Potential predictive value of CD8A and PGF protein expression in gastric cancer patients treated with neoadjuvant immunotherapy

doi:10.21203/rs.3.rs-4994678/v1

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Research Article

Potential predictive value of CD8A and PGF protein expression in gastric cancer patients treated with neoadjuvant immunotherapy

https://doi.org/10.21203/rs.3.rs-4994678/v1

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Background

Immunoneoadjuvant therapy has garnered considerable attention owing to significant strides in cancer treatment. We aimed to explore the molecular mechanisms underpinning immunoneoadjuvant therapy through a comprehensive multiomics analysis using samples from a registered clinical trial cohort.

Methods

Preoperative samples were collected from 16 patients, and postoperative samples were obtained from 12 among them. RNA-seq and Olink proteomics were employed to identify key genes before and after neoadjuvant treatment. The weighted coexpression network was constructed using Weighted gene co-expression network analysis (WGCNA). Furthermore, the proportion of infiltrated immune cells was calculated using xCell based on normalized expression data derived from RNA-seq.

Results

Patients were stratified into T1 (good efficacy) and T2 (poor efficacy) groups based on Tumor Regression Grade (TRG) to neoadjuvant immunotherapy. Compared to the T2 group (TRG2 and TRG3), the T1 group (TRG0 and TRG1) showed significant differences in pathways related to inflammatory response and myeloid leukocyte activation. Furthermore, the T1 group exhibited elevated levels of CD8 + T cells and B cells. The top two factors with the highest area under the Receiver Operating Characteristic (ROC) curve were CD8a molecule (CD8A) (1.000) and C-C motif chemokine ligand 20 (CCL20) (0.967). Additionally, the expression of Placenta Growth Factor (PGF) and TNF receptor superfamily member 21 (TNFRSF21) proteins significantly increased compared to the T2 group. High expression of CD8A and PGF were associated with favorable and poor prognosis in gastric cancer patients, respectively. Immunoinfiltration analysis revealed a positive correlation between CD8A and Dendritic Cell (DC) levels, while a negative correlation was observed with Myeloid-derived suppressor cell (MDSC) levels.

Conclusions

Through multiomics analysis, we discovered that CD8A is linked to enhanced treatment response and tumor regression. Conversely, PGF exhibited contrasting effects, hinting at a potential adverse influence on treatment outcomes.

Gastric adenocarcinoma

Neoadjuvant immunotherapy

Sindillimab

Olink proteomics

CD8A

PGF

Gastric cancer remains a significant global health concern, ranking fifth in incidence and fourth in mortality worldwide^[1]. Despite advancements in tumor therapy, the subtle and atypical clinical symptoms of early gastric cancer contribute to over 60% of patients developing local or distant metastasis at the time of diagnosis^[2]. Currently, radical surgical resection can effectively cure the disease in patients with early-stage gastric cancer. However, for those with locally advanced gastric cancer, even with interventions such as radiotherapy and chemotherapy, the 5-year survival rate sharply decreases^[3–5]. Therefore, the need for new treatments becomes particularly imperative.

The combination of immunotherapy and chemotherapy has established itself as the standard first-line treatment for advanced gastric cancer. In the CheckMate-649 trial evaluating Nivolumab, patients treated with combination chemotherapy exhibited significantly longer median overall survival (OS) and median progression-free survival (PFS) compared to those treated with chemotherapy alone^[6]. Given the notable efficacy of immunotherapy in advanced gastric cancer, numerous clinical studies have explored whether incorporating immunotherapy into perioperative treatment can enhance the survival time of patients with locally advanced gastric cancer. A Phase III study, Keynote585, presented at the ESMO meeting in 2023, indicated that increased perioperative immunotherapy improved event-free survival (EFS) and pathological complete response (pCR) rates in patients but did not demonstrate a survival advantage over placebo. Identifying individuals who truly benefit from immunotherapy is a pivotal consideration in its current application during the perioperative period. To address this, we conducted a Phase II clinical study evaluating the perioperative treatment of locally advanced gastric cancer using sintilimab in combination with Fluorouracil, Leucovorin, Oxaliplatin, and Docetaxel (FLOT) (Clinical trial number: NCT04341857). Preliminary results indicate that sintilimab combined with FLOT neoadjuvant therapy achieved an 18.8% pCR rate^[7]. The study also observed variable responses among patients, with some achieving pCR and others not. Currently, much debate is ongoing regarding which indicators serve as reliable predictors of immunotherapy efficacy.

Subgroup analysis revealed that patients with higher Combined Positive Score (CPS) scores of Programmed cell death-Ligand 1 (PD-L1) exhibited a more favorable therapeutic effect, PD-L1 expression levels were significantly higher on CD8 + T cells than on CD4 + T cells. However, the expression of PD-L1 is not exactly consistent with the efficacy of immunotherapy^[8–10]. Blood specimens, offering the advantages of convenience and multiple sampling compared to tissue specimens, have been explored for potential predictive indicators of immunotherapy efficacy. Other studies indicated that cytokines in the blood may also serve as predictors of immunotherapy effectiveness. In our clinical studies, we collect blood samples from patients to identify efficacy indicators. Protein biomarkers are the cornerstones in disease prediction, diagnosis, and prevention. The advent of high-throughput proteomics enables the simultaneous quantification of numerous proteins. However, the vast amount of data acquired poses challenges for analysis, and the potential for false positive results complicates subsequent validation efforts. Recently, Olink technology has gained popularity for providing multiple detection panels targeting various disease processes. Its requirement for small sample volumes is particularly advantageous when clinical samples are limited. Furthermore, it can capture a broad spectrum of proteins across the entire dynamic range (> 10 logs). A previous study demonstrated that Olink proteomics exhibits excellent repeatability and stability in detecting proteins in plasma samples^{[11, 12]}. In the current study, Olink proteomics was employed to identify inflammation-associated proteins that showed differences before and after neoadjuvant therapy, aiming to identify potential markers.

This study employed a comprehensive omics analysis to discern key molecular characteristics associated with the efficacy of sintilimab combined with FLOT neoadjuvant therapy for gastric/gastroesophageal junction adenocarcinoma. This involved analyzing differential proteins before and after neoadjuvant therapy and among different therapeutic groups. The study further investigated the interplay between relevant molecules and immune infiltration, laying the groundwork for the clinical treatment and efficacy evaluation of gastric/gastroesophageal junction adenocarcinoma.

2.1 Patient and sample collection

Plasma samples were collected from 16 enrolled patients who visited Henan Cancer Hospital between August 10, 2019, and July 15, 2020, before and after treatment, following a standardized treatment regimen. The study received approval from the ethics committee of the Affiliated Cancer Hospital of Zhengzhou University and was conducted in accordance with local ethical guidelines, and informed consent for participation in the study has been obtained. Each patient underwent four cycles of the FLOT regimen (docetaxel 50, oxaliplatin 80, leucovorin 200, and fluorouracil 2600 mg/m², continuous 24-hour intravenous infusion, day 1, 1 cycle every 2 weeks) combined with three cycles of sintilimab (200 mg, intravenous infusion, day 1, one cycle every 3 weeks). Radical resection was performed after neoadjuvant therapy, followed by four cycles of adjuvant therapy with the FLOT regimen. The characteristics of the 16 patients are detailed in Table 1. Peripheral anticoagulant blood (2 mL, 1600 g) was collected from each patient before the first and second neoadjuvant therapy. Centrifugation was performed for 15 min to obtain upper plasma and middle white membrane. The upper plasma was carefully drawn and dispensed into 2 mL frozen storage tubes (1 mL/tube) and stored at − 80°C for future use. The Buffy Coat in the middle was meticulously absorbed and refrigerated at − 80°C. All the aforementioned procedures were completed within 2 h of blood collection.

2.2 RNA-seq sequencing analysis

Total RNA was isolated using Trizol (Invitrogen), followed by purification with QIAGEN RNeasy and treatment with RNase-free DNAase (QIAGEN). The process encompassed library preparation and sequencing experiments, and the sequencing results were imported into ACGT101-miR (LC Sciences, Houston, Texas, USA) for analysis. The mRNA and small RNA-seq libraries were prepared and used. The analysis process is outlined as follows: Initial steps involved the removal of 3’ adapters and unwanted sequences to obtain clean data. Length screening was performed, retaining sequences with base lengths between 18 and 26 nucleotides. The obtained sequences underwent comparison with various RNA databases (mRNA, RFam, and Repbase databases, excluding miRNA) and were filtered to obtain valid data. Subsequently, miRNA identification and differentiation analysis were conducted by comparing precursors and the genome, leading to the final prediction of target genes for the different miRNAs. The TruSeq Small RNA Sample Preparation Kits (Illumina, San Diego, USA) were used to prepare the small RNA sequencing library. Thereafter, the constructed library was sequenced using Illumina Hiseq2000/2500 with a single-ended read length of 1 × 50 bp. The analysis results were determined with a significance threshold of P < 0.05. The upregulated miRNA statistical map, clustering heat map, and volcano map were generated. Finally, differential miRNA target genes were predicted and enriched. GO and KEGG enrichment analyses were applied to identify significantly differentially expressed GO functional entries and KEGG enrichment pathways. The expression of miRNA was displayed using log10(norm value) (log10(0.0001) when the norm value is 0). In cases of biological repeats, the norm value of different miRNAs was used for miRNA expression display through the Z-value method. The formula for calculating the Z value is: Zsample-i = [(norm sample-i)-Mean (norm of all samples)]/[Standard deviation(norm of all samples)].

2.3 Olink Immuno-Oncology Assay

Olink proteomics relies on Proximity Extension Assay (PEA) technology, enabling the simultaneous analysis of 92 inflammation-related biomarkers. Each target protein was identified using double antibodies and coupled with its specific complementary DNA barcode. The resulting DNA sequences were then detected and quantified using a high-throughput microfluidic real-time PCR instrument (Biomark HD, Fluidigm). The obtained data underwent qualitative control and normalization using internal extension control and interboard control to adjust for in-run and inter-run variations. The final analytical readings are expressed as normalized protein expression (NPX) values, followed by log2 conversion. The limma package was employed to identify differentially expressed proteins, with a P-value cutoff of 0.05. GO and KEGG enrichment analyses were performed using ggplot2.

2.4 WGCNA analysis and driver gene mining

Using RNA-seq normalized expression data, a weighted coexpression network was constructed using Weighted gene co-expression network analysis (WGCNA) (version 1.69) with default parameters. Pearson’s correlation coefficient was employed to assess the correlation between gene modules and treatment information within patient groups. Subsequently, hub genes were identified based on the connectivity of gene modules and their association with phenotypic traits within the modules. Module connectivity was defined as the correlation between genes and modules (module membership), while clinical feature relationship was defined as the absolute value of Pearson’s correlation coefficient between each gene and therapeutic information (phenotypic significance). Candidate hub genes were screened based on a module membership degree > 0.6 and phenotypic significance > 0.6. The final hub gene was determined by selecting the common gene that met both criteria. Signal pathways enriched by hub genes were analyzed using the cluster Profiler package (version 3.14.3).

2.5 Immunoinfiltration and miRNA regulatory element

xCell (version 1.1.0) was employed to calculate the proportion of infiltrated immune cells in each sample using RNA-Seq standardized expression data. The Wilcoxon test was used to compare the significance of differences in the proportion of infiltrated immune cells between groups. The miRWalk database was used to extract regulatory outcomes of corresponding target genes exhibiting significant differences in medium and high expression of miRNA results. The Pearson’s correlation coefficient was calculated for proteins between the T1 and T2 groups. Proteins with an absolute correlation coefficient value greater than or equal to 0.5 with target molecules were identified as their co-expressed proteins.

2.6 Statistical analysis

Data are presented as mean ± standard deviation or median (first and third quartiles). Statistical analysis was performed using SPSS Statistics 25. A P-value < 0.05 was considered statistically significant.

3.1 Characteristics of the Participants

Sixteen patients who met the inclusion criteria participated in this study, and the experimental flowchart is shown in Fig. 1. As the study design, plasma samples from all 16 enrolled patients were selected for Olink omics analysis, of which nine patients underwent leukocyte RNA-seq. A comparison was made between the data obtained before and after the first neoadjuvant therapy. The patient characteristics are detailed in Table 1. Within this cohort, Tumor Regression Grade (TRG) is a measure of histopathological response to neoadjuvant therapy, patients in the T1 group comprised those with TRG0 and TRG1, while those in the T2 group included patients

with TRG2 and TRG3.

Table 1

The clinicopathological features of 16 patients.
No.	Age/Sex	Olink		RNA-seq		TRG	ypTNM	Histological type	Tumor site	Differentiated degree	pCR	PDL1
No.	Age/Sex	BF	AF	BF	AF	TRG	ypTNM	Histological type	Tumor site	Differentiated degree	pCR	PDL1
1	45/Male	Yes	Yes	Yes	Yes	TRG2	ypT3N3aM0	Adenocarcinoma	Body	poorly differentiated	Yes	5
2	66/Female	Yes	Yes	No	No	TRG1	ypT4aN0M0	Adenocarcinoma	Antrum	poorly differentiated	No	1
3	55/Male	Yes	Yes	Yes	Yes	TRG1	ypT1N1M0	Adenocarcinoma	Cardia	moderately differentiated	Yes	/
4	42/Male	Yes	Yes	No	No	TRG3	ypT3N1M0	Adenocarcinoma	Body	Medium-low differentiation	No	30
5	57/Female	Yes	Yes	No	No	TRG2	ypT2N1M0	Adenocarcinoma	Antrum	moderately differentiated	No	<1
6	49/Male	Yes	Yes	Yes	Yes	TRG1	ypT1N0M0	Adenocarcinoma	Cardia	poorly differentiated	Yes	10
7	58/Male	Yes	Yes	Yes	Yes	TRG2	ypT1N2M0	Adenocarcinoma	Cardia	moderately differentiated	Yes	5
8	61/Male	Yes	Yes	No	No	TRG2	ypT3N1M0	Adenocarcinoma	Whole stomach	poorly differentiated	No	5
9	66/Male	Yes	Yes	Yes	Yes	TRG3	ypT3N2M0	Adenocarcinoma	Cardia	poorly differentiated	Yes	<1
10	69/Male	Yes	Yes	Yes	Yes	TRG0	ypT0N0M0	Adenocarcinoma	Cardia	moderately differentiated	Yes	<1
11	57/Male	Yes	Yes	No	No	TRG3	ypT3N1M0	Adenocarcinoma	Cardia	moderately differentiated	No	1
12	50/Female	Yes	Yes	Yes	Yes	TRG1	ypT1N1M0	Adenocarcinoma	Cardia	moderately differentiated	Yes	5
13	66/Male	Yes	No	Yes	Yes	TRG2	ypT3N2M0	Adenocarcinoma	Cardia	poorly differentiated	Yes	<1
14	58/Male	Yes	No	No	No	TRG0	ypT0N0M0	Adenocarcinoma	Antrum	poorly differentiated	No	/
15	55/Male	Yes	No	No	No	TRG2	ypT3N0M0	Adenocarcinoma	Cardia	Low-medium differentiation	No	<1
16	45/Male	Yes	No	Yes	Yes	TRG2	ypT2N0M0	Adenocarcinoma	Cardia	poorly differentiated	Yes	30
/: No residual tumor cells were found

3.2 Enrichment of tumor immune-related inflammatory pathways

We conducted a correlation analysis of tumor-infiltrating immune cells in the T1 and T2 groups (Fig. 2A). The results revealed that, compared with the T1 group, CD8⁺ T cells and B cells in the T2 group significantly decreased, while monocytes and neutrophils significantly increased. To elucidate the infiltration of immune cells in vivo with the treatment, we further analyzed the T1 and T2 groups before and after neoadjuvant therapy, respectively. We found that in the T1 group, CD4⁺ T cells and monocytes were higher after treatment than before treatment. The number of CD8⁺ T cells increased and the number of B cells decreased in T2 group after treatment compared with before treatment. Overall, compared with the T1 group, the number of B cells decreased, and the number of neutrophils increased in the T2 group, as shown in Fig. 2B. Based on miRNA data, the highest enrichment scores for the T1 and T2 groups were 0.59 (P = 0.009) and 0.62 (P = 0.006), respectively (Fig. 2C). GO enrichment analysis was conducted on the expression of significantly different genes in the respective groups. The pathways associated with inflammatory response and myeloid leukocyte activation exhibited the most significant differences (Fig. 2D). To further investigate the function of differentially expressed proteins, we conducted GO enrichment analysis in different contexts. The results revealed that the differential proteins were enriched in cell activation, response to cytokine, the cytokine-mediated signaling pathway, and T cell activation, lymphocyte activation was enriched after treatment but not before treatment (Fig. 2E).

Upon analyzing RNA-seq data, the inflammatory pathway emerged as the most significantly different in the T1 and T2 groups. Furthermore, we analyzed the expression of 92 proteins related to tumor immunity in different groups using Olink proteomics. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these proteins were enriched in various of inflammatory response, immune response and chemokine activity (Fig. 3A), with several pathways, such as the cytokine − cytokine receptor interaction, chemokine signaling pathway, and TNF signaling pathway (Fig. 3B).

3.3 Key molecular biomarkers were associated with immune infiltration

Pearson’s correlation analysis revealed the interaction of 57 proteins after treatment (Supplementary Fig. 1). Seven related proteins with the most noticeable differences were identified between the T1 and the T2 group, including Angiopoietin 2 (ANGPT2), PGF, C-X3-C motif chemokine ligand 1 (CX3CL1), TNFRSF21, C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), and Granzyme B (GZMB). Compared to the T2 group, these seven different proteins were significantly downregulated in the T1 group (Fig. 4A-B). Receiver operating characteristic (ROC) curve results demonstrated that the AUC of PGF, IL33, TNFRSF21, IL15, and IFN-gamma were all > 0.8, the AUC values of PGF was 0.9143 (Fig. 4C). To further observe the potential role of the related molecules, we analyzed the data of 12 patients before and after neoadjuvant therapy (Fig. 4D). The results revealed that compared with before treatment, some proteins such as TNFRSF21 and PGF were also highly expressed after treatment. Figure 4E–F shows the volcano maps and ROC curves of the T1 and T2 groups after neoadjuvant therapy, wherein the Area under curve (AUC) values of TNFRSF21 and PGF were both 0.8833. Further cluster analysis was conducted on relevant differential proteins in the T1 and T2 groups before and after neoadjuvant therapy. The results indicated that compared with the T1 group, the CD8A protein expressions in the T2 group were decreased, the AUC values of the top three were CD8A(1.000), CCL20(0.967), and MUC-16(0.933) respectively (Fig. 4G–I).

These pathways may be linked to neoadjuvant therapy and prognosis in different groups. Simultaneously, heatmaps were generated for the relevant mRNA of samples from the T1 and T2 groups before neoadjuvant therapy, revealing 68 differentially expressed inflammation-related genes between them. Among these, 50 differentially expressed genes were downregulated in the T2 group compared to the T1 group, while 18 genes, including hsa-let-7f-2-3p_1ss22CT, hsa-miR-3665-p5_1ss17AG, mmu-miR-5126_L- 1_1ss18CTl, were upregulated. Notably, hsa-miR-1278_R-2, hsa-miR-548aa-1-p3_1ss 19TG, and hsa-miR-125b-5p_R-1 were also included (Supplementary Fig. 2A). A volcano map was generated to visualize the overall distribution of different miRNAs (Supplementary Fig. 2D). Additionally, differences in CircRNA and LncRNA were analyzed in the T1 group (No.3, No.6, No.10, No.12) and T2 group (No.1, No.7, No.9, No.13, No.16). Compared with the T1 group, the most significantly increased circRNA was circRNA15735, and the most decreased was hsa_circ_0007313 in the T2 group (Supplementary Fig. 2B-C, E-F).

3.4 Expression and prognostic value of CD8A and PGF in gastric cancer

Box plots were generated to describe the expression of two different prognostic proteins in the T1 and T2 groups (Figs. 5A-B), the expression of CD8A in T1 group was significantly higher than that in T2 group, while the expression level of PGF in T2 group was significantly increased. The prognostic value of differentially expressed genes in gastric cancer was assessed through Kaplan–Meier survival analysis, using complete mRNA transcriptomics data from The Cancer Genome Atlas (TCGA). It showed that high expressions of CD8A was associated with a favorable prognosis in patients with gastric cancer, with logrank P-value < 0.05 (Fig. 5C). Conversely, high expression of PGF was associated with a poor prognosis in patients with gastric cancer, with logrank P-value < 0.05 (Fig. 5D).

3.5 Analysis of immunoinfiltration and interaction between CD8 and PGF

Figure 6A–B illustrates the coexpression network before and after treatment. Proteins associated with CD8A and PGF before treatment included IL8, CX3CL1, GZMH, VEGFA, TNF, and CCL3. After treatment, the proteins associated with both were TNF, IL15, TNFRSF12A, MMP12, HGF, CX3CL1 and IL18. Three proteins, TNF, CX3CL1 and IL8, were involved both before and after treatment. CD8A was positively correlated with monocytes, macrophages, TNK cells, CD8 cells, dendritic cells, and negatively correlated with MDSC, while PGF was positively correlated with MDSC and TNK cells (Fig. 6C). In addition, miRNAs with medium and high expression levels were selected and the regulatory results of corresponding target genes were analyzed through miRWalk database mining (Fig. 6D).

Analyzing protein expression in peripheral blood offers a minimally invasive and accessible approach for monitoring biomarkers during neoadjuvant immunotherapy. To our knowledge, this is the first study analyzing changes in plasma protein levels before and after neoadjuvant immunotherapy for gastric cancer. We identified two markers with distinct prognostic significance within the gastric cancer immunotherapy neoadjuvant clinical trial cohort. Notably, CD8A exhibited increased expression in the T1 group after neoadjuvant therapy and were positively correlated with the efficacy of immunoinfiltrating cells, including monocytes, macrophages, TNK cells, CD8 cells. Conversely, the expression of PGF demonstrated significant increased in the T2 group, correlating with the tumor regression grade of neoadjuvant therapy.

A growing body of evidence suggested the involvement of chemokines in the pathogenesis of various malignancies, including gastric cancer^[13]. CD8A is a member of T cytotoxic pathway-related genes and encodes the CD8 antigen that is a cell surface glycoprotein found on most cytotoxic T cells. The CD8 antigen acts as a coreceptor with the T-cell receptor on the T cell to recognize antigens displayed by an antigen-presenting cell in the context of class I MHC molecules. CD8A expression may be a useful and measurable predictive marker of immunotherapeutic response and immune cell infiltration^[14]. A previous study for pan-cancer has reported that high status of CD8A with high expression of PD-L1 might be a predictive marker of immunotherapeutic response^[15]. Previous study also revealed the protective role of CD8A in the prognoses of hepatocellular carcinoma, metastatic melanoma, head and neck squamous cell carcinoma and bladder Cancer^[16–19]. However, few studies clarified the association between CD8A and immunotherapeutic response in gastric cancer, so we further investigated the prognostic value of CD8A in cancer patients treated with immunotherapy in TCGA dataset. Our results showed that low CD8A expression was associated with poor survival outcomes among cancer patients treated with immunotherapy. Thus, CD8A can not only be a useful prognostic factor in gastric cancer patients but also a predictive marker of immunotherapeutic response in cancer patients treated with immunotherapy.

PGF is well known as a member of the VEGF family, which is active in angiogenesis and endothelial cell growth, exerting an effect on its proliferation and migration. PGF has been reported as a potent stimulator in cancer invasion by activating angiogenesis^[20]. In addition, the overexpression of PGF is correlated with tumor stage, cancer progression and metastasis^[21]. In colorectal cancer, Kaplan–Meier curve analysis showed that higher expression of the PGF gene was associated with a lower survival rate. The in vitro expression of PGF was consistent with bioinformatics results^[22]. Similar results were obtained in our study that PGF expressed markedly highly in gastric cancer and played a role as an oncogene.

Our study revealed that, CD4⁺ T cells and monocytes increased in the T1 group after treatment, while CD8⁺ T cells and B cells decreased in the T2 group after treatment. The use of immune cell infiltration as a novel biomarker for predicting the prognosis of patients with various types of cancer holds great promise^{[23, 24]}. While previous reports have implicated immune infiltration in affecting tumor patient prognosis^[25–27], the interaction mechanism between prognosis and the tumor microenvironment remains incompletely understood. Neutrophils play a pivotal role in tumor formation and metastasis, exhibiting a dual role in inhibiting and promoting cancer^{[28, 29]}. The mechanism of cancer progression is intricately linked to inflammation. Our evaluation of immune cell expression in different tumor microenvironments revealed higher expression levels of monocytes and neutrophils in the T2 group compared to the T1 group. Additionally, the infiltration of CD8⁺ T cells, NK cells, and B cells was lower in the T2 group, whereas memory CD4⁺ T cells increased in both groups. Elevated leukocyte levels in the blood significantly correlate with short survival and cancer cell metastasis in patients with non-hematologic malignancies, primarily attributed to an increase in mature polymorphonuclear cells^[30]. B cells play a crucial role in humoral immunity, inhibiting the progression of tumor cells by secreting immunoglobulin and promoting T cell responses^{[31, 32]}. The heightened presence of inflammatory cells can induce the production and secretion of various chemokines and cytokines, serving as inflammatory mediators that recruit more inflammatory cells to the tumor microenvironment, thereby exacerbating a vicious cycle.

According to the results of this study, when treated with neoadjuvant immunotherapy using sintilimab in combination with FLOT protocol of gastric cancer patients, we can use Human ELISA Kit to detect the expression levels of CD8A and PGF before and after neoadjuvant therapy, evaluate and predict the possible response to therapy, and conduct dynamic monitoring of blood after treatment (for example, every 3–6 months) to better predict the progression of the disease. The present study has some limitations. First, we did’t sequence tissue samples in the tumor microenvironment, which will require additional mechanistic analysis in the future. Second, the number of gastric/gastroesophageal samples in our center is small. A larger number of gastric adenocarcinoma patients with pre- and post-operative RNA sequences need to be collected to further evaluate the performance of gastric adenocarcinoma models in predicting the expression of these molecules.

In conclusion, our study comprehensively assessed plausible carcinogenic pathways and identified novel associations with gastric/gastroesophageal diagnosis and prognosis. Our data suggest that neoadjuvant therapy efficacy may be reflected by alterations in specific proteins, offering potential etiological and clinical implications. The molecular aspects of tumor regression after neoadjuvant chemotherapy are currently under investigation, and our findings indicate that the promising candidate markers CD8A and PGF are strongly associated with immune invasion, potentially providing novel treatment strategies for patients with gastric adenocarcinoma.

Conflict of Interest

No potential conflict of interest was reported by the author(s).

Author Contributions

Chengjuan Zhang conceived and participated in research design, article writing. Tingjie Wang contributed to bioinformatic analysis. Jing Yuan performed the image arrangement and drafted the manuscript. Benling Xu contributed to clinical sample collection, Ruihua Bai, Xiance Tang and Xiaojie Zhang contributed to confirmation of pathological information. Minqing Wu, Tianqi Lei, Wenhao Xu were responsible for sample processing and storage. Yongjun Guo conceived the study, Ning Li conceived the study and provided clinical samples. All authors read and approved the final manuscript.

Funding

This research was supported by the Major public welfare projects in Henan Province- Research and development of new technologies for tumor liquid biopsy and immunotherapy (201300310400), The Health Science and Technology Innovation Project for Young people of Henan Provincial (YXKC2021032, YXKC2021008), Zhengzhou University young teachers basic research training project (JC23858081), Natural Science Foundation of Henan Province Project (242300420095).

Acknowledgements

The LC Bio Technology CO., Ltd. (Hangzhou, China) provided technical assistance for RNA sequencing and Olink Immuno-Oncology Assay.

Data availability statement

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Ethical Statement

This study was conducted in accordance with the declar-ation of Helsinki. This study was conducted with approval from the Ethics Committee of Henan Cancer Hospital (Ethics: 2019214).

Consent to participate

Written informed consent was obtained from all participants.

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No competing interests reported.

SupplementalFigure1.png
Supplementary Figure 1 Correlations between the differentially expressed inflammation-related biomarkers in after treatment (AF).
SupplementalFigure2.png
Supplemental Figure 2 The difference of MiRNA, LncRNA, and CircRNA in T1 and T2 groups.

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Potential predictive value of CD8A and PGF protein expression in gastric cancer patients treated with neoadjuvant immunotherapy

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Abstract

Background

Methods

Results

Conclusions

Figures

1 Introduction

2 Materials and methods

2.1 Patient and sample collection

2.2 RNA-seq sequencing analysis

2.3 Olink Immuno-Oncology Assay

2.4 WGCNA analysis and driver gene mining

2.5 Immunoinfiltration and miRNA regulatory element

2.6 Statistical analysis

3 Results

3.1 Characteristics of the Participants

3.2 Enrichment of tumor immune-related inflammatory pathways

3.3 Key molecular biomarkers were associated with immune infiltration

3.4 Expression and prognostic value of CD8A and PGF in gastric cancer

3.5 Analysis of immunoinfiltration and interaction between CD8 and PGF

4 Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1