Introduction
Gastrointestinal cancers (GIC) account for over one-quarter of all cancer cases and one-third of cancer-associated deaths worldwide [
1]. Although there has been great advancement in the treatment of GIC, the outcome for the majority of GIC patients remains poor [
2]. Thus, exploring a reliable prognostic index for patient survival can enable physicians to adopt better therapeutic and preventative measures.
Numerous studies in recent years have confirmed that systemic inflammation is crucial for the development and growth of GIC [
3,
4]. A variety of inflammatory cells and proinflammatory cytokines are activated in the early stages of carcinogenesis, which promote the creation of lymphatic ducts and new blood vessels, causing a pro-cancer microenvironment for growth and differentiation [
5]. At later stages, cancer-related inflammation can impair immune cell function, creating a conducive environment for metastasis [
6]. Thus, inflammatory indicators are anticipated to be important prognostic biomarkers in cancer. For instance, an elevated neutrophil-to-lymphocyte ratio (NLR) is linked to a weak immunological response and a high inflammatory response [
7‐
9]. In cancer patients, the nutritional status of the body is also closely associated with tumor development and clinical outcome. Some common nutritional indicators have been shown to have a high prognostic significance in cancer, such as body mass index (BMI) [
10] and serum albumin level [
11].
Recently, the advanced lung cancer inflammation index (ALI), a new inflammatory marker that is calculated as BMI (kg/m2) × albumin (g/dL)/NLR, was initially found to be a useful prognostic index in lung cancer [
12]. ALI is thought to reflect systemic inflammation better than other biomarkers due to combining the indicators of nutrition and inflammation. To date, some retrospective articles have analyzed the association between ALI and prognosis in GIC patients. However, there has not been a systematic evaluation of whether ALI is a reliable predictive factor for GIC patients. Thus, we conducted the first meta-analysis to identify the predictive significance of pre-treatment ALI in GIC patients, which may help to determine prognosis and formulate an effective treatment strategy that will further minimize mortality.
Methods
Literature search strategies
The current meta-analysis accompanied the PRISMA statement [
13]. The protocol for this meta-analysis was available in PROSPERO (CRD42022371374). On December 29, 2022, PubMed, EMBASE, and the Cochrane Library were retrieved using the keyword: “advanced lung cancer inflammation index [All Fields]”. We further searched Google Scholar for grey literature. Additionally, we manually retrieved the reference lists of the publications that qualified.
Inclusion and exclusion criteria
If studies met all the following criteria, they were included: patients diagnosed with GIC; research evaluated the prognostic value of ALI; provided at least one of the outcomes [overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and cancer-specific survival (CSS)]. The conference abstracts, case reports, or comments were excluded.
Data extraction and quality assessment
Data extraction mainly focused on the author, year, study region, study design, study period, sample size, the number of male and female patients, cancer types, treatment, follow-up duration, cut-off, and outcomes. The Newcastle–Ottawa Scale (NOS) score was utilized to evaluate the quality of the observational studies. High-quality literature was defined as having a score above six. All of the above steps were double-checked by Lilong Zhang and Kailiang Zhao, and any disparities were addressed by Weixing Wang and Wenhong Deng.
Statistical methods
Statistical analysis was conducted by Stata 15.0. The statistical heterogeneity was calculated using the chi-squared test.
P < 0.1 and I
2 > 50% indicated high heterogeneity, so a random effect model was applied; otherwise, the fixed effect model was used. The tests of Egger’s and Begg’s were employed to evaluate publication bias. If there was significant publication bias, we used the trim-and-fill method to modify the results [
14]. Sensitivity analysis was implemented to assess the stability of the results by excluding each study independently.
Discussion
Our goal was to explore the predictive significance of ALI in GIC patients, and the pooled data demonstrated that a lower ALI was remarkably related to shorter OS, DFS, and PFS. Furthermore, these results held steady even after sensitivity analysis and subgroup analysis. This is the first meta-analysis to thoroughly explore the impact of ALI on the prognosis of GIC patients. As an extremely accessible indicator in clinical practice, pre-treatment assessment of patients’ ALI can help physicians more effectively and easily predict clinical outcomes and assist them to adjust treatment in a timely manner, thereby further reducing mortality. However, it is worth noting that our results also found that ALI levels were not associated with CSS in patients with GIC. Considering that this index (including PFS) only integrated the data of two studies, it may lead to instability in the results, which need to be further confirmed by subsequent studies.
Both the systemic inflammatory response and nutritional state are recognized prognostic factors in cancer patients, and mounting research has shown a close relationship between the systemic inflammatory response and nutritional status in various cancers [
33]. Furthermore, the latest view is that systemic inflammatory response via host-tumor interaction is now considered to be the 7th hallmark of cancer [
34]. Systemic inflammatory response and nutritional status have been assessed using a variety of blood examination-based derivatives up to this point, such as NLR [
35], platelet-lymphocyte ratio (PLR) [
36,
37], prognostic nutrition index (PNI) [
38], BMI [
39], and albumin [
40], and a number of lines of research have shown that these derivatives have the potential to be employed by patients with malignancies as prognostic markers [
35‐
40].
The ALI is a newly defined cancer index, and one of its unique features is as a composite index combining the nutritional state and the inflammatory state [
12]. Deng et al
. confirmed the predictive ability of the ALI for 5-year OS and 5-year DFS was better than that of the PNI or systemic inflammation index (SII) in CRC patients [
20]. Some studies also found that ALI was superior to albumin, NLR, and BMI in predicting complications, 5-year PFS, and 5-year OS in CRC and OCC patients [
17,
22]. Interestingly, Wu et al
. revealed that ALI outperformed NLR, PLR, monocyte-lymphocyte ratio (MLR), SII, and PNI in predicting OS and DFS in patients with cholangiocarcinoma by using time-dependent ROC analysis [
16]. Thus, the ALI may have a higher discriminating value compared to other biomarkers. Taking all the current evidence together, our study found that ALI predicted a poor prognosis in patients with GIC, and the results held true in gastric, oesophageal, and colorectal cancers, according to subgroup analysis.
Surely, this analysis still has some limitations. The absence of ALI dynamics' evaluations, rather than the use of a single time-point value, is a significant limitation. The absence of a correlation between interleukins, chemokines, and ALI prevents us from elucidating the mechanistic relationship between ALI values and clinical outcomes. The use of various salvage maneuvers may, by chance, have altered the results in favor of one group depending on the opportunities at the treatment center. The vast majority of articles were retrospective cohort studies, which possibly limited their statistical power. There is a lack of uniformity in the cut-off values for ALI across studies, and aggregated survival results may deviate from the actual values. Thus, in order to confirm and update our conclusion, more high-quality studies with sizable sample sizes, particularly multicentre RCTs, were urgently required. At the same time, these studies should also include patients of different races and explore the optimal cut-off values to guide the clinic more precisely for the benefit of patients.
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