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. Author manuscript; available in PMC: 2024 Oct 17.
Published in final edited form as: Support Care Cancer. 2024 Mar 26;32(4):250. doi: 10.1007/s00520-024-08446-x

Perturbations in Inflammatory Pathways Are Associated with Shortness of Breath Profiles in Oncology Patients Receiving Chemotherapy

Joosun Shin 1,7, Christine Miaskowski 1,2, Melisa L Wong 2, Patsy Yates 3, Adam B Olshen 4,5, Roy Ritu 5, Vasuda Dokiparthi 1, Bruce Cooper 1, Steven Paul 1, Yvette P Conley 6, Jon D Levine 2, Marilyn J Hammer 7, Kord Kober 1
PMCID: PMC11484916  NIHMSID: NIHMS2026841  PMID: 38532105

Abstract

Purpose:

One plausible mechanistic hypothesis is the potential contribution of inflammatory mechanisms to shortness of breath. This study was aimed to evaluate for associations between the occurrence of shortness of breath and perturbations in inflammatory pathways.

Methods:

Patients with cancer reported the occurrence of shortness of breath six times over two cycles of chemotherapy. Latent class analysis was used to identify subgroups of patients with distinct shortness of breath occurrence profiles [i.e., None (70.5%), Decreasing (8.2%), Increasing (7.8%), High (13.5%)]. Using an extreme phenotype approach, whole transcriptome differential gene expression and pathway impact analyses were performed to evaluate for perturbed signaling pathways associated with shortness of breath between the None and High classes. Two independent samples [RNA-sequencing (n = 293) and microarray (n = 295) methodologies] were evaluated. Fisher’s Combined Probability method was used to combine these results to obtain a global test of the null hypothesis. In addition, an unweighted knowledge network was created using the specific pathway maps to evaluate for interconnections among these pathways.

Results:

Twenty-nine Kyoto Encyclopedia of Genes and Genomes inflammatory signaling pathways were perturbed. The mitogen-activated protein kinase signaling pathway node had the highest closeness, betweenness, and degree scores. In addition, five common respiratory disease-related pathways, that may share mechanisms with cancer-related shortness of breath, were perturbed.

Conclusions:

Findings provide preliminary support for the hypothesis that inflammation contribute to the occurrence of shortness of breath in patients with cancer. In addition, the mechanisms that underlie shortness of breath in oncology patients may be similar to other respiratory diseases.

Keywords: Dyspnea, gene expression, inflammation, neoplasms

INTRODUCTION

Dyspnea is a common symptom in oncology patients. 1 While dyspnea worsens quality of life and decreases overall survival, its underlying mechanisms are unknown. 1 Of note, the American Society of Clinical Oncology Guideline on the Management of Dyspnea in Advanced Cancer recommended that research on the mechanisms of dyspnea is needed to develop targeted interventions. 1

In terms of a plausible mechanistic hypothesis, airway inflammation and associated perturbations in vagal afferent neurons appear to play central roles in the development of dyspnea. 2 First, inflammation results in the activation of bronchopulmonary C-fibers. 2 Second, inflammatory processes induce airway wall remodeling that increases tension in airway smooth muscles. 2 Third, airway inflammation leads to sensory neuroplasticity that results in hyperexcitability in primary afferent neurons and amplification of synaptic transmission. Finally, systemic inflammation may reflect the “spill-over” of inflammatory mediators within the lung, primary changes in the extrapulmonary immune response, or a combination of both processes. 3

Tumor cells and cytotoxic drugs may contribute to the development of dyspnea through the stimulation of innate and adaptive immune mechanisms. 4 This systemic response results in the activation of inflammatory signaling pathways and the destruction of bronchoalveolar structures. 5 In patients with lung cancer, 6 significant decreases in pulmonary function tests (PFTs) occurred following chemotherapy. These reductions in PFTs are an indicator of pulmonary toxicity. 6

Inflammation can have direct effects on skeletal muscles (e.g., diaphragm) through activation of intramuscular signaling pathways (e.g., Janus-activated kinase/signal transducer and activator of transcription (JAK/STAT), p38 mitogen-activated protein kinase (MAPK) pathways). 7 Equally important, chronic inflammation can result in skeletal muscle atrophy. 7 In one study of oncology patients, 8 more severe dyspnea was associated with lower levels of maximal inspiratory pressure, a measure of diaphragmatic strength. In a preclinical study, 9 systemic administrations of a clinical dose of doxorubicin resulted in inflammation and weakness of the diaphragm. Therefore, it is reasonable to hypothesize that systemic and peripheral lung inflammation contribute to the development of dyspnea in oncology patients.

In terms of the molecular mechanisms of dyspnea in oncology patients, three candidate gene studies were identified. 10–12 In a study of lung cancer survivors, 11 the severity of dyspnea was associated with single nucleotide polymorphisms (SNPs) in interleukin (IL)-6 and IL-1β. In a study of patients with non-small cell lung cancer, 12 three SNPs in the BReast Cancer 1 gene were associated with the severity of dyspnea. In patients with advanced cancer, 10 individuals who were homozygous for the rare allele in the 5-hydroxytryptamine receptor 3B gene were more likely to report severe dyspnea. Of note, no studies have evaluated for associations between the occurrence of dyspnea and perturbations in inflammatory pathways.

The wide range in prevalence rates suggests that a large amount of inter-individual variability exists in the occurrence of dyspnea. To evaluate this variability, we used latent class analysis (LCA) to identify subgroups of patients with distinct dyspnea profiles. 13 Using occurrence data from 1338 patients undergoing chemotherapy, 70.5% did not report shortness of breath. Of the remaining 395 patients, three distinct shortness of breath profiles were identified (i.e., Decreasing (8.2%), Increasing (7.8%), High (13.5%)). In the current analysis, an extreme phenotype and data-driven approach was used to evaluate for perturbed inflammatory pathways between the None and High classes. Then, a knowledge network was used to identify the most influential pathway and interactions among the pathways. 14

METHODS

Methods for this study are describe in detail in Appendix A. In brief, eligible patients were ≥18 years of age; had breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; were scheduled to receive at least two additional cycles of chemotherapy.

Study was approved by the Institutional Review Board at the University of California, San Francisco and at each of the study sites. Eligible patients were approached in the infusion unit during their first or second cycle of chemotherapy and written informed consent was obtained. Of the 2234 patients approached, 1343 consented to participate, and 1338 reported the occurrence of shortness of breath a total of six times over two chemotherapy cycles. The other measures and blood were collected at the enrolment assessment (i.e., prior to the second or third cycle of chemotherapy).

For this analysis, of the 717 patients who provided a blood sample (Supplemental Figure 1), 357 had their samples processed using RNA sequencing (i.e., RNA-seq sample) and 360 had their samples processed using microarray (i.e., microarray sample) technologies.

Instruments

As reported in Appendix A, patients completed demographic and clinical questionnaires. Shortness of breath item from the Memorial Symptom Assessment Scale was used to assess for the occurrence of the symptom at each of the six assessments. 15

Data Analysis

As reported previously, 13 LCA was used to identify subgroups of patients with distinct shortness of breath profiles over the six assessments. For each sample, differences in demographic and clinical characteristics between the None and High shortness of breath classes were evaluated using parametric and non-parametric tests. Significance corresponded to a p-value of <.05. For the logistic regression models, demographic and clinical characteristics included in the final model were selected using a backwards stepwise logistic regression approach. All of these analyses were performed using R (version 4.2.1). 16

Differential expression and pathway impact analyses (PIA) and knowledge network construction

Gene expression and PIA were performed based on our previous experience. 17 In brief, differential expression was quantified using empirical Bayes models that were implemented using edgeR 18 for the RNA-seq sample and limma 19 for the microarray sample. Analyses were adjusted for demographic and clinical characteristics that remained significant in the final logistic regression model. In addition, the models included surrogate variables to adjust for variations due to unmeasured sources. 20

PIA included results of the differential expression analyses for all of the genes that had a common direction of expression to determine probability of pathway perturbations (pPERT) using Pathway Express. 21 Total of 222 signaling pathways were defined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 22 For each sample, a separate test was performed for each pathway. Next, Fisher’s Combined Probability method was used to combine these results to obtain a global test of the null hypothesis. 23 Significance of the combined transcriptome-wide PIA was assessed using a false discovery rate (FDR) of 0.025. 24 Then, the KEGG Orthology was used to classify the perturbed inflammatory pathways. 25

Next, an unweighted knowledge network was created based on interconnections among the inflammatory pathways using KEGG pathway maps. 14 Nodes were defined as perturbed inflammatory pathways identified in this analysis. Edges were defined as shared member(s) identified in KEGG pathway maps. To gain insights into the structural importance of each node, scores for three centrality measures (i.e., betweenness, closeness, degree) were calculated using Cytoscape (version 3.9.1). 26

RESULTS

RNA-seq and Microarray Performance

Of the 293 patients in the RNA-seq sample, 233 were in the None and 60 were in the High shortness of breath classes. Median library threshold size was 9,209,606 reads. Following the application of quality control filters, 15,967 genes were included in the final analysis. The common dispersion was estimated as 0.1943, with a biological coefficient of variation of 0.4407.

Of the 295 patients in the microarray sample, 242 were in the None and 53 were in the High shortness of breath classes. All of these samples demonstrated good hybridization performance. Following quality control filters, 44,225 loci were included in the final analysis.

Demographic and Clinical Characteristics

In RNA seq sample, compared to None class, High class was more likely to have a lower performance status, a higher number of comorbidities, a higher comorbidity burden, a lower AUDIT score, and lower hemoglobin and hematocrit levels. Differences in other patient characteristics are reported in Supplemental Table 1.

In microarray sample, compared to None class, High class had a lower performance status, a higher number of comorbidities, a higher comorbidity burden and a higher number of prior cancer treatments. Differences in other patient characteristics are reported in Supplemental Table 2.

Regression Analyses

In the final model for RNA-seq sample (Table 1), KPS score, hemoglobin levels, cancer diagnosis were retained used as covariates in the gene expression analysis. Patients who had a lower functional status and lung cancer were more likely to belong to the High class.

Table 1.

Multiple Logistic Regression Analyses Predicting Membership in the High Shortness of Breath Class

RNA seq Sample (n = 293)
Predictors Odds Ratio 95% CI p-value
Karnofsky Performance Status score 0.97 0.95, 1.00 0.041
Hemoglobin (g/dL) 0.83 0.65, 1.05 0.122
Cancer diagnosis
 Breast 1.00
 Gastrointestinal 0.59 0.27, 1.23 0.168
 Gynecological 0.69 0.25, 1.68 0.429
 Lung 4.72 1.90, 12.11 0.001
Overall model fit: AUC of the ROC = 0.713
Microarray Sample (n = 295)
Predictors Odds Ratio 95% CI p-value
Karnofsky Performance Status score 0.98 0.95, 1.01 0.148
Self-Administered Comorbidity Questionnaire score 1.18 1.07, 1.31 0.002
Cancer diagnosis
 Breast 1.00
 Gastrointestinal 0.33 0.11, 0.84 0.028
 Gynecological 0.92 0.41, 2.03 0.842
 Lung 1.74 0.73, 4.09 0.204
Overall model fit: AUC of the ROC = 0.713
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Abbreviations: AUC = area under curve; CI = confidence interval; g/dL= grams per deciliter; RNA = ribonucleic acid; ROC = receiver operating characteristic

In the final model for the microarray sample (Table 1), KPS score, SCQ score, and cancer diagnosis were retained and used as covariates in the gene expression analysis. Patients who had a higher comorbidity burden were more likely to belong to the High class. Patients who had gastrointestinal cancer were less likely to belong to the High class.

Perturbed Inflammatory Signaling Pathways

Final differential expression models for the two samples each included two surrogate variables and three phenotypic characteristics. A total of 5130 genes and 4922 genes were included in the PIA analyses for the RNA seq and microarray samples, respectively. Across the two samples, 73 KEGG signaling pathways were significantly perturbed at an FDR of 0.025. As shown in Table 2, 29 of these pathways were related to inflammatory mechanisms.

Table 2.

Perturbed Inflammatory KEGG Signaling Pathways Between Patients in the None Versus the High Shortness of Breath Classes

Pathway ID Pathway Name Combined Analysis Statistics
Immune System
hsa04612 Antigen processing and presentation X2 = 21.97, pPert = 0.0042
hsa04672 Intestinal immune network for IgA production X2 = 21.94, pPert = 0.0042
hsa04610 Complement and coagulation cascades X2 = 20.64, pPert = 0.0064
hsa04613 Neutrophil extracellular trap formation X2 = 19.19, pPert = 0.0077
hsa04621 NOD-like receptor signaling X2 = 18.84, pPert = 0.0077
hsa04650 Natural killer cell mediated cytotoxicity X2 = 16.94, pPert = 0.0119
hsa04623 Cytosolic DNA-sensing pathway X2 = 16.55, pPert = 0.0125
hsa04625 C-type lectin receptor signaling pathway X2 = 15.72, pPert = 0.0149
hsa04622 RIG-I-like receptor signaling pathway X2 = 15.66, pPert = 0.0150
hsa04062 Chemokine signaling pathway X2 = 15.51, pPert = 0.0157
hsa04659 Th17 cell differentiation X2 = 14.64, pPert = 0.0199
hsa04666 Fc gamma R-mediated phagocytosis X2 = 14.23, pPert = 0.0220
hsa04611 Platelet activation X2 = 13.75, pPert = 0.0248
Signal Molecules and Interaction
hsa04060 Cytokine-cytokine receptor interaction X2 = 22.75, pPert = 0.0042
hsa04061 Viral protein interaction with cytokine and cytokine receptor X2 = 19.04, pPert = 0.0077
Signal Transduction
hsa04151 PI3K-Akt signaling pathway X2 = 16.17, pPert = 0.0137
hsa04010 MAPK signaling pathway X2 = 16.14, pPert = 0.0137
hsa04371 Apelin signaling pathway X2 = 15.35, pPert = 0.0163
hsa04068 FoxO signaling pathway X2 = 14.80, pPert = 0.0191
hsa04390 Hippo signaling pathway X2 = 14.50, pPert = 0.0203
hsa04064 NF-kappa B signaling pathway X2 = 13.87, pPert = 0.0242
hsa04630 JAK-STAT signaling pathway X2 = 13.77, pPert = 0.0248
Transport and Catabolism
hsa04144 Endocytosis X2 = 19.50, pPert = 0.0077
hsa04145 Phagosome X2 = 16.78, pPert = 0.0125
hsa04146 Peroxisome X2 = 15.18, pPert = 0.0169
Cell growth and death
hsa04210 Apoptosis X2 = 19.42, pPert = 0.0077
hsa04217 Necroptosis X2 = 18.73, pPert = 0.0077
hsa04218 Cellular senescence X2 = 16.54, pPert = 0.0125
Cell motility
hsa04810 Regulation of actin cytoskeleton X2 = 15.19, pPert = 0.0169
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Abbreviations: DNA = deoxyribonucleic acid; FoxO = Forkhead box O; ID = identifier; IgA = Immunoglobulin A; JAK-STAT = Janus Kinase/Signal Transducers and Activators of Transcription; KEGG = Kyoto Encyclopedia of Genes and Genomes; MAPK = mitogen-activated protein kinase; NF-kappa B = nuclear factor kappa light chain enhancer of activated B cells; NOD = nucleotide-binding and Oligomerization Domain; pPert = combined perturbation p-value using Fisher’s Method adjusted using the Bonferroni method; PI3K-Akt = Phosphatidylinositol-3-kinase-protein kinase B; R = Receptor; RIG-I = Retinoic acid-inducible gene-I-like receptors; Th17 = T-helper 17.

Knowledge Network

Knowledge network consisted of 26 nodes (i.e., pathways) and 60 edges (average number of neighbors = 4.62; Figure 1). Viral protein interaction with cytokine and cytokine receptor, peroxisome, and hippo signaling pathways were not included in the knowledge network due to the lack of the interconnection with the other pathways.

Figure 1.

Figure 1.

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An undirected shortness of breath knowledge network generated from connections among the perturbed of inflammation-related Kyoto Encyclopaedia of Genes and Genomes (KEGG) signalling pathways associated with Shortness of Breath in Patients receiving chemotherapy. Nodes represent each of the KEGG signalling pathways. Edges represent connections between the pathways. Node size corresponds to betweenness centrality score (bigger is higher). Node fill shade corresponds to closeness centrality score (yellowish is higher). Node border colour represents KEGG Ontology classification (i.e., blue = signal transduction; red = immune system; grey = others). Abbreviations: ACTIN = regulation of actin cytoskeleton; APC = antigen processing and presentation; Apelin = apelin signalling pathway; APOP = apoptosis; CCC = complement and coagulation cascades; C-CR = cytokine-cytokine receptor interaction; C-DNA = cytosolic DNA-sensing pathway; Cell-Sene = cellular senescence; C-type LR = C-type lectin receptor signalling pathway; CXC = chemokine signalling pathway; ENDO = endocytosis; Fc-gamma = Fc gamma R-mediated phagocytosis; FoxO = FoxO signalling pathway; IgA = intestinal immune network for IgA production; JAK-STAT = JAK-STAT signalling pathway; MAPK = MAPK signalling pathway; NECROP = necroptosis; NET form = neutrophil extracellular trap formation; NF-kappa B = NF-kappa B signalling pathway; NK-cell = natural killer cell mediated cytotoxicity; NOD-LR = NOD-like receptor signalling pathway; PHAG = phagosome; PI3K-Akt = PI3K-Akt signalling pathway; PLT = platelet activation; RIG-I-IR = RIG-I-like receptor signalling pathway; Th17 cell = Th17 cell differentiation.

Signal transduction pathways grouped together within the knowledge network (blue circles). Subgroups of other inflammatory pathways were connected through these signal transduction pathways (i.e., red circles = immune system; grey circles = signal molecules and interaction, transport and catabolism, cell growth and death, and cell motility). Given that higher scores for closeness suggest that these nodes have closer relationships with and more direct influence on other nodes within the network, 27 Table 3 was organized in descending order of the closeness scores.

Table 3.

Centrality Measures for the Perturbed Inflammatory KEGG Signaling Pathways in the Shortness of Breath Knowledge Network

Pathway ID KO classification Pathway Name Betweenness Centrality Closeness Centrality* Degree Centrality†
hsa04010 Signal transduction MAPK signaling pathway 0.261 0.610 0.480
hsa04630 Signal transduction JAK-STAT signaling pathway 0.097 0.556 0.200
hsa04210 Cell growth and death Apoptosis 0.120 0.532 0.360
hsa04151 Signal transduction PI3K-Akt signaling pathway 0.118 0.532 0.360
hsa04650 Immune system Natural killer cell mediated cytotoxicity 0.135 0.500 0.240
hsa04613 Immune system Neutrophil extracellular trap formation 0.115 0.490 0.120
hsa04064 Signal transduction NF-kappa B signaling pathway 0.065 0.490 0.360
hsa04060 Signal molecules and interaction Cytokine-cytokine receptor interaction 0.073 0.472 0.160
hsa04621 Immune system NOD-like receptor signaling pathway 0.024 0.472 0.200
hsa04068 Signal transduction FoxO signaling pathway 0.080 0.463 0.200
hsa04062 Immune system Chemokine signaling pathway 0.017 0.463 0.280
hsa04622 Immune system RIG-I-like receptor signaling pathway 0.007 0.446 0.160
hsa04659 Immune system Th17 cell differentiation 0.082 0.439 0.160
hsa04810 Cell motility Regulation of actin cytoskeleton 0.023 0.439 0.120
hsa04623 Immune system Cytosolic DNA-sensing pathway 0.015 0.424 0.160
hsa04611 Immune system Platelet activation 0.013 0.410 0.120
hsa04666 Immune system Fc gamma R-mediated phagocytosis 0.042 0.403 0.120
hsa04371 Signal transduction Apelin signaling pathway 0.000 0.403 0.200
hsa04612 Immune system Antigen processing and presentation 0.101 0.391 0.200
hsa04144 Transport and catabolism Endocytosis 0.024 0.385 0.120
hsa04610 Immune system Complement and coagulation cascades 0.026 0.379 0.200
hsa04271 Cell growth and death Necroptosis 0.000 0.362 0.080
hsa04145 Transport and catabolism Phagosome 0.048 0.352 0.040
hsa04218 Cell growth and death Cellular senescence 0.000 0.321 0.040
hsa04625 Immune system C-type lectin receptor signaling pathway 0.000 0.309 0.080
hsa04672 Immune system Intestinal immune network for IgA production 0.000 0.284 0.040
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Abbreviations: DNA = Deoxyribonucleic acid; FoxO = Forkhead box O; hsa = Homo sapiens; ID = identifier; IgA = Immunoglobulin A; JAK-STAT = Janus Kinase/Signal Transducers and Activators of Transcription; KEGG = Kyoto Encyclopedia of Genes and Genomes; KO = Kyoto Encyclopedia of Genes and Genomes Orthology; MAPK = mitogen-activated protein kinase; NF-kappa B = nuclear factor kappa light chain enhancer of activated B cells; NOD = nucleotide-binding and oligomerization domain; PI3K-Akt = phosphatidylinositol-3-kinase-protein kinase B; R = receptor; RIG-I = Retinoic acid-inducible gene-I-like receptors; Th17 = T-helper 17

*

Table organized in descending order for closeness measures

†

Degree centrality = degree / (total number of nodes − 1)

MAPK pathway node had the highest closeness, betweenness, and degree scores. Next ten pathways with the highest centrality scores were: JAK/STAT signaling, apoptosis, phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt) signaling, natural killer (NK)-cell mediated cytotoxicity, neutrophil extracellular trap (NET) formation, nuclear factor kappa light chain enhancer of activated B cells (NF-kappa B) signaling, cytokine-cytokine receptor interaction, NOD-like receptor signaling, Forkhead box O (FoxO) signaling, and chemokine signaling.

Perturbed Respiratory Disease-related Pathways

As noted in the Introduction, our initial hypothesis was that shortness of breath would be associated with perturbations in inflammatory pathways. An evaluation of the PIA results identified five respiratory disease-related pathways that were significantly perturbed (Table 4). In an exploratory analysis, the KEGG pathway maps for each of these respiratory disease-related pathways were evaluated for the inclusion of inflammatory pathways. Table 4 summarizes these findings.

Table 4.

Perturbed Respiratory Disease-Related KEGG Signaling Pathways Between Patients in the None Versus the High Shortness of Breath Classes

Pathway ID Pathway Name Combined Analysis Statistics
Infectious Disease; Viral
hsa05171 Coronavirus disease – COVID-19 X2 = 30.41, pPert = 0.0009
hsa05164 Influenza A X2 = 22.98, pPert = 0.0042
Infectious Disease; Bacterial
hsa05133 Pertussis X2 = 19.40, pPert = 0.0077
hsa05152 Tuberculosis X2 = 15.87, pPert = 0.0145
Immune Disease
hsa05310 Asthma X2 = 18.66, pPert = 0.0077
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Abbreviations: COVID-19 = coronavirus disease; ID = identifier; KEGG = Kyoto Encyclopedia of Genes and Genomes; pPert = combined perturbation p-value using Fisher’s Method adjusted using the Bonferroni method

DISCUSSION

This study is the first to identify perturbations in inflammatory pathways associated with shortness of breath in oncology patients receiving chemotherapy. Our findings support the role of inflammatory mechanisms was supported and the majority of these inflammatory pathways interact with each other (Figure 1). This discussion focuses on the eleven inflammatory pathways with the highest closeness centrality scores that are organized using the KEGG Orthology for inflammatory mechanisms. 25

Signal Transduction

Signal transduction is a process through which external stimuli are transduced into cells through an ordered sequence of biochemical reactions that results in a signal cascade. 28 Signal transduction pathways with highest closeness scores were: MAPK signaling, JAK-STAT signaling, PI3K-Akt signaling, NF-kappa B signaling, and FoxO signaling.

MAPK pathway

The MAPK pathway node had the highest closeness, degree, and betweenness scores. Having the highest closeness score suggests that the MAPK pathway has the closest relationship with the highest number of other pathways, 27 as well as a high level of “direct and indirect influence” within the network. 14 Highest betweenness score suggests that this node represents a “bottleneck” pathway 27 that may play the role of a “bridge” that can monitor communications between other nodes in the network. 14 Highest degree score suggests that the MAPK pathway is a “hub” pathway that has more local effects on immediate neighborhood nodes. 14 Collectively, these findings suggest that the MAPK pathway has the most significant “global and local influence” within the shortness of breath knowledge network. 27

Consistent with our findings, evidence suggests that MAPK signaling plays a role in lung inflammation and lung injury. 29, 30 Activation of this pathway by cytotoxic drugs, tumor mass, and/or other types of cellular stress leads to proliferation of phagocytes and their influx into the lungs and associated production of chemokines, cytokines, and oxidative stress. 29 These processes contribute to apoptosis of alveolar epithelial cells, alveolar epithelial cell injury, decreases in cell migration in airway smooth muscle, and loss of the pulmonary endothelial barrier. 29 During these remodeling processes, a variety of inflammatory mediators activate vagal afferent neurons in the airways that may result in dyspnea. 2

JAK-STAT pathway

JAK-STAT pathway mediates intracellular messages to induce hematopoiesis and inflammation. 31 Based on its closeness score, it is the second most influential node within the knowledge network. This finding suggests that a variety of cytokines produced may induce pulmonary and systemic inflammation that contributes to shortness of breath. 2, 31 This hypothesis is supported by research that identified associations between dysregulations of JAK-STAT signaling and pulmonary fibrosis. 31 In addition, perturbations in the JAK-STAT pathway were identified as a common mechanism for idiopathic pulmonary fibrosis (IPF)-induced lung cancer. 32

PI3K-Akt pathway

The PI3K-Akt pathway stimulates innate immune responses and mediates the infiltration of immune cells into injured tissues. 33 PI3K-Akt signaling is involved in the pathogenesis of pulmonary fibrosis, a condition associated with dyspnea. 33 Specifically, the PI3K-Akt pathway is involved in epithelial-mesenchymal transition (EMT), epithelial cell senescence, and apoptosis of epithelial cells. 33

NF-kappa B pathway

NF-kappa B pathway is activated by pro-inflammatory cytokines, pattern recognition receptors, and oxidative stress. 34 NF-kappa B signaling induces pulmonary inflammation, coagulation, and airway cellular apoptosis. 34 Dysregulated NF-kappa B signaling is associated with the progression of chronic inflammatory airway disease (e.g., asthma and chronic obstructive pulmonary disease). 34 In a murine model of cardiopulmonary bypass-induced lung injury, 35 interactions between the NF-kappa B and PI3K/Akt signaling co-modulated pulmonary apoptosis.

FoxO pathway

FoxO pathway is involved in the regulation of apoptosis, oxidative stress, and cytokine release in a wide variety of immune cells. 36 While stimulated by MAPK pathway, the PI3K-Akt pathway inhibits FoxO signaling. 36 In a study that compared lung fibroblasts from patients with IPF to those from healthy donors, 37 a decrease in FoxO3 activity was found in the fibroblasts of patients with IPF. In addition, FoxO3 knockout mice with lung fibrosis, who received a bleomycin challenge, had an increase in pulmonary fibrosis and decreased lung function. 37

Immune System

Immune cells participate in innate and adaptive immune responses. 28 Perturbed immune system pathways with the highest closeness scores included: NK cell-mediated cytotoxicity, NET formation, NOD-like receptor signaling, and chemokine signaling.

NK cell-mediated cytotoxicity pathway

Evidence suggests that NK cells play important roles in lung inflammation. 38 Recruitment of NK cells to inflamed lung tissues produces multiple cytokines and chemokines and causes damage to lung tissues. 38 These changes in lung structure result in impaired gas exchange and the development of shortness of breath.

NET formation pathway

NETs are formed through lytic NETosis, that involves the loss of cell membrane activity. 39 Microorganisms and/or endogenous stimuli (e.g., damage-associated molecular patterns (DAMPs)) trigger NET formation. 39 During inflammation, neutrophils that infiltrate lung tissue undergo NET formation that results in the production of reactive oxygen species (ROS) and pro-inflammatory cytokines. This process plays a central role in the development of lung injury. 39

NOD-like receptor signaling pathway

NOD-like receptors are one type of pattern recognition receptors. 40 NOD-like receptors are involved in innate immunity by detecting DAMPs that are associated with cellular stress and intracellular pathogen-associated molecular pattern molecules (PAMPs) that enter the cell through phagocytosis as pattern recognition receptors. 40 These intracellular receptors activate various biological pathways; secrete numerous pro-inflammatory cytokines; induce neutrophil influx into the lungs; and induce apoptosis. 40

Chemokine signaling pathway

Interactions between chemokines and their receptors lead to activation of intracellular signaling and subsequent chemotactic migration of lymphocytes into inflamed lung tissue. 41 Infiltration of inflammatory and immune cells in lung tissues is the main pathogenic characteristic of asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, and acute lung injury. 41 Dysregulation of chemokine signaling in the lungs induces inflammation and progressive lung damage 41 and may contribute to the occurrence of dyspnea.

Signal Molecules and Interaction

Signal molecules transmit information between the cells of multicellular organisms. 28 In this analysis, the cytokine-cytokine receptor interaction pathway was perturbed.

Cytokine-cytokine receptor interaction

Cytokines are signal molecules that act as intercellular messengers and mediate cell growth, differentiation, and apoptosis. 42 Cytokine-cytokine receptor interactions in the lungs play an important role in tissue repair in response to lung injury and/or infection. 42 Evidence suggests that the dysregulation of cytokine production leads to profound lung injury, remodeling, and consequently fibrosis, 42 that may result in shortness of breath.

Cell Growth and Death

Cell proliferation and death are regulated to maintain tissue homeostasis. 28 In this analysis, the cell growth and death pathways that were identified included: apoptosis, necroptosis, and cell senescence. Among these pathways, the apoptosis pathway had the highest closeness score.

Apoptosis pathway

Apoptosis maintains the balance between cell growth and death. 43 Lung epithelial cell apoptosis occurs in response to lung damage in a variety of lung diseases. 43 Recruited or resident macrophages within the lung induce apoptosis in response to cellular or mechanical stress, epithelial injury, exposure to toxic molecules, and/or infectious agents. 43 While apoptosis is a regulated cell death mechanism that eliminates unwanted cells, failure to remove apoptotic cells by phagocytes leads to release of ROS and DAMPs that can induce epithelial and endothelial dysfunction. 43 Given that prolonged inflammation and delays in repair processes lead to lung damage and irreversible lung remodeling, 43 perturbations in apoptosis may contribute to the development of dyspnea.

Overlapping Mechanisms

Of the 29 inflammatory pathways listed on Table 2, 14 of them were found in the five respiratory disease-related pathways listed in Table 5. Of note, the JAK-STAT, MAPK, apoptosis, and NOD-like receptor pathways were found across at least three respiratory disease-related pathways. While these preliminary results need to be interpreted with caution, the relatively high prevalence rates for shortness of breath in these respiratory conditions (e.g., 34.3% in patients with COVID-19 and 59.3% in influenza 44) support the hypothesis that common inflammatory mechanisms contribute to the occurrence of this symptom. Future studies need to identify distinct shortness of breath profiles in patients with common respiratory conditions and evaluate for perturbations in inflammatory pathways.

Table 5.

Overlap Between Perturbed Inflammatory Pathways Associated with the occurrence of SOB in Oncology patients and inflammatory pathways identified in KEGG pathway maps for common respiratory disease-related pathways

Inflammatory Pathways-associated with SOB in oncology patients Respiratory Disease-related Pathways Associated with SOB in Oncology Patients
COVID-19 pathway Influenza A pathway Tuberculosis pathway Pertussis pathway Asthma pathway
JAK-STAT signaling pathway x x x x
NOD-like receptor signaling pathway x x x
MAPK signaling pathway x x x
Apoptosis x x x
Antigen processing and presentation x x
Endocytosis x x
RIG-I-like receptor signaling pathway x x
Natural killer cell mediated cytotoxicity x
Cytosolic DNA-sensing pathway x
Fc gamma R-mediated phagocytosis x
Complement and coagulation cascades x x
Platelet activation x
Neutrophil extracellular trap formation x
Cytokine-cytokine receptor interaction x
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Abbreviations: DNA = deoxyribonucleic acid, JAK-STAT = Janus Kinase/Signal Transducers and Activators of Transcription, KEGG = Kyoto Encyclopedia of Genes and Genomes, MAPK = mitogen-activated protein kinase, NOD = nucleotide-binding and oligomerization domain, RIG-I = Retinoic acid-inducible gene-I-like receptors; SOB = shortness of breath

LIMITATIONS

Given that this study is the first to report on associations between shortness of breath and pathway perturbations, these findings warrant confirmation. Longitudinal studies are needed that assess for associations between changes in the severity and distress of dyspnea and pathway perturbations.

CONCLUSIONS

While our findings warrant confirmation, we hypothesize that the mechanisms that underlie dyspnea in oncology patients may be similar to other inflammatory respiratory diseases. Comparative studies across various lung diseases would allow for the identification of common and distinct mechanisms for this symptom. In addition, because inflammatory pathways associated with shortness of breath can be triggered by a variety of stimuli, future research needs to examine the mechanisms that underlie the relationship between dyspnea and these triggers. Research that utilizes lung tissue may allow the identification of local effects of inflammatory pathways in the development of dyspnea in oncology patients.

Supplementary Material

Sup Figure 1
Sup Table 1
Sup Table 2

Acknowledgements:

Anatol Sucher and Brian Yuen managed the storage and processing of the biospecimens. This study extends work previously published as part of Joosun Shin’s PhD dissertation.

Funding:

This study was supported by grants from the National Cancer Institute (CA134900 and CA233774). Ms. Shin was supported by research grants from Oncology Nursing Foundation and International Society of Nurses in Genetics. Dr. Olshen is supported by grants UCSF NCI Cancer Center Support Grant (CA082103) and UCSF-CTSI (NCATS UL1 TR000004). Dr. Miaskowski is an American Cancer Society Clinical Research Professor. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Appendix A

METHODS

Patients and Settings

This study is part of a larger, longitudinal study of the symptom experience of oncology outpatients. Eligible patients were ≥18 years of age; had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and provided written informed consent. Patients were recruited from two Comprehensive Cancer Centers, one Veteran’s Affairs hospital, and four community-based oncology programs.

Study Procedures

The study was approved by the Institutional Review Board at the University of California, San Francisco and at each of the study sites. Eligible patients were approached in the infusion unit during their first or second cycle of chemotherapy by a member of the research team to discuss study participation and obtain written informed consent. Of the 2234 patients approached, 1343 consented to participate (60.1% response rate). The major reason for refusal was being overwhelmed with their cancer treatment.

Of these 1343 patients, 1338 reported the occurrence of shortness of breath a total of six times over two chemotherapy cycles (i.e., prior to chemotherapy administration, approximately 1 week after chemotherapy administration, and approximately 2 weeks after chemotherapy administration). All of the other measures and blood for ribonucleic acid (RNA) were collected at the enrollment assessment (i.e., prior to the second or third cycle of chemotherapy). For this analysis, a total of 717 patients provided a blood sample (Supplemental Figure 1). Of these 717 patients, 357 had their samples processed using RNA sequencing (i.e., RNA-seq sample) and 360 had their samples processed using microarray (i.e., microarray sample) technologies.

Instruments

Demographic and clinical characteristics

Patients completed a demographic questionnaire, Karnofsky Performance Status (KPS) scale,[1] Self-Administered Comorbidity Questionnaire (SCQ),[2] and Alcohol Use Disorders Identification test (AUDIT).[3] Toxicity of the chemotherapy regimen was rated using the MAX2 index.[4] Medical records were reviewed for disease and treatment information.

Shortness of breath measure

Shortness of breath item from the Memorial Symptom Assessment Scale (MSAS) was used to assess for the occurrence of the symptom at each of the six assessments.[5]

Data Analysis

Latent class analysis

As reported previously,[6] LCA was used to identify unobserved subgroups of patients (i.e., latent classes) with distinct shortness of breath profiles over the six assessments. Before performing the LCA, patients who reported the occurrence of shortness of breath for ≤1 of the six assessments were identified and labeled the “None” class (n=943, 70.5%). Then, the LCA was performed on the remaining 395 patients using MPlus™ Version 8.4.[7] A three-class solution was selected because this solution fit the data better than the 2-class solution. For the current analysis, using an extreme phenotype approach, an evaluation of differentially perturbed pathways between patients in the None and High shortness of breath classes was performed.

Imputation process

Missing data for demographic and clinical characteristics were imputed by the k-nearest-neighbors method, with k=9. For continuous variables, the Euclidean distance was used to find the nearest neighbors. The imputed value was the weighted average of the nearest neighbors, with each weight originally exp(-dist(x,j)), after which the weights were scaled to one. For categorical variables, distance was 0 if the predictor and the neighbor had the same value and 1 if they did not. The imputed value was the mode of the nearest neighbors.

Demographic and clinical data

Demographic and clinical data from the two patient samples (i.e., RNA-seq, microarray) were analyzed separately. Differences in demographic and clinical characteristics between the patients in the None and High shortness of breath classes were evaluated using parametric and non-parametric tests. Significance corresponded to a p-value of <.05.

In order to not overfit the regression models, a select number of significant demographic and clinical characteristics were included using the smaller sample sizes for the two analyses.[8, 9] These variables were selected based on previous evidence of an association with dyspnea.[10] For the RNA-seq sample, four variables were included (i.e., KPS score, SCQ score, hemoglobin level, cancer diagnosis). For the microarray sample, three variables were included (i.e., KPS score, SCQ score, cancer diagnosis). Demographic and clinical characteristics included in the final model were selected using a backwards stepwise logistic regression approach based on the likelihood ratio test (LRT). The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to gauge the overall adequacy of the logistic regression model for each sample.[11] All of these analyses were performed using R (version 4.2.1).[12]

Differential expression and pathway impact analyses (PIA) and knowledge network construction

The gene expression and PIA were performed based on our previous experience.[13, 14] In brief, differential expression was quantified using empirical Bayes models that were implemented separately using edgeR [15] for the RNA-seq sample and limma [16] for the microarray sample. These analyses were adjusted for demographic and clinical characteristics that remained significant in the final logistic regression model. In addition, the models included surrogate variables to adjust for variations due to unmeasured sources.[17, 18] Expression loci were annotated with Entrez gene identifiers. Gene symbols were derived from the HUGO Gene Nomenclature Committee resource database.[19] The differential expression results were summarized as the log fold-change and p-value for each gene. Only genes that had a common direction of expression across the two samples were retained for subsequent analyses. Common genes were matched using gene symbol.

The PIA included potentially important biological factors (e.g., gene-gene interactions, flow signals in a pathway, pathway topologies), as well as the magnitude (i.e., log fold-change) and p-values from the differential expression analysis for each sample.[20] The PIA included the results of the differential expression analyses for all of the genes (i.e., cutoff free) that had a common direction of differential expression to determine probability of pathway perturbations (pPERT) using Pathway Express.[21] A total of 222 signaling pathways were defined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.[22] For each sample, a separate test was performed for each pathway. Next, Fisher’s Combined Probability method was used to combine these results to obtain a single test (global) of the null hypothesis.[23, 24] The significance of the combined transcriptome-wide PIA was assessed using a false discovery rate (FDR) of 0.025.[25] Then, the KEGG Orthology was used to classify the perturbed pathways related to inflammatory mechanisms (i.e., signal transduction, immune system, signal molecules and interaction, transport and catabolism, cell growth and death, and cell motility).[26]

Next, an unweighted knowledge network was created based on interconnections among the inflammatory pathways using KEGG pathway maps. A knowledge network is a multi-edge graph that combines heterogeneous information from several sources; provides information about the nature and degree of interactions between and/or among nodes; and allows for the identification of nodes that have structural importance.[27] Nodes were defined as perturbed inflammatory signaling pathways identified in this analysis. Edges were defined as shared member(s) identified in KEGG pathway maps.

To gain insights into the structural importance of each node, scores for three centrality measures (i.e., betweenness, closeness, degree) were calculated using Cytoscape (version 3.9.1).[28] Betweenness refers to the number of shortest paths going through a node. Closeness refers to the distance between nodes. Degree refers to the number of edges connected to a node.[29, 30]

Footnotes

Competing interests: Dr. Wong reported conflicts of interest outside of the submitted work: An immediate family member is an employee of Genentech with stock ownership and Dr. Wong receives royalties from UpToDate. The remaining authors have no conflicts of interest to declare.

Ethical Approval: This study was approved by the Committee on Human Research at the University of California.

Consent to participate: This study was exempted from written informed consent.

Consent for publication: All of the authors read the final version of the paper and approved its submission for publication.

Availability of data and materials:

Data will be provided to the publisher after they obtain a material transfer agreement from the University of California, San Francisco. Individuals who would like a copy of the survey can contact the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Sup Figure 1
NIHMS2026841-supplement-Sup_Figure_1.docx (57.1KB, docx)
Sup Table 1
NIHMS2026841-supplement-Sup_Table_1.docx (37KB, docx)
Sup Table 2
NIHMS2026841-supplement-Sup_Table_2.docx (37.5KB, docx)

Data Availability Statement

Data will be provided to the publisher after they obtain a material transfer agreement from the University of California, San Francisco. Individuals who would like a copy of the survey can contact the corresponding author.

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