Cancer cachexia alone may account for more than 20% of deaths in patients. Cachexia is present in 15 to 40% of cancer patients affecting approximately 80% of patients with advanced illness (Mondello et al. 2014). In the current study, 60% of the patients presented with cachexia, 17.5% with refractory cachexia and 9.5% with pre-cachexia results that are consistent with those described in the literature and therefore, the metabolic derangements associated with cancer cachexia were expected to be present.

Ideal cachexia scoring should include facets of inflammation, weight loss, sarcopenia and appetite for optimal classification (Zhou et al. 2018), and not percentage weight loss and BMI exclusively (Crawford 2019). Cachexia staging identifies periods of stability, where the potential for reversal of muscle loss (even 90 days preceding death) (Prado et al. 2013) is possible and detects patients who may respond to nutritional intervention during this “window”, enabling nutritional and medical goal-directed treatment, that is, interventions affecting prognosis versus palliative care (Ozorio et al. 2017).

Sarcopenia, measured by bioelectrical impedance analysis (BIA) identifies patients with, or at risk of, developing cachexia (Crawford 2019). The presence of sarcopenia observed in this group of patients (42% for SMI versus 60% for HGS) would place them at greater risk for cachexia. BMI showed that 35% of the patients were overweight and 10% were obese, therefore, masking the underlying sarcopenia. Sarcopenia, independently, is prognostic for lower survival in obese patients with cancer (Martin et al. 2015), therefore, its assessment is crucial for a thorough nutritional assessment.

Haemoglobin (Hb) and serum albumin have been extensively investigated as biomarkers of malnutrition in cancer patients (Martin et al. 2015; Bullock et al. 2020; Knight et al. 2004). Decreased serum albumin reflects lean tissue loss, an increased systemic inflammatory response and may be a negative prognostic factor for survival in various primary cancer diagnoses (Bullock et al. 2020). The significantly lower albumin value of the patients versus that of the controls’, is suggestive of the advanced nature of the cachexia in this study. Additionally, the significantly higher presence of anaemia where, 78% of the patients were anaemic, is similar to that described in the literature (30–90% of patients are anaemic). The significantly lower Hb in this study renders Hb a reliable biomarker, indicative of the extent of inflammation and cachexia, that may be caused by a plethora of factors (Madeddu et al. 2018).

Neutrophils, lymphocytes and platelets play a role in systemic inflammation, where their respective ratios, provide valuable insights regarding prognosis, staging and metastasis (Yang et al. 2018). Elevated blood neutrophil counts and reduced lymphocyte counts are typical in inflammation (Dupré and Malik 2018). The results in the current study did not reflect this: neutrophil levels were not raised in the cases; however, lymphocyte counts were significantly lower, in keeping with the presence of inflammation. Numerous factors may affect neutrophil counts, including blood volume, ethnicity, age, extent of disease, treatment status with administration of chemotherapy drugs and smoking status, some of which were not controlled for in this study (Wu et al. 2020).

Platelets may promote tumour growth and metastasis (Gianazza et al. 2020) and may also predict survival in cancer cachexia. Wang et al. showed that a platelet count > 261 × 109/L was associated with a reduced overall survival (Wang et al. 2020). Comparatively, the mean platelet count of the patients in this study was 300 × 109/L, indicating advanced cachexia and disease, supporting emerging data on the diagnostic potential of platelet counts in cancer cachexia (Haemmerle et al. 2018).

Increased NLR and PLR are correlated with a decreased overall survival and cancer-specific survival (Dupré and Malik 2018) and a raised SII may indicate a poor prognosis in various malignancies (Hu et al. 2014), being potentially more reliable than NLR and PLR (Hirahara et al. 2020). NLR, PLR and SII were all significantly higher for the patients compared to the controls in this study further supporting the evidence of the advanced nature of the disease.

For the patients, investigational markers (CRP, IL-6, IL-8 and TNFα) all were significantly higher in relation to the reference values obtained for ROC curve analysis. These results underpin the protein depletion evident in the patients, with these biomarkers driving the nutritional deterioration. Tumour necrosis factor alpha plays a role in muscle protein breakdown, raised IL-6 affects muscle mass (Carson and Baltgalvis 2010), and an overexpression of IL-8 is correlated with metastases and advanced disease (Alfaro et al. 2017)—synonymous with a reduced nutritional status. Associations of these cytokines to sarcopenia is central to cachexia management in the early stages, guiding clinicians to direct treatment plans as aggressive or palliative (Paczek et al. 2020), dependent on how advanced the disease is as reflected by the biomarkers.

Research that documents raised CXCL5 levels may be questionable due to inconsistencies in its measurement (Binwu 2018), challenging the reliability of CXCL5 as a prognostic marker for cancer (Hu et al. 2018). Plausible explanations for differences found in the reporting levels of CXCL5 levels include: measurement of tissue samples versus serum levels; biomarker measurement in peripheral circulation versus blood surrounding the tumour samples; tumour stage [CXCL5 levels are dependent on stages and not tumour size or mass (Lim and Chung 2015)]; the extent of metastasis when measurement occurred (Hu et al. 2018); small sample sizes (Lee et al. 2018). These areas of inconsistency are not standardized across all literature, questioning the prognostic value of CXCL5 expression in cancer patients. These confounding variables emphasize the need for larger and more clearly standardized research approaches for the investigations of CXCL5. The aforementioned factors may account for the absence of increased CXCL5 found in the patients compared to the control individuals and “poor performance” of CXCL5 in the ROC curve analysis (AUC 0.59) encountered in the current study.

Additionally, the timing and trajectory of the metastatic process in which the blood samples were collected may be relevant. Reports show that when CXCL5 and IL-8 are concurrently depleted, there is a synergistic effect on metastasis, where metastatic activity is increased (Lopez-Lago et al. 2013). Therefore, the rise and fall of CXCL5 and IL-8 may impact results depending on the respective trajectories of the metastatic process. The flux and interplay between biomarkers is not completely understood.

The significantly raised IL-8 and “normal” CXCL5 levels found in the patients in this study could be an indication that the environment was not conducive for enhanced metastatic activity. Potentially, results may have been different if the samples were taken a few weeks earlier or later. This raises the question of the timing in measuring these biomarkers, i.e., in isolation or as consecutive samples at defined time intervals. Biomarker levels could possibly rise and fall in “waves” and “cycles” dependently or independently to drive metastasis, impacting the reliability of these cytokines as cachexia indicators and furthermore, making objective comparisons of the relationships of these markers to sarcopenia between studies challenging.

The results of H3Cit in this study also differed from those reported in the literature. No notable difference was found between patients and control participants, and the AUC for ROC curve analysis was 0.56. In contrast, others have reported raised (three-fold) H3Cit levels in cancer patients (Thålin et al. 2018). This difference may be explained by several factors: choice of methodology used for analysis (serum versus colorectal mucus samples) and single diagnosis as opposed to mixed diagnoses (Loktionov et al. 2019). Furthermore, other studies reported that approximately 20% of cancer patients displayed neutrophil counts of more than double the upper reference limit (Thålin et al. 2018), which was not the case in the current study, possibly explained by differences in stages of chemotherapy of the patients. Neutrophil activation and neutrophil extracellular trap (NET) formation is a probable source of circulating H3Cit in cancer patients (Thålin et al. 2018), perhaps the “normal” levels of circulating H3Cit found in this study are related to the “normal” neutrophil counts shown.

Lack of standardization in cut-offs and reference values further complicates comparisons. Some studies use a cut-off at the 75th percentile (Grilz et al. 2019), but omit ROC curve analysis, while others report high AUC results (0.884) for H3Cit, where H3Cit was investigated as part of the peptidylarginine deiminase 4 (PAD4) complex, inclusive of other markers (Loktionov et al. 2019). Additionally, currently used ELISAs are not standardised for H3Cit measurement (Thålin et al. 2020). These inconsistencies in the measurement and reporting may explain why the outcomes in this study differ from those reported in the literature.

Patient selection may also impact H3Cit outcomes. Studies reporting raised H3Cit focussed primarily on patients in the early stages of their disease (Grilz et al. 2019; Loktionov et al. 2019), therefore, questioning the extent of metastatic disease and presence of cachexia (Thålin et al. 2018). The presence of cachexia in 60% of the patients in the current study may account for the differences shown in this study. Furthermore, H3Cit levels are elevated in diagnoses including strokes, COVID-19 and aortic stenosis. Studies reporting raised H3Cit did not exclude these conditions (Thålin 2018), which too may confound results. Therefore, specific guidelines for future research regarding exclusions for measurement of H3Cit, requires clarification.

“Resistance” and “tolerance” are terms used to describe the underlying physiology and metabolic changes of cancer cachexia stages (Maccio et al. 2021). The resistance phase is an initiation of the immune response to target tumour or cancer cells, accompanied by inflammation. The tolerance phase aims to mitigate the damage caused by the resistance phase. The progression from resistance to tolerance and flux between these phases is not clearly defined, driven by factors that are not fully understood and may impact on the measurements of biomarkers. Therefore, markers that indicate the extent of resistance versus tolerance phases are required before attempting to understand the role of biomarkers that drive metastasis and cachexia. These considerations remain largely unknown complicating standardisation for future research.

Both albumin and Hb showed significant relationships to HGS and protein status, where lower biomarkers levels related significantly to lower HGS, total protein, SMM, and SMI. Additionally, albumin was significantly associated with the presence or absence of sarcopenia. Haemoglobin was shown to be reliable in predicting malnutrition, and conversely sarcopenia was indicative of the presence of anaemia. Similarly, Wang et al. reported significantly lower albumin levels (p < 0.001) in the presence of sarcopenia in patients with advanced cancer (Wang et al. 2020).

Neither NLR, PLR nor SII were found to show significant associations with sarcopenia in the current study using SMI. Similarly, Laing et al. failed to show a significant relationship between NLR (p = 0.630) or PLR (p = 0.529) to sarcopenia, but did show significance for the association of the lymphocyte to monocyte ratio (LMR), p = 0.007 with sarcopenia (Liang et al. 2021). However, other studies have shown NLR (p = 0.011), lymphocyte count (p = 0.002) (Kim et al. 2016), and PLR (p < 0.001) (Chen 2018) (Lin et al. 2018) to be significantly associated with sarcopenia. In the current study, using ROC curve cut-offs to categorize participants for nominal statistical analysis, PLR was significantly correlated with total protein and SMM. No significance was found for NLR and SII using these parameters.

Using nominal category cut-offs, HGS was significantly associated with PLR, but no significance was shown for NLR or SII. Chen et al. showed no significance for HGS with NLR or PLR in their study, even though the same method for the cut-offs for HGS were applied, potentially due to the low AUC score for these biomarkers that was evident in their trial (Chen et al. 2020). Linking inflammation to cachexia status remains inconsistent, possibly attributed to the multiple definitions used to define cancer cachexia or the cuts-offs applied for statistical analysis.

Cut-offs of 10 mg/L for CRP in cancer cachexia typically yield significant relationships of CRP with a reduced lean mass and an increased loss of lean mass (Cordeiro et al. 2020). The current study found no significant associations of CRP with total protein, SMM or SMI; however, CRP was significantly related to sarcopenia and to HGS using the cut-off from the ROC curve analysis for CRP (2.775 mg/l). Perhaps this more “stringent” cut-off that was used from the ROC curve analysis, may explain why no significance was shown for CRP to total protein, SMM or SMI in this study.

For IL-6, and TNFα, the literature supports relationships of these markers with anorexia and skeletal muscle breakdown (Kim et al. 2016). While significantly raised IL-6 (p < 0.0001), TNFα (p < 0.0001), and CRP (p < 0.0001) in malnourished and at-risk for malnutrition patients with colorectal cancer using the mini-nutrition assessment score (MNA) has been reported (Daniele et al. 2017), in gastric and lung cancer patients, IL-6 failed to predict weight loss and sarcopenia, even as its concentration increased. The authors suggested that IL-6 may be more valuable in the early stages of cancer cachexia with increased acute phase proteins causing tissue wasting (Scheede-Bergdahl et al. 2012). In the current study, significant associations of IL-6 to malnutrition were only found using ROC curve cut-offs for the biomarker and categories for the malnutrition assessment. Similarly, applying cut-offs for total protein and SMM, TNFα was the only biomarker found to be significantly associated with protein status, sarcopenia and with the presence or absence of sarcopenia. Furthermore, significant relationships were observed for both IL-6 and TNFα with HGS. In contrast, other research yielded no meaningful relationships between anthropometric measurements and IL-6 when using continuous variables for statistical analyses (Srdic et al. 2016). These differences indicate that stage of disease or statistical methods applied may be factors affecting outcomes and interpretation of results.

Cachexia and sarcopenia definitions may impact outcomes in correlating these measurements to IL-8. Whilst, higher levels of IL-8 have positively correlated with weight loss and sarcopenia in pancreatic cancer [percentage weight loss to define cachexia (> 5%) and computed tomography (CT) images for the diagnosis of sarcopenia] (Hou et al. 2018), the current study found that IL-8 was only significantly related with CSS categories, but not with sarcopenia, HGS, or protein status, potentially explained by differences in cachexia and sarcopenia definitions employed.

The literature supports significantly raised CRP (p = 0.020) and IL-6 (p = 0.040) for patients with cachexia compared to non-cachectic patients (Srdic et al. 2016). Similarly, using CSS scores, the current study found cachexia to be significantly related to CRP, IL-6, IL-8 and TNFα. All relationships were positive, indicating that as inflammation increased, the cachexia status deteriorated. Notwithstanding the poor ROC curve outcomes, H3Cit was significantly related to total protein when cut-offs for protein were applied, showing promise for future investigations for the use of H3Cit in cancer cachexia.

Relationships between CXCL5 and H3Cit to sarcopenia, anthropometric indices and cachexia are unexplored, with this study pioneering an attempt to find relationships in this regard. In this context, CXCL5 and H3Cit are considered relatively new and less well-described biomarkers for cancer cachexia. Inconsistencies in their respective measurements, and how they are integrated into research, requires standardization to enable the potential roles of these emerging biomarkers to be better interpreted from future research.

Although CXCL5 and H3Cit were not found to be reliable markers in cancer cachexia or with respect to their relationships to sarcopenia, this study introduces the paradigm of improving current knowledge of the relationships between the less understood biomarkers of advanced cancer cachexia. For future research, more clearly defined patient groups, with regard to stage of disease, primary diagnosis, presence of metastases, and including a comprehensive cachexia assessment and staging will produce more meaningful comparisons amongst studies and ensure goal-directed treatment for prognosis in cancer cachexia. The current study had limiting components including multiple different primary diagnoses of cases, the relatively small sample size to allow for powerful statistical analyses of smaller groups within the total sample and the diverse cancer treatment that cases were receiving. However, the value of this type of research coveys a message to the broader cachexia research community, to continue investigations to better understand emerging biomarkers, their measurement, together with sarcopenia and cachexia in more uniformly standardized settings.