Embrace Changes, Always

This is the first time our work is selected as the Featured Research in Journal of Translational Medicine. We are deeply honored. And here, I, the corresponding author, would like to share one of my untold stories behind this research.

Published in Biomedical Research

Embrace Changes, Always
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BioMed Central
BioMed Central BioMed Central

Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach - Journal of Translational Medicine

Background Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are synthetic chemicals known for their widespread use and environmental persistence. These compounds have been increasingly linked to hepatotoxicity and the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms by which PFAS contribute to HCC remain underexplored. Methods This study employs a multi-omics approach that combines network toxicology, integrated machine learning, single-cell RNA sequencing, spatial transcriptomics, experimental validation, and molecular docking simulations to uncover the mechanisms through which PFAS exposure drives HCC. We analyzed publicly available transcriptomic data from several HCC cohorts and used differential gene expression analysis to identify targets associated with both PFAS exposure and HCC. We constructed a protein–protein interaction (PPI) network and a survival risk model, the PFAS-related HCC signature (PFASRHSig), based on integrated machine learning to identify prognostic biomarkers, with the goal of identifying core targets of PFAS in HCC progression and prognosis. RT-qPCR and immunohistochemical (IHC) staining were used to validate the expression levels of the targets in both tumor and normal tissues. Molecular docking simulations were conducted to assess the binding affinities between PFAS compounds and selected target proteins. Results Functional enrichment studies revealed that PFAS targets were associated with metabolic signaling pathways, which are actively involved in lipid, glucose, drug metabolism, etc. Through integrated machine learning and PPI network analysis, we identified six genes, APOA1, ESR1, IGF1, PPARGC1A, SERPINE1, and PON1, that serve as core targets of PFAS in both HCC progression and prognosis. These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. The results of RT-qPCR and IHC staining were consistent with the in silico findings. Molecular docking simulations revealed strong binding affinities between PFAS compounds and these core targets, supporting their potential roles in PFAS-induced hepatocarcinogenesis. Conclusions Our study highlights key molecular targets and pathways involved in PFAS-induced liver carcinogenesis and proposes a robust survival risk model (PFASRHSig) for HCC. These findings provide new insights into PFAS toxicity mechanisms and offer potential therapeutic targets for mitigating the health risks associated with PFAS exposure. Collectively, our findings help in advancing clinical applications by providing insights into disease mechanisms and potential therapeutic interventions.

I joined a research team in my freshman year in Wenzhou Medical University that  focused on bioinformatic analysis regarding breast cancer. The PI had big ambitions, as she spoke often of publishing groundbreaking papers and making a name in medical science. What she lacked, however, was the ability to turn any of those dreams into reality.

For two long years, she pushed us to satisfy her endless curiosity. Our own academic interests? Irrelevant. Our training needs? Ignored. She had us running analyses and generating data, all to support her ever-shifting, half-baked hypotheses.

Despite claiming herself as a mentor in computational biology, she could barely give any constructive suggestions and guidance. I had to spend my own money on R programming courses because the guidance I needed simply didn’t exist in her team. As for experimental design, she once had us perform T cell–related studies on nude mice (Balb/c), an act that defied scientific logic.

Still, I hesitated to leave. I feared that switching labs would “break the continuity” of my research training, as if there was anything coherent about the past two years.

Then the final straw came: one day during the summer holiday, after weeks of late nights spent in the lab, she said something cruel in a meeting, dismissive, cutting, and entirely uncalled for. That was it. I said goodbye.

After I left, I found a new mentor, who has real vision, solid expertise, and the real ability to lead a big team. I also met brilliant collaborators from across the country (e.g., Shanghai Jiao tong University Renji Hospital, BIOPIC, ...), who are thoughtful, capable, and passionate regarding their insights. For the first time, I felt part of a real scientific community, not just a cog in someone else's vanity project.

Then came the milestone that I finally got my first paper accepted. It was published in Journal of Translational Medicine. That achievement would have been unimaginable had I remained trapped in that dysfunctional lab. Stepping away didn’t just spare me further frustration, instead, it led me to a new environment filled with clarity, rigor, and collaboration.

That first publication was just the beginning. Encouraged and inspired by those around me, I went on to publish my second study, which, to my surprise, was selected as a Featured Research article. Yes, again in Journal of Translational Medicine

Now, as the first or corresponding author, I’ve published seven papers with a cumulative impact factor exceeding 50.  And more importantly, I love what I am doing now, deeply.

None of these would have happened if I hadn’t found the courage to leave the wrong place.

Embrace changes.

Always.

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