostic value is evaluated using statistical methods such as univariate Cox regression and Kaplan-Meier survival analysis. These analyses provide metrics like Hazard Ratios and survival curves for endpoints such as Overall Survival, Progression-Free Interval, Disease-Specific Survival, and Disease-Free Interval.

What role does single-cell RNA sequencing play in this research?
Single-cell RNA sequencing allows researchers to map the heterogeneity of CST2 expression at the cellular level, revealing which cell subpopulations within tumors express CSTThis high-resolution data helps in understanding tumor microenvironment interactions and the localization of CST2 within different tumor compartments.

How can immunological analyses enhance our understanding of CST2 in cancer?
By examining the relationship between CST2 expression and immune cell infiltration or activity, researchers can determine whether CST2 influences the tumor immune microenvironment. This insight is critical for predicting responses to immunotherapy and developing combined therapeutic strategies.

References

1. GTEx Consortium. (n.d.). Genotype-Tissue Expression (GTEx) Project
2. Cancer Cell Line Encyclopedia (CCLE). (n.d.)
3. The Cancer Genome Atlas (TCGA). (n.d.)
4. National Center for Biotechnology Information. (n.d.). Gene Expression Omnibus (GEO)
5. Single-cell and Spatially-resolved Cancer Resources (SCAR). (n.d.)