Acid-Base Homeostasis and its Implications on Cancer Phenotypic Behaviors

Oncology Biophysics Life Sciences Medicine Cell Biology Bioengineering Biochemistry
acid-base homeostasis cancer microenvironment metabolic reprogramming Fenton reaction iron metabolism

Cancer is a major global public health concern, with its complex pathological processes and diverse manifestations being a focus of research. Many studies have shown that acid-base imbalance plays a crucial role in the occurrence and development of cancer, but the underlying mechanisms are not fully understood. In this study titled “Acid–base homeostasis and implications to the phenotypic behaviors of cancer,” the authors explore the relationship between acid-base balance and cancer phenotypic behaviors from a systems biology perspective.

Research Background

Acid-base homeostasis is an essential characteristic for all living cells to maintain normal physiological functions. Long-term imbalance in intracellular pH (phi) and extracellular pH (phe) can lead to various diseases. In this study, the authors aim to explain, through computational models and transcriptome data analysis, why cancer tissue cells tend to have an alkaline intracellular pH while the extracellular environment is acidic. Additionally, the authors seek to challenge the current mainstream views on the roles of Na+/H+ exchangers, lactate exporters, and carbonic anhydrases in causing intracellular alkalinization and extracellular acidification in cancer.

Paper Source

This research was conducted by Yi Zhou, Wennan Chang, Xiaoyu Lu, Jin Wang, Chi Zhang, and Ying Xu. The institutions involved include China-Japan Union Hospital of Jilin University, University of Georgia, Indiana University School of Medicine, Purdue University, Northwestern University, and Stony Brook University. The article was published in “Genomics, Proteomics & Bioinformatics,” Volume 21, pages 1133 to 1148, in 2023.

Research Process

Research Flow

The authors conducted the study through the following steps:

  1. Data Collection and Processing: Transcriptome data from 4,750 human cancer tissue samples covering 9 cancer types were obtained from The Cancer Genome Atlas (TCGA) database, along with 503 corresponding control samples. Results were validated using single-cell RNA sequencing data.

  2. Computational Model Development: Based on previous research, a computational model of cellular Fenton reactions was established to predict the average production rate of OH- from oxidation-reduction reactions, which continuously perturb intracellular pH homeostasis.

  3. Correlation Analysis: Linear regression analysis was used to explore the relationship between Fenton reaction levels and reprogrammed metabolisms (RMs), investigating how these RMs maintain pH stability by producing H+.

  4. Phenotypic Behavior Analysis: The phenotypic behaviors induced by Fenton reactions and RMs in different cancer types were analyzed, including cancer growth rates, metastasis rates, and immune cell composition.

Experimental Details

  1. Samples and Analysis Tools: RNA sequencing data from TCGA and single-cell RNA sequencing data were utilized. Single-sample Gene Set Enrichment Analysis (ssGSEA) was employed to assess the expression levels of different reprogrammed metabolisms in cancer.

  2. Fenton Reaction Estimation: Key reactants for estimating Fenton reaction levels included hydrogen peroxide (H2O2), superoxide (O2-), and Fe2+, along with their reaction relationships.

  3. Algorithms and Tools: The graph neural network algorithm (scFEA) was used to calculate metabolic flux levels for each sample to estimate Fenton reaction rates.

  4. Regression Analysis and Model Construction: Linear regression analysis and L1 regularization methods were used to establish association models between Fenton reactions and RMs, testing these models’ predictive capabilities across different cancer types.

Main Results

  1. Challenge to Intracellular Alkalinization and Acidification Hypotheses: Experimental results showed that Na+/H+ exchangers were not significantly upregulated in most cancer types, contradicting currently accepted views. Additionally, lactate exporters and carbonic anhydrases failed to explain the intracellular alkalinization phenomenon.

  2. Confirmation of Fenton Reaction Levels: Analysis of 9 cancer types revealed significantly higher cellular Fenton reaction levels in all cancer types compared to control groups. These reactions continuously produce OH-, disrupting intracellular pH stability.

  3. Identification of Reprogrammed Metabolisms: 43 RMs were identified, through which H+ produced can neutralize OH- generated by Fenton reactions, maintaining stable intracellular pH. These reprogrammed metabolisms play important roles in different cancer types.

  4. Explanation of Phenotypic Behaviors: The study showed that cellular Fenton reaction levels are closely related to cancer growth and metastasis rates. Cancer types with faster regeneration and proliferation rates tend to have higher Fenton reaction levels.

Conclusions and Significance

This study proposes a unified framework for systematically studying cancer-inducing stress, adaptive metabolic reprogramming, and cancer behaviors. The results indicate that Fenton reactions and reprogrammed metabolisms play crucial roles in cancer. This framework explains the phenomena of intracellular alkalinization and extracellular acidification that current mainstream views fail to address. Moreover, the study provides new perspectives on cancer growth and metastasis mechanisms, offering new ideas for cancer treatment and drug development.

Research Highlights

  1. Challenging Traditional Hypotheses: The study challenges the roles of Na+/H+ exchangers and lactate exporters in cellular pH regulation, providing new evidence against popular views.

  2. Unified Framework: A unified framework for cancer phenotypic behaviors was established through Fenton reactions and reprogrammed metabolisms, answering many unresolved questions.

  3. Diversity Analysis: Covering 9 different cancer types, the study provides extensive data support and validation, ensuring the reliability of research results.

  4. Computational Method Innovation: Advanced graph neural network algorithms were used to accurately estimate metabolic fluxes, enhancing the precision and reliability of the research.

This study not only provides a new perspective for understanding the biological mechanisms of cancer but also brings new hope for future anti-cancer research and treatment strategies.