Advancing prognostic precision in gastric cancer with an immunoinflammatory index

Abstract

Gastric cancer remains a major global health challenge with high morbidity and mortality rates. Recent advancements in immunology and inflammation research have highlighted the crucial roles that these biological processes play in tumor progression and patient outcomes. This has sparked new interest in developing prognostic biomarkers that integrate these two key biological processes. In this letter, we discuss the recent study by Ba et al, which proposed a novel prognostic immunoinflammatory index for patients with gastric cancer. We underscore the importance of this research, its potential impact on medical practice, and the prospective avenues for further investigation in this rapidly emerging area of study.

Key Words: Gastric cancer; Immunoinflammatory index; Prognostic biomarkers; Inflammation; Immunology

Core Tip: This letter discusses the study by Ba et al on the development of a novel prognostic index for gastric cancer by integrating immune-inflammatory biomarkers and lymphocyte subsets. This index is closely associated with tumour characteristics and can serve as an independent prognostic factor for both progression-free survival and overall survival. This innovative approach holds promise for improving patient stratification, personalizing treatment, and ultimately enhancing patient outcomes.

TO THE EDITOR

Gastric cancer (GC), the fifth most common malignancy worldwide, poses a significant challenge for both clinical practitioners and scientific investigators[1-3]. Despite significant advancements in surgical techniques, chemotherapy, and targeted therapies, the prognosis for many patients remains poor due to advanced stage diagnosis, postoperative recurrence, and metastasis[4-6]. In clinical practice, the tumour-node-metastasis staging system is widely regarded as the cornerstone for prognosis prediction and treatment decision-making in patients with GC[4,7]. However, due to the complexity and heterogeneity of GC, this system often falls short in accurately predicting the prognosis of individual patients[8]. This situation underscores the urgent need for more accurate prognostic tools to better predict patient outcomes and tailor treatments accordingly.

Emerging technologies, such as liquid biopsies, which analyse blood components including neutrophils, lymphocytes, circulating tumor DNA (ctDNA), and other parameters, offer promising noninvasive approaches for monitoring disease progression and real-time response to therapy[9]. The ctDNA fragments are released into the bloodstream by tumour cells and can provide direct information about the tumour, such as mutations and genetic variations[10]. Neutrophils and lymphocytes can reflect the immune status and the body’s response to tumours, as inflammation influences immune modulation, affecting both tumour cells and their microenvironment and thereby impacting disease-related outcomes[11]. Previous studies have measured inflammation and the immune response in GC patients via biomarkers based on the ratio of two blood components, i.e., the platelet-to-lymphocyte ratio (PLR), the lymphocyte-to-monocyte ratio, the prognostic nutritional index (PNI), and the neutrophil-to-lymphocyte ratio (NLR)[12]. After verification, a higher NLR is correlated with the longitudinal trend of ctDNA and is associated with a poorer prognosis in patients receiving immune therapy[13]. Researchers have subsequently integrated three blood components to develop biomarkers for GC, such as the systemic immune-inflammatory index (SII) and the systemic inflammation response index (SIRI)[14,15]. The difference between the two is that the SIRI takes the monocyte count into account, whereas the SII focuses on the platelet count. The above immune-inflammatory biomarkers have been successfully applied to the prognostic assessment of GC, but their prediction accuracy still needs improvement[16].

Recently, research on the diagnosis of GC via a combination of the PLR, NLR, and SII has been conducted, resulting in significantly greater area under the curve (AUC) values than those of the PLR, NLR, and SII alone[17]. Additionally, the grading system SII-PNI, which combines the PNI and the SII, has demonstrated promising potential in predicting tumour response and prognosis in locally advanced GC patients[18]. To more accurately predict the prognosis of stage III GC patients, a novel scoring system called inflammation, nutritional status, and clinicopathological parameters, which integrates inflammation, nutritional status, and clinicopathological parameters has also been developed[19]. However, this integration considers the total lymphocyte count but fails to account for the various peripheral lymphocyte subsets, which are crucial for the circulating immune response. The absolute counts of these subsets have been used as indicators of immune function[20]. Since impaired immune function may be associated with postoperative recurrence of GC, incorporating lymphocyte subsets could provide a more accurate prediction of clinical prognosis and recurrence in postoperative GC patients[21].

DEVELOPMENT AND VALIDATION OF THE PROGNOSTIC IMMUNOINFLAMMATORY INDEX

To improve the prognosis of GC patients, Ba et al[22] proposed a new prognostic immunoinflammatory index (PII) by comprehensively analysing immune-inflammatory biomarkers and lymphocyte subsets. Additionally, the contribution of each variable to the prognosis of GC patients was quantified via LASSO-Cox regression. Specifically, 12 immune-inflammatory biomarkers and lymphocyte subsets, such as the NLR, SIRI, CD3(+), CD3(+)CD4(+), and CD19(+), were collected from 291 GC patients who underwent surgical treatment. CD3, CD4, and CD19 are key surface markers of immune cells that are widely used in clinical immunology and haematology, particularly for assessing immune function and monitoring treatment efficacy[23,24]. The values of CD3(+), CD19(+), and CD3(+)CD4(+) were obtained by calculating the percentage of cells expressing CD3 and CD19, as well as the cells co-expressing CD3 and CD4 across the total cell population. Subsequently, 9 variables with a P value of less than 0.05 in univariate Cox regression were further included in the LASSO-Cox regression analysis. Finally, the PII score was established on the basis of the 7 variables with nonzero regression coefficients: PII score = 0.608 × NLR + 0.277 × PLR + 0.059 × SIRI + 0.480 × CD3(+) + 0.176 × CD3(+)CD16(+)CD56(+) - 0.459 × CD4(+)CD8(+) - 0.501 × CD19(+).

According to the PII scoring equation, the NLR and CD19(+) are the two variables with the highest absolute values among all the coefficients, indicating that immune-inflammatory biomarkers and lymphocyte subsets play crucial roles in disease progression and prognosis. The validity of the PII was confirmed through time-dependent AUC and decision curve analysis, underscoring its potential for precise patient stratification and informed treatment decisions. Importantly, the nomogram for predicting progression-free survival and overall survival, which was constructed with the PII, had a greater concordance index and superior 1, 3, and 5-year AUC values compared with the tumour-node-metastasis staging system.

To further compare the similarities and differences between the PII proposed by Ba et al[22] and other indices. Table 1 presents the calculation rules for each index. In addition to the PII and PNI, the other indices directly operate on the variables without assigning specific weights. Furthermore, both the SII-PNI and the inflammation, nutritional status, and clinicopathological parameters determine the scores of each biomarker on the basis of thresholds and assess the patients’ conditions through total scores. Notably, among all prognostic indices for GC, only the PII takes lymphocyte subsets into account. It is important to emphasize that incorporating more variables does not necessarily lead to better outcomes, as redundancy may exist among them. To address this issue, Ba et al[22] employed LASSO-Cox regression in the variable selection process, ensuring the provision of more comprehensive information while reducing variable redundancy.

Table 1 Calculation rules for prognostic immunoinflammatory index and other indices.

Index	Calculation rule
PLR	Platelet count/lymphocyte count
LMR	Lymphocyte count/monocyte count
PNI	Albumin + 5 × lymphocyte count
NLR	Neutrophil count/lymphocyte count
SII	Neutrophil count/lymphocyte count × platelet count
SIRI	Neutrophil count × monocyte count/lymphocyte count
SII-PNI	High SII (≥ 471.5) and low PNI (≤ 48.6) were scored as 1, and the rest of the values were scored as 0
INPS	Low BMI (< 23 kg/m2), low prealbumin (< 180 mg/L), high NLR (≥ 2.7), high PLR (≥ 209.4), low LMR (< 2.8), and low PNI (< 45.1) were scored as 1, and the rest of the values were scored as 0
PII	0.608 × NLR + 0.277 × PLR + 0.059 × SIRI + 0.480 × CD3(+) + 0.176 × CD3(+)CD16(+)CD56(+) - 0.459 × CD4(+)CD8(+) - 0.501 × CD19(+)

PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; PNI: Prognostic nutritional index; NLR: Neutrophil-to-lymphocyte ratio; SII: Systemic immune-inflammatory index; SIRI: Systemic inflammation response index; INPS: Inflammation, nutritional status, and clinicopathological parameters; BMI: Body mass index; PII: Prognostic immunoinflammatory index.

CLINICAL IMPLICATIONS OF THE PII IN GC

The proposed blood-based PII is of significant importance for clinical practice, as the variables required for its calculation are easily accessible, and the nomogram constructed using the PII has proven to be a robust prognostic prediction model. PII has also been shown to be significantly associated with several clinical characteristics, such as body mass index, primary tumour site, tumour size, and carbohydrate antigen 199. It has the potential to enhance patient stratification, allowing clinicians to identify individuals at greater risk for disease progression or recurrence. This, in turn, could facilitate earlier and more aggressive intervention strategies, potentially improving survival outcomes. Moreover, the PII may serve as a valuable tool for monitoring treatment response. By tracking changes in PII over time, clinicians can gain insights into the effectiveness of various therapies and adjust treatment plans accordingly.

FUTURE DIRECTIONS AND CHALLENGES

While the PII represents a promising prognostic tool for GC, several challenges and avenues for future research remain. First, further validation studies in larger, more diverse patient cohorts are needed to confirm the generalizability and robustness of the PII. Second, the underlying mechanisms linking immune and inflammatory responses to GC prognosis remain largely unclear. Future research should aim to elucidate these mechanisms, providing a more comprehensive understanding of the host-tumour interaction. Finally, the integration of the PII with other clinical and molecular markers may enhance its predictive accuracy, enabling even more precise patient stratification and treatment personalization.

CONCLUSION

The development and validation of the PII by Ba et al[22] represent a significant contribution to the field of GC research. This index offers a novel approach for assessing patient prognosis and has the potential to inform clinical decision-making. However, further validation and exploration of its underlying mechanisms are needed to fully realize its clinical potential.