BUFFALO, NY — January 5, 2026 — A new #research paper featured as the #cover of Volume 17, Issue 12 of Aging-US was #published on December 22, 2025, titled “A combination of differential expression and network connectivity analyses identifies a common set of RNA splicing and processing genes altered with age across human tissues.”
In this study by Caio M.P.F. Batalha from the University of São Paulo, André Fujita from the University of São Paulo and Kyushu University, and Nadja C. de Souza-Pinto also from the University of São Paulo, researchers investigated how gene activity changes with age across multiple human tissues. They found that many tissues share common aging-related alterations in genes involved in RNA splicing and RNA processing. These findings are important because RNA processing is essential for accurate protein production, and disruptions in this process are linked to aging and disease.
Aging affects all tissues, yet identifying molecular changes that are shared across the body has remained challenging. To address this, researchers moved beyond traditional approaches that focus exclusively on changes in gene expression levels. They also analyzed how genes alter their patterns of interaction within regulatory networks, capturing age-related changes that are not evident from expression data alone.
“Gene expression data (in TPM – transcripts per million) were obtained from the Genotype-Tissue Expression (GTEx) project.”
Using RNA sequencing data from nearly one thousand human donors aged 20 to 70, the research team analyzed eight tissues, including blood, brain, heart, skin, and muscle. The results showed that many aging-related changes become evident only when gene network behavior is considered. When gene expression and network connectivity were analyzed together, a consistent group of genes emerged across tissues, most of which were linked to RNA splicing and RNA processing, key steps in the production of functional proteins.
The study also revealed that these RNA-related genes are highly interconnected at the protein level. Many of them form known protein complexes, including components of the spliceosome, which plays a central role in RNA maturation. With age, the interactions among these genes tend to reorganize in similar ways across tissues, pointing to a shared biological response rather than independent, tissue-specific effects.
In addition to RNA processing, the researchers observed age-related changes in pathways involved in managing damaged RNAs and proteins, including protein degradation, autophagy, and DNA damage response mechanisms. These pathways support cellular quality control and help limit the accumulation of molecular errors that increase with age.
Overall, this study identifies RNA splicing and RNA processing as central, conserved features of human aging across tissues. It also demonstrates that network-based approaches provide a more complete view of the aging transcriptome, offering new insights into age-related biological changes and potential directions for aging research.
DOI - https://doi.org/10.18632/aging.206347
Corresponding author - Nadja C. de Souza-Pinto - nadja@iq.usp.br
Abstract video - https://www.youtube.com/watch?v=A1slKwaSd6g
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BUFFALO, NY — January 5, 2026 — A new #research paper featured as the #cover of Volume 17, Issue 12 of Aging-US was #published on December 22, 2025, titled “A combination of differential expression and network connectivity analyses identifies a common set of RNA splicing and processing genes altered with age across human tissues.”
In this study by Caio M.P.F. Batalha from the University of São Paulo, André Fujita from the University of São Paulo and Kyushu University, and Nadja C. de Souza-Pinto also from the University of São Paulo, researchers investigated how gene activity changes with age across multiple human tissues. They found that many tissues share common aging-related alterations in genes involved in RNA splicing and RNA processing. These findings are important because RNA processing is essential for accurate protein production, and disruptions in this process are linked to aging and disease.
Aging affects all tissues, yet identifying molecular changes that are shared across the body has remained challenging. To address this, researchers moved beyond traditional approaches that focus exclusively on changes in gene expression levels. They also analyzed how genes alter their patterns of interaction within regulatory networks, capturing age-related changes that are not evident from expression data alone.
“Gene expression data (in TPM – transcripts per million) were obtained from the Genotype-Tissue Expression (GTEx) project.”
Using RNA sequencing data from nearly one thousand human donors aged 20 to 70, the research team analyzed eight tissues, including blood, brain, heart, skin, and muscle. The results showed that many aging-related changes become evident only when gene network behavior is considered. When gene expression and network connectivity were analyzed together, a consistent group of genes emerged across tissues, most of which were linked to RNA splicing and RNA processing, key steps in the production of functional proteins.
The study also revealed that these RNA-related genes are highly interconnected at the protein level. Many of them form known protein complexes, including components of the spliceosome, which plays a central role in RNA maturation. With age, the interactions among these genes tend to reorganize in similar ways across tissues, pointing to a shared biological response rather than independent, tissue-specific effects.
In addition to RNA processing, the researchers observed age-related changes in pathways involved in managing damaged RNAs and proteins, including protein degradation, autophagy, and DNA damage response mechanisms. These pathways support cellular quality control and help limit the accumulation of molecular errors that increase with age.
Overall, this study identifies RNA splicing and RNA processing as central, conserved features of human aging across tissues. It also demonstrates that network-based approaches provide a more complete view of the aging transcriptome, offering new insights into age-related biological changes and potential directions for aging research.
DOI - https://doi.org/10.18632/aging.206347
Corresponding author - Nadja C. de Souza-Pinto - nadja@iq.usp.br
Abstract video - https://www.youtube.com/watch?v=A1slKwaSd6g
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Using Machine Learning to Identify Senescence-Inducing Drugs for Resistant Cancers
Aging-US
5 minutes 31 seconds
4 weeks ago
Using Machine Learning to Identify Senescence-Inducing Drugs for Resistant Cancers
Treating aggressive cancers that do not respond to standard therapies remains one of the most significant challenges in oncology. Among these are basal-like breast cancers (BLBC), which lack hormone receptors and HER2 amplification. This makes them unsuitable for many existing targeted treatments. As a result, therapeutic options are limited, and patient outcomes are often poor.
One emerging strategy is to induce senescence, a state in which cancer cells permanently stop dividing but remain metabolically active. This approach aims to slow or stop tumor growth without killing the cells directly. Although promising, the clinical application of senescence-based therapies has been limited by several challenges.
Senescence is typically identified using biomarkers such as p16, p21, and beta-galactosidase activity. However, these markers are often already present in aggressive cancers like BLBC (Sen‑Mark+ tumors), making it difficult to determine whether a treatment is truly inducing senescence or merely reflecting the tumor’s existing biology. Moreover, conventional screening methods may mistake reduced cell growth for senescence, cell death, or temporary growth arrest, leading to inaccurate assessments. This is especially problematic in large-scale drug screening, where thousands of compounds must be evaluated quickly and reliably.
To overcome these issues, researchers from Queen Mary University of London and the University of Dundee have developed a new machine learning–based method to improve the detection of senescence in cancer cells. Their findings were recently published in Aging-US.
The Study: Developing the SAMP-Score
The study, titled “SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16-positive cancer cells,” was led by Ryan Wallis and corresponding author Cleo L. Bishop from Queen Mary University of London. This paper was featured on the cover of Aging-US Volume 17, Issue 11, and highlighted as our Editors’ Choice.
Full blog - https://aging-us.org/2025/12/using-machine-learning-to-identify-senescence-inducing-drugs-for-resistant-cancers/
Paper DOI - https://doi.org/10.18632/aging.206333
Corresponding author - Cleo L. Bishop - c.l.bishop@qmul.ac.uk
Abstract video - https://www.youtube.com/watch?v=qXI_KI3EgHE
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Keywords - aging, SAMP-Score, senescence, senescent marker positive cancer cells, Sen-Mark+, machine learning, pro-senescence, high-throughput compound screening
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Aging-US
BUFFALO, NY — January 5, 2026 — A new #research paper featured as the #cover of Volume 17, Issue 12 of Aging-US was #published on December 22, 2025, titled “A combination of differential expression and network connectivity analyses identifies a common set of RNA splicing and processing genes altered with age across human tissues.”
In this study by Caio M.P.F. Batalha from the University of São Paulo, André Fujita from the University of São Paulo and Kyushu University, and Nadja C. de Souza-Pinto also from the University of São Paulo, researchers investigated how gene activity changes with age across multiple human tissues. They found that many tissues share common aging-related alterations in genes involved in RNA splicing and RNA processing. These findings are important because RNA processing is essential for accurate protein production, and disruptions in this process are linked to aging and disease.
Aging affects all tissues, yet identifying molecular changes that are shared across the body has remained challenging. To address this, researchers moved beyond traditional approaches that focus exclusively on changes in gene expression levels. They also analyzed how genes alter their patterns of interaction within regulatory networks, capturing age-related changes that are not evident from expression data alone.
“Gene expression data (in TPM – transcripts per million) were obtained from the Genotype-Tissue Expression (GTEx) project.”
Using RNA sequencing data from nearly one thousand human donors aged 20 to 70, the research team analyzed eight tissues, including blood, brain, heart, skin, and muscle. The results showed that many aging-related changes become evident only when gene network behavior is considered. When gene expression and network connectivity were analyzed together, a consistent group of genes emerged across tissues, most of which were linked to RNA splicing and RNA processing, key steps in the production of functional proteins.
The study also revealed that these RNA-related genes are highly interconnected at the protein level. Many of them form known protein complexes, including components of the spliceosome, which plays a central role in RNA maturation. With age, the interactions among these genes tend to reorganize in similar ways across tissues, pointing to a shared biological response rather than independent, tissue-specific effects.
In addition to RNA processing, the researchers observed age-related changes in pathways involved in managing damaged RNAs and proteins, including protein degradation, autophagy, and DNA damage response mechanisms. These pathways support cellular quality control and help limit the accumulation of molecular errors that increase with age.
Overall, this study identifies RNA splicing and RNA processing as central, conserved features of human aging across tissues. It also demonstrates that network-based approaches provide a more complete view of the aging transcriptome, offering new insights into age-related biological changes and potential directions for aging research.
DOI - https://doi.org/10.18632/aging.206347
Corresponding author - Nadja C. de Souza-Pinto - nadja@iq.usp.br
Abstract video - https://www.youtube.com/watch?v=A1slKwaSd6g
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