Thomas M. O'COnnell

Background: Over the past several decades, advances in basic and clinical research have increasingly established cancer‐induced cachexia as a distinct pathophysiological syndrome that warrants targeted therapeutic approaches alongside conventional anticancer treatments. Despite its high prevalence and substantial impact on morbidity, mortality, and quality of life, the condition remains underrecognized and inadequately managed in clinical practice. Correspondingly, scientific activity in the field has expanded rapidly, spanning molecular mechanisms, clinical characterization, and emerging interventional strategies. Methods: This bibliometric analysis aims to provide a comprehensive, quantitative overview of evolving research in cancer‐induced cachexia over the past 25 years. We conducted a bibliometric analysis of cancer cachexia research published between 2001 and 2025 using the Web of Science Core Collection. A total of 5775 English‐language primary research articles were retrieved using the search terms “cancer AND cachexia,” and including papers in English of the types articles, letters, and proceedings papers. Data were analyzed using the Bibliometrix R package and its web interface, BiblioShiny, to assess publication trends, author productivity, collaboration networks, and keyword co‐occurrence. Additional visualizations were generated using the R programming language and AI‐guided analyses to explore trends and bursts in the evolution of cachexia research. Results: Cancer cachexia publications increased at an annual rate of 6.7%, exceeding the overall growth rate of scientific literature. In terms of number of papers and citation indices, the Journal of Cachexia, Sarcopenia and Muscle is the dominant journal in the field. Research is characterized by high levels of international collaboration, though research networks remain geographically clustered. Keywords were analyzed using co‐occurrence networks to reveal specific connections and clusters of research topics. Trend analyses of keywords showed the progression of research in the areas of metabolism and the role of inflammatory mediators. Burst analysis revealed research “hotspots” over the last 25 years including studies of specific interventions such as immunotherapy, gastrointestinal cancers, and metabolic dysregulation. Conclusions: Cancer cachexia research has expanded significantly over the past two decades but remains limited in scale and fragmented across geographic and disciplinary boundaries. A shift toward investigating biological mechanisms and therapeutics indicates a maturing field with growing translational potential. Growing trends and recent research bursts in immune function and inflammation suggest an important role of these areas in further therapeutic development. Continued integration of clinical, nutritional, and molecular research will be essential to advance effective interventions. This bibliometric analysis offers a data‐driven foundation to inform future research priorities and collaboration strategies in cancer‐induced cachexia.

Summary: The increasing size and complexity of omics datasets can make effective visualization and interpretation very challenging. Differential expression datasets as from RNA-sequencing and proteomics can contain many thousands of features and even after filtering for significance there can be hundreds or thousands of features to consider. A common tool for visualizing this type of data is the volcano plot where each feature is plotted as the log2 transformed fold change along the x-axis and the negative log10 of a P-value along the y-axis. These plots provide a useful visualization of the largest and most significant changes, but the majority of the features are often crowded and overlapping near the “cone” of the volcano. In order to provide a biologically informative way to simplify the visualization and interpretation of these types of datasets we developed the Pathway Volcano tool. This R-Shiny based software utilizes the Reactome API to select specific pathways and then filters the volcano plots to show only the data associated with those pathways. In this manner, many of the significant features in the crowded section of the volcano plot can be revealed to support the impact of specified pathways. This tool provides a range of interactive features to interrogate the data along with the ability to download png files and tables with pathway associated data. Availability and implementation: Pathway Volcano is a freely available R Shiny package. The program was developed in R version 4.3.3 using R Studio version 2024.09.1 Build 394. Running the app locally requires the packages ggplot2, plotly, shiny (https://shiny.posit.co/) dplyr (https://dplyr.tidyverse.org), and ReactomeContentServer (https://bioconductor.org/packages/ReactomeContentService4R/). The full code and documentation including example datasets are available at https://github.com/thoconne/PathwayVolcano and has been deposited in Zenodo under DOI 10.5281/zenodo.15425246.

Summary: The increasing size and complexity of omics datasets can make effective visualization and interpretation very challenging. Differential expression datasets as from RNA-sequencing and proteomics can contain many thousands of features and even after filtering for significance there can be hundreds or thousands of features to consider. A common tool for visualizing this type of data is the volcano plot where each feature is plotted as log2 transformed fold change along the x-axis and the negative log10 of a p-value along the y-axis. These plots provide a useful visualization of the largest and most significant changes, but the majority of the features are often crowded and overlapping near the “cone” of the volcano. In order to provide a biologically informative way to simplify the visualization and interpretation of these types of datasets we developed the Pathway Volcano tool. This R-Shiny based software utilizes the Reactome API to select specific pathways and then filters the volcano plots to show only the data associated with those pathways. In this manner, many of the significant features in the crowded section of the volcano plot can be revealed to support the impact of specified pathways. This tool provides a range of interactive features to interrogate the data along with the ability to download png files and tables with pathway associated data. Availability and implementation: Pathway Volcano is a freely available R Shiny package. The program was developed in R version 4.3.3 using R Studio version 2024.09.1 Build 394. Running the app locally requires the packages ggplot2 (1), plotly (2), shiny (3) dplyr (4) and ReactomeContentServer (5). The full code and documentation including example datasets are available at https://github.com/thoconne/PathwayVolcano Contact: thoconne@iu.edu

Investigating repeated resistance training separated by a training break enables exploration of the potential for a proteomic memory of resistance training (RT)-induced skeletal muscle growth. Our aim was to examine skeletal muscle proteome response to 10-week RT (RT1) followed by 10-week training cessation (i.e. detraining, DT), and finally, 10-week retraining (RT2). Thirty healthy, untrained participants conducted either periodic RT (RT1-DT-RT2, n=17) or a 10-week no-training control period (n=13) followed by 20 weeks of RT (n=11). RT included twice-weekly supervised whole-body RT sessions, and resting vastus lateralis biopsies were obtained every ten weeks for proteomics analysis using high-end DIA-PASEF’s mass spectrometry. The first RT period altered 150 proteins (93% increased) involved in e.g. energy metabolism and protein processing compared with minor changes during the no-training control period. The proteome adaptations were similar after the second RT compared to baseline demonstrating reproducibility in proteome adaptations to RT. Many of the proteins induced by RT1 were reversed towards baseline after detraining and increased again after retraining. These reversible proteins were especially involved in aerobic energy metabolism. Interestingly, several proteins increased after RT1 remain elevated after detraining, including carbonyl reductase 1 (CBR1) and proteins involved in muscle contraction, cytoskeleton and calcium-binding. Amongst the latter, calcium-activated protease calpain-2 (CAPN2) has been recently identified as an epigenetic muscle memory gene. We show that resistance training evokes retained protein levels even after 2.5 months of no training. This is the first study to demonstrate a potential proteomic memory of resistance training-induced muscle growth in human skeletal muscle. Key points: Repeated resistance training in humans separated by a training break (i.e. detraining) enables the identification of temporal protein signatures over the training, detraining, and retraining periods as well as studying reproducibility of protein changes to resistance training. Muscle proteome adaptations were similar after a second period of resistance training when compared to baseline, demonstrating reproducibility in proteome adaptations to earlier resistance training. Many of the proteins induced by resistance training were reversed towards baseline after detraining and increased again after retraining. These reversible proteins were especially involved in aerobic energy metabolism. Several proteins increased after resistance training remain elevated after detraining, including carbonyl reductase 1 (CBR1) and calcium-binding proteins such as calpain-2 (CAPN2), a recently identified epigenetic muscle memory gene. Human skeletal muscle experiences retained protein changes following resistance training persisting over two months demonstrating a potential proteomic memory of resistance training-induced muscle growth. Abstract Figure: Human skeletal muscle proteome response was investigated after 10-week resistance training (RT1) followed by 10-week training cessation (i.e. detraining, DT), and finally, 10-week retraining (RT2). Many of the proteins were reversed towards baseline after DT and increased again after RT2. These reversible proteins were especially involved in aerobic energy metabolism. However, several RT-induced proteins remain elevated after DT, including carbonyl reductase 1 (CBR1) and many proteins involved in muscle contraction or cytoskeleton and calcium-binding. Amongst the latter, calcium-activated protease calpain-2 (CAPN2) is a recently identified epigenetic muscle memory gene. This study shows that resistance training evokes retained protein levels even after 2.5 months of no training and demonstrates a potential proteomic memory of RT-induced muscle growth in human skeletal muscle. Created in BioRender.com.

The present study examined for the first time the effects of exercise-like electrical pulse stimulation administered under distinct nutritional conditions on 1) the transcriptome of the C2C12 myotubes and 2) their media containing extracellular vesicle-carried microRNAs. We report that higher glucose availability augmented transcriptional responses related especially to contractility and cytokine/inflammatory pathways. Agreeing with in vivo studies, we show that the packing of exercise-responsive miR-1-3p was increased in the extracellular vesicles in response to myotube contractions.

Introduction A primary objective of metabolomics studies is identifying the critical metabolic pathways that are altered under specific conditions. There are a number of tools such as enrichment or over-representation algorithms which can identify pathways, but tools for visualizing these pathways beyond static diagrams are limited. Objectives Development of an open-source software tool to enable users to generate metabolic pathway diagrams that can be interactively adjusted to allow for the most lucid and meaningful representation of the data. The ability to input and integrate multiple metabolic pathways and interactively adjust could facilitate the identification of novel metabolic pathways. Methods The Viime-Path software package utilizes the metabolic pathways in the Reactome Database. The initial pathway layouts are generated using the Human-like Orthogonal Network Layout (HOLA) algorithm which was designed to take such datasets and map them onto orthogonal network layouts using aesthetic principles derived from the analysis of human-generated networks. The software is written in TypeScript and Vue, utilizing the WebCola graph layout library and D3.js for visualization primitives. Results We have created an easy-to-use, web-based, open-source tool to easily import metabolic pathways from the Reactome database. The tool allows for interactive optimization of pathways to best represent the most salient features for a given analysis. The tool is available on GitHub at https://github.com/girder/viime-path. Conclusions The Viime-Path tool will enable the metabolomics community to interactively draw metabolic pathway networks with options to adjust specific components and layout, thus providing new levels of clarity and focus.

Background: Children and young adults with single ventricle (SV) heart disease frequently develop heart failure (HF) that is intractable and difficult to treat. Our understanding of the molecular and biochemical reasons underlying this is imperfect. Thus, there is an urgent need for biomarkers that predict outcome and provide a rational basis for treatment, and advance our understanding of the basis of HF. Objective: We sought to determine if a metabolomic approach would provide biochemical signatures of HF in SV children and young adults. If significant, these analytes might serve as biomarkers to predict outcome and inform on the biological mechanism(s) of HF. Methods: We applied a multi-platform metabolomics approach composed of mass spectrometry (MS) and nuclear magnetic resonance (NMR) which yielded 495 and 26 metabolite measurements respectively. The plasma samples came from a cross-sectional set of young SV subjects, ages 2–19 years with ten control (Con) subjects and 16 SV subjects. Of the SV subjects, nine were diagnosed as congestive HF (SVHF), and 7 were not in HF. Metabolomic data were correlated with clinical status to determine if there was a signature associated with HF. Results: There were no differences in age, height, weight or sex between the 3 cohorts. However, statistical analysis of the metabolomic profiles using ANOVA revealed 44 metabolites with significant differences between cohorts including 41 profiled by MS and 3 by NMR. These metabolites included acylcarnitines, amino acids, and bile acids, which distinguished Con from all SV subjects. Furthermore, metabolite profiles could distinguish between SV and SVHF subjects. Conclusion: These are the first data to demonstrate a clear metabolomic signature associated with HF in children and young adults with SV. Larger studies are warranted to determine if these findings are predictive of progression to HF in time to provide intervention.

Background:Cancer cachexia is a severe metabolic disorder characterized by progressive weight loss along with a dramatic loss in skeletal muscle and adipose tissue. Like cancer, cachexia progresses in stages starting with pre-cachexia to cachexia and finally to refractory cachexia. In the refractory stage, patients are no longer responsive to therapy and management of weight loss is no longer possible. It is therefore critical to detect cachexia as early as possible. In this study we applied a metabolomics approach to search for early biomarkers of cachexia.Methods:Multi-platform metabolomics analyses were applied to the murine Colon-26 (C26) model of cachexia. Tumor bearing mice (n= 5) were sacrificed every other day over the 14-day time course and control mice (n= 5) were sacrificed every fourth day starting at day 2. Linear regression modeling of the data yielded metabolic trajectories that were compared with the trajectories of body weight and skeletal muscle loss to look for early biomarkers of cachexia.Results:Weight loss in the tumor-bearing mice became significant at day 9 as did the loss of tibialis muscle. The loss of muscle in the gastrocnemius and quadriceps was significant at day 7. Reductions in amino acids were among the earliest metabolic biomarkers of cachexia. The earliest change was in methionine at day 4. Significant alterations in acylcarnitines and lipoproteins were also detected several days prior to weight loss.Conclusion:The results of this study demonstrate that metabolic alterations appear well in advance of observable weight loss. The earliest and most significant alterations were found in amino acids and lipoproteins. Validation of these results in other models of cachexia and in clinical studies will pave the way for a clinical diagnostic panel for the early detection of cachexia. Such a panel would provide a tremendous advance in cachectic patient management and in the design of clinical trials for new therapeutic interventions.

We aimed to identify plasma and urine metabolites altered by the Dietary Approaches to Stop Hypertension (DASH) diet in a post-hoc analysis of a pilot feeding trial. Twenty adult participants with un-medicated hypertension consumed a Control diet for one week followed by 2 weeks of random assignment to either Control or DASH diet. Non-missing fasting plasma (n = 56) and 24-h urine (n = 40) were used to profile metabolites using untargeted gas chromatography/mass spectrometry. Linear models were used to compare metabolite levels between the groups. In urine, 19 identifiable untargeted metabolites differed between groups at p < 0.05. These included a variety of phenolic acids and their microbial metabolites that were higher during the DASH diet, with many at false discovery rate (FDR) adjusted p < 0.2. In plasma, eight identifiable untargeted metabolites were different at p < 0.05, but only gamma-tocopherol was significantly lower on DASH at FDR adjusted p < 0.2. The results provide insights into the mechanisms of benefit of the DASH diet.