Sex-biased molecular differences in lung adenocarcinoma are ethnic and smoking specific

Gene regulatory networks reveal sex difference in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.

METHODS

Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.

RESULTS

We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.

CONCLUSIONS

These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

Identification of the consistently differential expressed hub mRNAs and proteins in lung adenocarcinoma and construction of the prognostic signature: a multidimensional analysis

BACKGROUND

This study aimed to elucidate the consistency of differentially expressed hub mRNAs and proteins in lung adenocarcinoma (LUAD) across populations and to construct a comprehensive LUAD prognostic signature.

METHODS

The transcriptomic and proteomics data from different populations were standardized and analyzed using the same criteria to identify the consistently differential expressed mRNAs and proteins across genders and races. We then integrated prognosis-related mRNAs with clinical, pathological, and EGFR (epidermal growth factor receptor) mutation data to construct a survival model, subsequently validating it across populations. Through plasma proteomics, plasma proteins that consistently differential expressed with LUAD tissues were screened and validated, with their associations discerned by measuring expressions in tumor tissues and tumor vascular normalization.

RESULTS

The consistency rate of differentially expressed mRNAs and proteins was ~20-40%, with ethnic factors leading to about 40-60% consistency of differentially expressed mRNA or protein across populations. The survival model based on the identified eight hub mRNAs as well as stage, smoking status, and EGFR mutations, demonstrated good prognostic prediction capabilities in both Western and East Asian populations, with a higher number of unfavorable variables indicating poorer LUAD prognosis. Notably, GPI expression in tumor tissues was inversely correlated with vascular normalization and positively correlated with plasma GPI expression.

CONCLUSION

Our study underscores the significance of integrating transcriptomics and proteomics data, emphasizing the need to account for genetic diversity among ethnic groups. The developed survival model may offer a holistic perspective on LUAD progression, enhancing prognosis and therapeutic strategies.

Role of sex in immune response and epigenetic mechanisms

The functioning of the human immune system is highly dependent on the sex of the individual, which comes by virtue of sex chromosomes and hormonal differences. Epigenetic mechanisms such as X chromosome inactivation, mosaicism, skewing, and dimorphism in X chromosome genes and Y chromosome regulatory genes create a sex-based variance in the immune response between males and females. This leads to differential susceptibility in immune-related disorders like infections, autoimmunity, and malignancies. Various naturally available immunomodulators are also available which target immune pathways containing X chromosome genes.

Molecular basis of sex differences in cancer: Perspective from Asia

Cancer is a leading cause of mortality and morbidity globally. Sex differences in cancer are evident in death rates and treatment responses in several cancers. Asian patients have unique cancer epidemiology influenced by their genetic ancestry and sociocultural factors in the region. In this review, we show molecular associations that potentially mediate sex disparities observed in cancer in Asian populations. Differences in sex characteristics are evident at the cytogenetic, genetic, and epigenetic levels mediating processes that include cell cycle, oncogenesis, and metastasis. Larger clinical and in vitro studies that explore mechanisms can confirm the associations of these molecular markers. In-depth studies of these markers can reveal their importance as diagnostics, prognostics, and therapeutic efficacy markers. Sex differences should be considered in designing novel cancer therapeutics in this era of precision medicine.