Application of statistical and bioinformatics methods to study inflammation driven health challenges

Inflammation plays a crucial yet paradoxical influence on both acute and chronic disease, fostering tumor progression while also directing mechanisms for immune defense. This thesis comprises three studies that explore the molecular determinants of inflammation, symptomatology, and intercellular communication, with a particular focus on developing translational biomarkers for future therapeutic interventions.

Paper I focuses on plasma proteins as robust non-invasive biomarkers for lung cancer (LC). By leveraging multiplex proximity extension assay (PEA), we have identified a signature of five- proteins panel (CD83, GZMA, GZMB, CD8A, and MMP12) that reliably distinguishes LC, including early-stage cancer compared with other cancer types and non-cancer patients. Expanding this panel with four other additional proteins (GAL9, PDCD1, CD4, and HO1), further enhanced sensitivity for advanced LC and improved discrimination of other cancers, which emphasize the value of integrating multiple inflammatory and immune-regulatory proteins for precise diagnosis.

Paper II investigates the molecular basis of symptom burden (SB) in patients referred by primary health care provider for suspected LC. By integrating symptoms in patient-reported outcome measures (EORTC QLQ-C30) with plasma proteomic profiles, we have discovered distinct clusters of inflammation associated proteins that track with increasing SB severity. Despite clear shifts in protein expression at different SB levels, no single "inflammatory signature" fully captures symptom heterogeneity. This emphasizes the need for a noble integrative approach/domain proposed as "Symptomomics". This new domain will serve as a "big data" paradigm for clinical manifestation by coupling patient-reported outcomes with multi-omics data to uncover underlying molecular hallmarks across complex symptom clusters.

Paper III is focused on protozoan parasites, by demonstrating how Plasmodium falciparum in malaria responds to nutritional stress via increased extracellular vesicle (EV) release. Alterations in EV cargo composition, particularly small RNAs that reflect a conserved stress response mechanism and highlight EVs as potential biomarkers or therapeutic targets in malaria pathogenesis.

Collectively, these studies represent how integrating high-throughput data, statistical and bioinformatics analyses, and patient-reported measures can begin to untangle the multifaceted roles of inflammation driven health challenges. Such knowledge might flag the way for developing more sensitive diagnostic tools, that target anti-inflammatory therapies, and personalized symptom management strategies across oncology and infectious diseases.

List of scientific papers

I. Multiplex plasma protein assays as a diagnostic tool for lung cancer. Mohammad Tanvir Ahamed, Jenny Forshed, Adrian Levitsky, Janne Lehtio, Amanj Bajalan, Maria Pernemalm, Lars E. Eriksson & Björn Andersson. Cancer Science, 2024, 115(10), 3439-3454. https://doi.org/10.1111/cas.16300 https://doi.org/10.1111/cas.16300

II. Integrative analysis of differential protein expression and symptom burden using co- expression and gene ontology. Mohammad Tanvir Ahamed, Amanj Bajalan, Janne Lehtio, Maria Pernemalm, Lars E. Eriksson, Björn Andersson. [Manuscript]

III. Starvation induces changes in abundance and small RNA cargo of extracellular vesicles released from Plasmodium falciparum infected red blood cells. Leonie Vetter, Amanj Bajalan, Mohammad Tanvir Ahamed, Caterina Scasso, Sulman Shafeeq, Björn Andersson & Ulf Ribacke. Scientific Reports, 2023, 13(1), 18423, https://doi.org/10.1038/s41598-023-45590-6