S100A9 as a novel diagnostic and prognostic biomarker in human gastric cancer

Potential Serum HMGB1, HSP90, and S100A9 as Metastasis Predictive Biomarkers for Cancer Patients and Relevant Cytokines: A Pilot Study

Metastatic cancer is still one of the leading causes of death worldwide despite significant advancements in diagnosis and treatment. Biomarkers are one of the most promising diagnostic tools that are used alongside traditional diagnostic tools in cancer patients. DAMPs are intracellular molecules released in response to cellular stress, tissue injury, and cell death. There have been shown to be associated with worsening prognosis among such patients, and some DAMPs could potentially be used as predictive biomarkers of metastatic status. The goal of this study is to investigate DAMP expression and the probability that certain DAMPs could be predictive biomarkers of the metastatic stage in various cancer types. Forty cancer patients at Naresuan University Hospital, Thailand, were enrolled. Then, an investigation of HSP90, HMGB1, S100A9, and ATP expression and cytokine/chemokine profiling in serum was performed using an immunological-based assay. We assessed the predictive biomarker candidates and the association between DAMP expression and cytokines/chemokines using an ROC curve analysis and a correlation regression analysis. The results showed that HSP90 has strong potential as a metastatic predictive biomarker, with a cutoff value of 25.46 ng/mL (AUC 0.8207, sensitivity 82.61%, specificity 75.00%, 95% CI 0.6860-0.9553). This was followed by HMGB1 and S100A9, which exhibited sensitivity of 82.61 and 65.22%, and specificity of 68.75 and 56.25%, respectively. Interestingly, the candidate DAMPs negatively correlate with various serum cytokines, for example, HMGB1 vs. IL-15 (slope 88.05, R 0.3297, p-value 0.005), HMGB1 vs. IFN-γ (slope 2.235, R 0.3052, p-value 0.0013) and HSP90 vs. IFN-γ (slope 0.0614, R 0.2187, p-value 0.008), suggesting that they are highly elevated in advanced metastatic tumors, which is possibly associated with the immunomodulation effect. We postulated that HSP90, HMGB1, and S100A9 have the potential to be predictive biomarkers for supporting tumor metastasis categorization using histopathology.

The role of the S100A8/S100A9 in gastric tumor progression

Gastric premalignant lesions can develop into cancer through multiple steps and inflammation plays a critical role. The aim of this study is to uncover the characteristics of macrophages and their gene expression in premalignant gastric lesions to identify novel biomarkers and potential targets for treatment. We used the computational algorithm CIBERSORT to estimate immune cell subsets present in gastric tissue. We applied WGCNA to identify inflammation-related modules and hub genes. Single-cell analysis was used to identify macrophage sub-clusters specific to pathology. In addition, the in-vitro experiment was performed to verify the mechanism of the key inflammatory factors in the growth of gastric cancer. WGCNA identified a module that was positively correlated with pathological changes and highly related to inflammation scores. Single-cell analysis revealed a macrophage subset, and we observed that S100A8 and S100A9 + macrophages made up a significantly higher proportion in early gastric cancer (EGC) tissues. Our functional enrichment analysis suggested that these macrophages may play a role in gastric tumorigenesis through the activation of the NFκB signaling pathway. In vitro experiments verified that S100A9 can promote the proliferation and migration of AGS cells through the TLR4-NFκB signaling pathway, and the S100A8/S100A9 inhibitor Paquinimod can inhibit their proliferation and migration. Our findings suggest that S100A8 and S100A9 + macrophages may activate the TLR4-NFκB signaling pathway to promote cell proliferation and migration leading to gastric tumor progression. Macrophages with high expression of S100A8/S100A9 are critical in the progression of gastric inflammation to cancer. Cytokine S100A9 can activate the TLR4-NFκB signaling pathway and promote the proliferation and migration of gastric adenocarcinoma cells.

EMT-related gene risk model establishment for prognosis and drug treatment efficiency prediction in hepatocellular carcinoma

This study was designed to evaluate the prognosis and pharmacological therapy sensitivity of epithelial mesenchymal transition-related genes (EMTRGs) that obtained from the EMTome database in hepatocellular carcinoma (HCC) using bioinformatical method. The expression status of EMTRGs were also investigated using the clinical information of HCC patients supported by TCGA database and the ICGC database to establish the TCGA cohort as the training set and the ICGC cohort as the validation set. Analyze the EMTRGs between HCC tissue and liver tissue in the TCGA cohort in the order of univariate COX regression, LASSO regression, and multivariate COX regression, and construct a risk model for EMTRGs. In addition, enrichment pathways, gene mutation status, immune infiltration, and response to drugs were also analyzed in the high-risk and low-risk groups of the TCGA cohort, and the protein expression status of EMTRGs was verified. The results showed a total of 286 differentially expressed EMTRGs in the TCGA cohort, and EZH2, S100A9, TNFRSF11B, SPINK5, and CCL21 were used for modeling. The TCGA cohort was found to have a worse outcome in the high-risk group of HCC patients, and the ICGC cohort confirmed this finding. In addition, EMTRGs risk score was shown to be an independent prognostic factor in both cohorts by univariate and multivariate COX regression. The results of GSEA analysis showed that most of the enriched pathways in the high-risk group were associated with tumor, and the pathways enriched in the low-risk group were mainly associated with metabolism. Patients in various risk groups had varying immunological conditions, and the high-risk group might benefit more from targeted treatments. To sum up, the EMTRGs risk model was developed to forecast the prognosis for HCC patients, and the model might be useful in assisting in the choice of treatment drugs for HCC patients.

A novel NET-related gene signature for predicting DLBCL prognosis

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is an aggressive malignancy. Neutrophil extracellular traps (NETs) are pathogen-trapping structures in the tumor microenvironment that affect DLBCL progression. However, the predictive function of NET-related genes (NRGs) in DLBCL has received little attention. This study aimed to investigate the interaction between NRGs and the prognosis of DLBCL as well as their possible association with the immunological microenvironment.

METHODS

The gene expression and clinical data of patients with DLBCL were downloaded from the Gene Expression Omnibus database. We identified 148 NRGs through the manual collection of literature. GSE10846 (n = 400, GPL570) was used as the training dataset and divided into training and testing sets in a 7:3 ratio. Univariate Cox regression analysis was used to identify overall survival (OS)-related NETs, and the least absolute shrinkage and selection operator was used to evaluate the predictive efficacy of the NRGs. Kaplan-Meier plots were used to visualize survival functions. Receiver operating characteristic (ROC) curves were used to assess the prognostic predictive ability of NRG-based features. A nomogram containing the clinical information and prognostic scores of the patients was constructed using multivariate logistic regression and Cox proportional risk regression models.

RESULTS

We identified 36 NRGs that significantly affected patient overall survival (OS). Eight NRGs (PARVB, LYZ, PPARGC1A, HIF1A, SPP1, CDH1, S100A9, and CXCL2) were found to have excellent predictive potential for patient survival. For the 1-, 3-, and 5-year survival rates, the obtained areas under the receiver operating characteristic curve values were 0.8, 0.82, and 0.79, respectively. In the training set, patients in the high NRG risk group presented a poorer prognosis (p < 0.0001), which was validated using two external datasets (GSE11318 and GSE34171). The calibration curves of the nomogram showed that it had excellent predictive ability. Moreover, in vitro quantitative real-time PCR (qPCR) results showed that the mRNA expression levels of CXCL2, LYZ, and PARVB were significantly higher in the DLBCL group.

CONCLUSIONS

We developed a genetic risk model based on NRGs to predict the prognosis of patients with DLBCL, which may assist in the selection of treatment drugs for these patients.

S100A8 and S100A9 in Cancer

S100A8 and S100A9 are Ca²⁺ binding proteins that belong to the S100 family. Primarily expressed in neutrophils and monocytes, S100A8 and S100A9 play critical roles in modulating various inflammatory responses and inflammation-associated diseases. Forming a common heterodimer structure S100A8/A9, S100A8 and S100A9 are widely reported to participate in multiple signaling pathways in tumor cells. Meanwhile, S100A8/A9, S100A8, and S100A9, mainly as promoters, contribute to tumor development, growth and metastasis by interfering with tumor metabolism and the microenvironment. In recent years, the potential of S100A8/A9, S100A9, and S100A8 as tumor diagnostic or prognostic biomarkers has also been demonstrated. In addition, an increasing number of potential therapies targeting S100A8/A9 and related signaling pathways have emerged. In this review, we will first expound on the characteristics of S100A8/A9, S100A9, and S100A8 in-depth, focus on their interactions with tumor cells and microenvironments, and then discuss their clinical applications as biomarkers and therapeutic targets. We also highlight current limitations and look into the future of S100A8/A9 targeted anti-cancer therapy.

In vivo imaging of microenvironmental and anti-PD-L1-mediated dynamics in cancer using S100A8/S100A9 as an imaging biomarker

PURPOSE

As a promotor of tumor invasion and tumor microenvironment (TME) formation, the protein complex S100A8/S100A9 is associated with poor prognosis. Our aim was to further evaluate its origin and regulatory effects, and to establish an imaging biomarker for TME activity.

METHODS

S100A9-/-cells (ko) were created from syngeneic murine breast cancer 4T1 (high malignancy) and 67NR (low malignancy) wildtype (wt) cell lines and implanted into either female BALB/c wildtype or S100A9-/- mice (n = 10 each). Anti-S100A9-Cy5.5-targeted fluorescence reflectance imaging was performed at 0 h and 24 h after injection. Potential early changes of S100A9-presence under immune checkpoint inhibition (anti-PD-L1, n = 7 vs. rat IgG2b as isotype control, n = 3) were evaluated.

RESULTS

In S100A9-/-mice contrast-to-noise-ratios were significantly reduced for wt and S100A9-/-tumors. No significant differences were detected for 4T1 ko and 67NR ko cells as compared to wildtype cells. Under anti-PD-L1 treatment S100A9 presence significantly decreased compared with the control group.

CONCLUSION

Our results confirm a secretion of S100A8/S100A9 by the TME, while tumor cells do not apparently release the protein. Under immune checkpoint inhibition S100A9-imaging reports an early decrease of TME activity. Therefore, S100A9-specific imaging may serve as an imaging biomarker for TME formation and activity.

Gastric cancer: An epigenetic view

Gastric cancer (GC) poses a serious threat worldwide with unfavorable prognosis mainly due to late diagnosis and limited therapies. Therefore, precise molecular classification and search for potential targets are required for diagnosis and treatment, as GC is complicated and heterogeneous in nature. Accumulating evidence indicates that epigenetics plays a vital role in gastric carcinogenesis and progression, including histone modifications, DNA methylation and non-coding RNAs. Epigenetic biomarkers and drugs are currently under intensive evaluations to ensure efficient clinical utility in GC. In this review, key epigenetic alterations and related functions and mechanisms are summarized in GC. We focus on integration of existing epigenetic findings in GC for the bench-to-bedside translation of some pivotal epigenetic alterations into clinical practice and also describe the vacant field waiting for investigation.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Chronic Stress-Induced Gene Changes In Vitro and In Vivo: Potential Biomarkers Associated With Depression and Cancer Based on circRNA- and lncRNA-Associated ceRNA Networks

Circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) have been considered as biomarkers or regulators in many diseases. However, the exact role of circRNA- or lncRNA-mediated competing endogenous RNA (ceRNA) networks in the modulation of depression pathogenesis-relevant processes is not clear. In this study, we profiled whole transcriptome in depression patients’ blood samples via microarray analysis. As a result, a total of 340 circRNAs, 398 lncRNAs, 206 miRNAs, and 92 mRNAs were differentially expressed between the depression and control groups. Then, we constructed ceRNA networks according to the differentially expressed genes (DEGs). Using bioinformatics analysis, 89 pairs of circRNA-ceRNA and 49 pairs of lncRNA-ceRNA networks were obtained. Since depression is a broad and heterogeneous condition that is known as promoter for many chronic diseases including cancer, so we further dug out 28 circRNAs, 61 lncRNAs, 26 miRNAs, and 29 mRNAs that are associated with cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in cancer-related signaling pathways such as MAPK, Wnt, IL-17, Ras, and PI3K-Akt. Genes involved in the above pathways such as S100A9, GATA2, SRFP5, SLC45A3, NTRK1, FRZB, has_circ_0014221, has_circ_0014220, and has_circ_0087100 were dysregulated in various cancer cell lines by stress hormones induced. HDC, GATA2, SLC45A3, and NTRK1 were downregulated in tumor-bearing mice subjected to chronic unpredictable mild stress (CUMS). LncRNA-mediated ceRNA network validation showed that overexpression of miR-4530 declined HDC level. Our findings highlight the potential circRNA- and lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of depression and as potential biomarkers in depression cancer comorbidity through the pathways of IL-17 or histidine metabolism.

High S100A9+ cell density predicts a poor prognosis in hepatocellular carcinoma patients after curative resection

S100A9 is differentially expressed in various cell types and is associated with the development, progression and metastasis of various cancers. However, the expression, distribution, and clinical significance of S100A9 in hepatocellular carcinoma (HCC) remain unclear. In the present study, The Cancer Genome Atlas (TCGA) database was used to examine S100A9 gene expression in HCC; we found that S100A9 expression was associated with HCC prognosis. In addition, S100A9 protein expression was assessed by immunohistochemistry analysis of tissues from 382 HCC patients. We found that the infiltration of S100A9⁺ cells in both tumor and nontumor tissues could predict poor overall survival (P = 0.0329, tumor; P = 0.0003, nontumor) and a high recurrence risk (P = 0.0387, tumor; P = 0.0015, nontumor) in our tissue microarray analysis. Furthermore, immunofluorescence double staining revealed that the primary S100A9-expressing cells in adjacent nontumoral tissue were CD15⁺ neutrophils, and both CD68⁺ macrophages and CD15⁺ neutrophils expressed S100A9 in HCC tumor tissues. Taken together, the results suggest that high S100A9⁺ cell density predicts a poor prognosis in HCC patients, and S100A9 expression could potentially serve as an independent prognostic marker for HCC.

Analysis of Gastric Cancer Transcriptome Allows the Identification of Histotype Specific Molecular Signatures With Prognostic Potential

Gastric cancer is the fifth most common malignancy but the third leading cause of cancer-associated mortality worldwide. Therapy for gastric cancer remain largely suboptimal making the identification of novel therapeutic targets an urgent medical need. In the present study we have carried out a high-throughput sequencing of transcriptome expression in patients with gastric cancers. Twenty-four patients, among a series of 53, who underwent an attempt of curative surgery for gastric cancers in a single center, were enrolled. Patients were sub-grouped according to their histopathology into diffuse and intestinal types, and the transcriptome of the two subgroups assessed by RNAseq analysis and compared to the normal gastric mucosa. The results of this investigation demonstrated that the two histopathology phenotypes express two different patterns of gene expression. A total of 2,064 transcripts were differentially expressed between neoplastic and non-neoplastic tissues: 772 were specific for the intestinal type and 407 for the diffuse type. Only 885 transcripts were simultaneously differentially expressed by both tumors. The per pathway analysis demonstrated an enrichment of extracellular matrix and immune dysfunction in the intestinal type including CXCR2, CXCR1, FPR2, CARD14, EFNA2, AQ9, TRIP13, KLK11 and GHRL. At the univariate analysis reduced levels AQP9 was found to be a negative predictor of 4 years survival. In the diffuse type low levels CXCR2 and high levels of CARD14 mRNA were negative predictors of 4 years survival. In summary, we have identified a group of genes differentially regulated in the intestinal and diffuse histotypes of gastric cancers with AQP9, CARD14 and CXCR2 impacting on patients' prognosis, although CXCR2 is the only factor independently impacting overall survival.