The S100 protein family in lung cancer

Knockdown of S100A2 inhibits the aggressiveness of endometrial cancer by activating STING pathway

BACKGROUND

Endometrial cancer is a kind of gynaecological cancer. S100A2 is a newfound biomarker to diagnose endometrial cancer. This study was to investigate the role of S100A2 on regulating migration and invasion of endometrial cancer.

METHODS

The mRNA and protein levels of S100A2 were obtained by quantitative real-time polymerase chain reaction, immunohistochemistry and western blot methods. Cell viability was measured by the Cell Counting Kit-8 assay. Cell migration and invasion were quantified using transwell assays. Western blot assay was conducted to quantify protein expressions of epithelial to mesenchymal transition-related proteins (N-cadherin and E-cadherin). Furthermore, in vivo tumour formation experiments were performed to evaluate the role of S100A2 on tumour xenografts.

RESULTS

S100A2 was significantly up-regulated in endometrial cancer tissues. Knockdown of S100A2 inhibited cell viability, migration and invasion of endometrial cancer cells. Meanwhile, STING pathway was activated by the inhibited S100A2. STING inhibitor C-176 significantly reversed the effects of S100A2 knockdown on aggressive behaviours of endometrial cancer cells. Inhibition of S100A2 dramatically suppresses the tumour growth in vivo.

CONCLUSIONS

S100A2 functions as an oncogene in endometrial cancer. Targeting S100A2 may be a promising therapeutic method to treat endometrial carcinoma.

Identification of oxidative stress signatures of lung adenocarcinoma and prediction of patient prognosis or treatment response with single-cell RNA sequencing and bulk RNA sequencing data

Lung adenocarcinoma (LUAD), the most common subtype of lung cancer globally, has seen improved prognosis with advancements in diagnostic, surgical, radiotherapy, and molecular therapy techniques, while its 5-year survival rate remains low. Molecular biomarkers provide prognostic value. Oxidative stress factors, such as reactive nitrogen species and ROS, are crucial in various stages of tumor progression, influencing cell transformation, proliferation, angiogenesis, and metastasis. ROS demonstrate dual roles, affecting tumor cells, hypoxia sensitivity, and the microenvironment. Comprehensive analysis of oxidative stress in LUAD has not been conducted to date. Therefore, we systematically investigated the regulatory patterns of oxidative stress in LUAD based on oxidative stress-related genes and correlated these patterns with cellular infiltration characteristics of the tumor immune microenvironment. The model utilizes single-factor Cox analysis to screen key differential genes with prognostic value and employs least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct a prognostic-related prediction model. Ten candidate genes were selected based on this model. The risk score was constructed using the coefficients and expression levels of these ten genes. Furthermore, the impact of this risk score on overall survival (OS) was determined. Two genes with the most significant differential expression, SFTPB and S100P, were selected through qRT-PCR. Cell experiments including CCK-8, Edu, transwell assays confirmed their effects on lung cancer cells growth, consistent with the results of bioinformatics analysis. These findings suggested that this model held potential clinical value for evaluating the prognosis of lung adenocarcinoma.

TFAP2A Activates S100A2 to Mediate Glutamine Metabolism and Promote Lung Adenocarcinoma Metastasis

BACKGROUND

Lung adenocarcinoma (LUAD) is a fatal disease with metabolic abnormalities. The dysregulation of S100 calcium-binding protein A2 (S100A2), a member of the S100 protein family, is connected to the development of various cancers. The impact of S100A2 on the LUAD occurrence and metastasis, however, has not yet been reported. The functional mechanism of S100A2 on LUAD cell metastasis was examined in this article.

METHODS

The expression of TFAP2A and S100A2 in LUAD tissues and cells was analyzed by bioinformatics and qRT-PCR, respectively. The enrichment pathway analysis was performed on S100A2. Bioinformatics analysis determined the binding relationship between TFAP2A and S100A2, and their interaction was validated through dual-luciferase and chromatin immunoprecipitation experiments. Cell viability was determined using cell counting kit-8 (CCK-8). A transwell assay was performed to analyze the invasion and migration of cells. Immunofluorescence was conducted to obtain vimentin and E-cadherin expression, and a western blot was used to detect the expression of MMP-2, MMP-9, GLS, and GLUD1. The kits measured the NADPH/NADP ratio, glutathione (GSH)/glutathione disulfide (GSSG) levels, and the contents of glutamine, α-KG, and glutamate.

RESULTS

S100A2 was upregulated in LUAD tissues and cells, and S100A2 mediated glutamine metabolism to induce LUAD metastasis. Additionally, the transcriptional regulator TFAP2A was discovered upstream of S100A2, and TFAP2A expression was upregulated in LUAD, which indicated that TFAP2A promoted the S100A2 expression. The rescue experiment found that upregulation of S100A2 could reverse the inhibitory effects of silencing TFAP2A on glutamine metabolism and cell metastasis.

CONCLUSION

In conclusion, by regulating glutamine metabolism, the TFAP2A/S100A2 axis facilitated LUAD metastasis. This suggested that targeting S100A2 could be beneficial for LUAD treatment.

Activation of Hypoxia Inducible Factor-1 Alpha-Mediated DNA Methylation Enzymes (DNMT3a and TET2) Under Hypoxic Conditions Regulates S100A6 Transcription to Promote Lung Cancer Cell Growth and Metastasis

Aims: This research was aimed at investigating the effects of hypoxia inducible factor-1 alpha (HIF-1α)-mediated DNA methylation enzymes (ten-eleven translocase-2 [TET2] and DNA methyltransferase-3a [DNMT3a]) under hypoxic conditions on S100A6 transcription, thereby promoting the growth and metastasis of lung cancer cells. Methods: The expression of HIF-1α or S100A6 in lung cancer cells was interfered with under normoxic and hypoxic conditions, and the cell proliferative, migratory, and invasive properties were assessed. The mechanism of HIF-1α-regulated TET2 and DNMT3 effects on S100A6 transcription under hypoxic conditions was further investigated. Results: Functionally, S100A6 over-expression promoted lung cancer cell proliferation and metastasis. S100A6 over-expression reversed the inhibitory effects of HIF-1α interference on the proliferation and metastasis of lung cancer cells. S100A6 was induced to express in an HIF-1α-dependent manner under hypoxic conditions, and silencing S100A6 or HIF-1α suppressed lung cancer cell proliferation and metastasis under hypoxic conditions. Further, The Cancer Genome Atlas-lung adenocarcinoma database analysis revealed that S100A6 mRNA levels had a negative correlation with methylation levels. Mechanistically, CpG hypomethylation status in the S100A6 promoter hypoxia response element had an association with HIF-1α induction. TET2 was enriched in S100A6 promoter region of lung cancer cells under hypoxic conditions, whereas DNMT3a enrichment was reduced in S100A6 promoter region. HIF-1α-mediated S100A6 activation was linked to DNMT3a-associated epigenetic inactivation and TET2 activation. Innovation: The activation of HIF-1α-mediated DNA methylation enzymes under hypoxic conditions regulated S100A6 transcription, thereby promoting lung cancer cell growth and metastasis. Conclusion: In lung cancer progression, hypoxia-induced factor HIF-1α combined with DNA methylation modifications co-regulates S100A6 transcriptional activation and promotes lung cancer cell growth and metastasis.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis

PURPOSE

Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets.

METHODS

ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors.

RESULTS

Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001).

CONCLUSIONS

This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

S100A6 could not promote the differentiation of Calu-6 lung cancer cell line

Background

Our previous study demonstrated that S100 calcium binding protein A6 (S100A6) impairs tumorigenesis by Calu-6 lung cancer cells, as well as inhibit their growth. However, the role that S100A6 plays in tumor cell differentiation has not been previously explored. This study aimed to confirm the effect of S100A6 on the direction of differentiation in the human lung cancer cell linem Calu-6m based on our previous published research.

Materials and methods

A S100A6-overexpressing lentiviral vector was successfully constructed in our previous study. Nude mouse tumorigenicity was then applied successfully, and 15 mice were divided into three groups (Calu-6, Calu-6/neo, Calu-6/S100A6). After 5 weeks, we detected lung cancer markers with immunohistochemistry in mice tumor tissues, including the adenocarcinoma markers, TTF-1 and NapsinA, the squamous cell carcinoma markers, P40, CK5/6 and P63, and the small cell lung cancer markers CD56, Syn, CgA, TTF-1, CK, and Ki-67. Differences among the three groups were statistically compared.

Results

All the above-mentioned markers were positive in the tumor tissues of all three groups, and there were no significant differences.

Conclusion

S100A6 cannot promote differentiation of the undifferentiated human lung cancer cell line, Calu-6, into adenocarcinoma, squamous, or small cell carcinoma cell lines.

Combining Classic and Novel Neutrophil-Related Biomarkers to Identify Non-Small-Cell Lung Cancer

BACKGROUND

Recent studies have revealed that neutrophils play a crucial role in cancer progression. This study aimed to explore the diagnostic value of neutrophil-related biomarkers for non-small-cell lung cancer (NSCLC).

METHODS

We initially assessed the associations between classic neutrophil-related biomarkers (neutrophil-to-lymphocyte ratio (NLR), absolute neutrophil counts (NEU), absolute lymphocyte counts (LYM)) and NSCLC in 3942 cases and 6791 controls. Then, we measured 11 novel neutrophil-related biomarkers via Luminex Assays in 132 cases and 66 controls, individually matching on sex and age (±5 years), and evaluated their associations with NSCLC risk. We also developed the predictive models by sequentially adding variables of interest and assessed model improvement.

RESULTS

Interleukin-6 (IL-6) (odds ratio (OR) = 10.687, 95% confidence interval (CI): 3.875, 29.473) and Interleukin 1 Receptor Antagonist (IL-1RA) (OR = 8.113, 95% CI: 3.182, 20.689) shows strong associations with NSCLC risk after adjusting for body mass index, smoking status, NLR, and carcinoembryonic antigen. Adding the two identified biomarkers to the predictive model significantly elevated the model performance from an area under the receiver operating characteristic curve of 0.716 to 0.851 with a net reclassification improvement of 97.73%.

CONCLUSIONS

IL-6 and IL-1RA were recognized as independent risk factors for NSCLC, improving the predictive performance of the model in identifying disease.

The role of miR-6884-5p in epithelial-mesenchymal transition in non-small cell lung cancer

Significant progress has been made in the management of non-small cell lung cancer (NSCLC), though a big barrier remains, which is epithelial-mesenchymal transition (EMT). Our study aimed to evaluate the function of miR-6884-5p and S100A16 in EMT-aggravated NSCLC. The tumor tissues and adjacent tissues from 92 NSCLC patients were collected to analyze the expression of miR-6884-5p and S100A16. Then lung cancer cell line A549 was co-transfected with miR-6884-5p mimics and S100A16 to further evaluate their function. Compared to adjacent tissues, low expression of miR-6884-5p was observed in the NSCLC tissues and associated with severe NSCLC progression. MiR-6884-5p expression was negatively correlated with EMT in NSCLC. Luciferase assay data revealed that miR-6884-5p could directly bind to the 3'UTR of S100A16 and inhibited the expression of S100A16 in A549 cells. Moreover, miR-6884-5p mimics significantly ameliorated EMT progression, and overexpression of S100A16 could reverse the inhibitory effect of miR-6884-5p in A549 cells. MiR-6884-5p inhibited EMT through directly targeting S100A16 in NSCLC. Our findings suggest that miR-6884-5p could be a diagnostic marker of NSCLC, as well as a potential candidate for NSCLC treatment.

Blastocyst-Derived Lactic Acid May Regulate S100A6 Expression and Function in Mouse Decidualization via Stimulation of Uterine Epithelial Arachidonic Acid Secretion

(1) Background: Inflammatory responses are implicated in embryo implantation, decidualization, pregnancy maintenance and labor. Both embryo implantation and decidualization are essential to successful pregnancy in rodents and primates. S100A6 is involved in inflammation, tumor development, apoptosis and calcium homeostasis. S100A6 is strongly expressed in mouse decidua, but the underlying mechanisms of how S100A6 regulates implantation and decidualization are poorly defined. (2) Methods: Mouse endometrial stromal and epithelial cells are isolated from day 4 pseudopregnant mouse uteri. Both immunofluorescence and Western blotting are used to analyze the expression and localization of proteins. The molecular mechanism is verified in vitro by Western blotting and the quantitative polymerase chain reaction. (3) Results: From days 4 to 8 of pregnancy, S100A6 is specifically expressed in mouse subluminal stromal cells. Blastocyst-derived lactic acid induces AA secretion by activating the luminal epithelial p-cPLA2. The epithelial AA induces stromal S100A6 expression through the COX2/PGI2/PPAR δ pathway. Progesterone regulates S100A6 expression through the progesterone receptor (PR). S100A6/RAGE signaling can regulate decidualization via EGFR/ERK1/2 in vitro. (4) Conclusions: S100A6, as an inflammatory mediator, is important for mouse implantation and decidualization.

Single-cell RNA-sequencing uncovers the dynamic changes of tumour immune microenvironment in advanced lung adenocarcinoma

BACKGROUND

The heterogeneity of lung adenocarcinoma (LUAD) plays a vital role in determining the development of cancer and therapeutic sensitivity and significantly hinders the clinical treatment of LUAD.

OBJECTIVE

To elucidate the cellular composition and reveal previously uncharacterised tumour microenvironment in LUAD using single-cell RNA-sequencing (scRNA-seq).

METHODS

Two scRNA-seq datasets with 106 829 high-quality cells from 34 patients including 11 normal, 9 early (stage I and II) and 14 advanced (stage III and IV) LUAD were integrated and clustered to explore diagnostic and therapeutic cell populations and their biomarkers for diverse stages of LUAD. Three independent bulk RNA-seq datasets were used to validate the results from scRNA-seq analysis. The expression of marker genes for specific cell types in early and advanced LUAD was verified by immunohistochemistry (IHC).

RESULTS

Comprehensive cluster analysis identified that S100P+ epithelial and SPP1+ macrophage, positively related to poor outcomes, were preferentially enriched in advanced stage. Although the accumulation of KLRB1+CD8+ T cell and IGHA1+/IGHG1+ plasma cell both significantly associated the favourable prognosis, we also found KLRB1+CD8+ T cell decreased in advanced stage while IGHA1+/IGHG1+ plasma cells were increased. Cell-cell communication analysis showed that SPP1+ macrophage could interact with most of CD8+ subclusters through SPP1-CD44 axis. Furthermore, based on three independent bulk RNA-seq datasets, we built risk model with nine marker genes for specific cell subtypes and conducted deconvolution analysis, both supporting our results from scRNA-seq data. We finally validated the expression of four marker genes in early and advanced LUAD by IHC.

CONCLUSION

Our analyses highlight the molecular dynamics of LUAD epithelial and microenvironment and provide new targets to improve LUAD therapy.

Characteristics of Interstitial Pneumonia With Autoimmune Features (IPAF): Protocol for a Multicenter Prospective Study

BACKGROUND

"Interstitial lung disease" (ILD) is a broad term encompassing diseases of different backgrounds. "Interstitial pneumonia with autoimmune features" (IPAF) is a recent term that implies the presence of autoimmunity.

OBJECTIVE

This study aims to determine the characteristics of Polish patients with IPAF, compare them with patients with other interstitial pneumonias, and search for the prognostic and diagnostic biomarkers of IPAF in serum and bronchoalveolar lavage fluid (BALF).

METHODS

This multicenter prospective study plans to recruit 240 participants divided into 1 study group and 2 control groups. Biological fluid samples will be collected according to Polish Respiratory Society management guidelines and stored at -80°C for further tests. Prospective 5-year observations of 60 newly diagnosed individuals are planned. The study will be divided into subsections. First, we plan to characterize Polish patients with IPAF (study group) against their peers with other ILDs (2 control groups). Control group 1 will comprise patients with idiopathic ILDs, including mainly idiopathic pulmonary fibrosis and nonspecific interstitial pneumonia. Control group 2 will comprise patients with connective tissue disease-associated interstitial lung diseases, such as rheumatoid arthritis, systemic sclerosis, polymyositis, dermatomyositis, Sjögren's syndrome, mixed connective tissue disease, and systemic lupus erythematosus. Radiological and functional parameters will be analyzed. Patients will be compared in terms of high-resolution computed tomography results, the 6-minute walking test performance, and pulmonary function test parameters. The diagnosis of IPAF will be reassessed on a regular basis through multidisciplinary discussion in order to determine its clinical stability. In the laboratory arm, inflammation and fibrosis pathways will be assessed. Cytokine levels (interleukin 8, transforming growth factor beta 1, chemokine C-C motif ligand [CXCL]18, CXCL1, surfactant protein [SP]-A, SP-D, Krebs von den Lungen-6 protein, and chitinase 1) will be measured in serum and BALF. A comparative analysis of serum and BALF cytokine levels will be performed in order to establish potential differences between systemic and local inflammatory pathways. In the quality of life (QoL) arm of the study, dyspnea and cough and their impact on various aspects of the QoL will be assessed. Depression and anxiety will be measured with the Hospital Anxiety and Depression Modified Scale and the 9-item Patient Health Questionnaire, and potential correlations with symptom prevalence will be assessed.

RESULTS

This study will start recruiting patients to phase 1 in October 2023. The final results will be available in 2028. We plan to publish preliminary results after 2-3 years from the start of phase 1.

CONCLUSIONS

This study will be a step toward a better understanding of IPAF etiopathogenesis and outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/44802.

Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients

Diabetic nephropathy (DN) is the most prevalent microvascular consequence of diabetes and has recently risen to the position of the world's second biggest cause of end-stage renal diseases. Growing studies suggest that oxidative stress (OS) responses are connected to the advancement of DN. This study aimed to developed a novel diagnostic model based on OS-related genes. The differentially expressed oxidative stress-related genes (DE-OSRGs) experiments required two human gene expression datasets, which were given by the GEO database (GSE30528 and GSE96804, respectively). The potential diagnostic genes were identified using the SVM-RFE assays and the LASSO regression model. CIBERSORT was used to determine the compositional patterns of the 22 different kinds of immune cell fraction seen in DN. These estimates were based on the combined cohorts. DN serum samples and normal samples were both subjected to RT-PCR in order to investigate the degree to which certain genes were expressed. In this study, we were able to locate 774 DE-OSRGs in DN. The three marker genes (DUSP1, PRDX6 and S100A8) were discovered via machine learning on two different machines. The high diagnostic value was validated by ROC tests, which focused on distinguishing DN samples from normal samples. The results of the CIBERSORT study suggested that DUSP1, PRDX6, and S100A8 may be associated to the alterations that occur in the immunological microenvironment of DN patients. Besides, the results of RT-PCR indicated that the expression of DUSP1, PRDX6, and S100A8 was much lower in DN serum samples compared normal serum samples. The diagnostic value of the proposed model was likewise verified in our cohort, with an area under the curve of 9.946. Overall, DUSP1, PRDX6, and S100A8 were identified to be the three diagnostic characteristic genes of DN. It's possible that combining these genes will be effective in diagnosing DN and determining the extent of immune cell infiltration.

Single-cell RNA sequencing reveals enhanced antitumor immunity after combined application of PD-1 inhibitor and Shenmai injection in non-small cell lung cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have altered the clinical management of non-small cell lung cancer (NSCLC). However, the low response rate, severe immune-related adverse events (irAEs), and hyperprogressive disease following ICIs monotherapy require attention. Combination therapy may overcome these limitations and traditional Chinese medicine with immunomodulatory effects provides a promising approach. Shenmai injection (SMI) is a clinically effective adjuvant treatment for cancer with chemotherapy and radiotherapy. Therefore, the combined effects and mechanisms of SMI and programmed death-1 (PD-1) inhibitor against NSCLC was focused on this study.

METHODS

A Lewis lung carcinoma mouse model and a lung squamous cell carcinoma humanized mouse model were used to investigate the combined efficacy and safety of SMI and PD-1 inhibitor. The synergistic mechanisms of the combination therapy against NSCLC were explored using single-cell RNA sequencing. Validation experiments were performed using immunofluorescence analysis, in vitro experiment, and bulk transcriptomic datasets.

RESULTS

In both models, combination therapy alleviated tumor growth and prolonged survival without increasing irAEs. The GZMA^high and XCL1^high natural killer (NK) cell subclusters with cytotoxic and chemokine signatures increased in the combination therapy, while malignant cells from combination therapy were mainly in the apoptotic state, suggesting that mediating tumor cell apoptosis through NK cells is the main synergistic mechanisms of combination therapy. In vitro experiment confirmed that combination therapy increased secretion of Granzyme A by NK cells. Moreover, we discovered that PD-1 inhibitor and SMI combination blocked inhibitory receptors on NK and T cells and restores their antitumoral activity in NSCLC better than PD-1 inhibitor monotherapy, and immune and stromal cells exhibited a decrease of angiogenic features and attenuated cancer metabolism reprogramming in microenvironment of combination therapy.

CONCLUSIONS

This study demonstrated that SMI reprograms tumor immune microenvironment mainly by inducing NK cells infiltration and synergizes with PD-1 inhibitor against NSCLC, suggested that targeting NK cells may be an important strategy for combining with ICIs. Video Abstract.

S100 as Serum Tumor Marker in Advanced Uveal Melanoma

S100 protein is routinely used as a serum tumor marker in advanced cutaneous melanoma. However, there is scarce and inconclusive evidence on its value in monitoring disease progression of uveal melanoma. In this monocenter study, we retrospectively assessed the connection between documented S100 protein levels of patients suffering from stage IV uveal melanoma and the clinical course of disease. Where available, we analyzed expression of S100 in melanoma metastases by immunohistochemistry. A total of 101 patients were included, 98 had available serum S100 levels, and in 83 cases, sufficient data were available to assess a potential link of S100 with the clinical course of the uveal melanoma. Only 12 of 58 (20.7%) patients had elevated serum levels at first diagnosis of stage IV disease. During progressive disease, 54% of patients showed rising serum S100 levels, while 46% of patients did not. Tumor material of 56 patients was stained for S100. Here, 26 (46.4%) showed expression, 19 (33.9%) weak expression, and 11 (19.6%) no expression of S100. Serum S100 levels rose invariably in all patients with strong expression throughout the course of disease, while patients without S100 expression in metastases never showed rising S100 levels. Thus, the value of S100 serum levels in monitoring disease progression can be predicted by immunohistochemistry of metastases. It is not a reliable marker for early detection of advanced disease.

The Potent G-Quadruplex-Binding Compound QN-302 Downregulates S100P Gene Expression in Cells and in an In Vivo Model of Pancreatic Cancer

The naphthalene diimide compound QN-302, designed to bind to G-quadruplex DNA sequences within the promoter regions of cancer-related genes, has high anti-proliferative activity in pancreatic cancer cell lines and anti-tumor activity in several experimental models for the disease. We show here that QN-302 also causes downregulation of the expression of the S100P gene and the S100P protein in cells and in vivo. This protein is well established as being involved in key proliferation and motility pathways in several human cancers and has been identified as a potential biomarker in pancreatic cancer. The S100P gene contains 60 putative quadruplex-forming sequences, one of which is in the promoter region, 48 nucleotides upstream from the transcription start site. We report biophysical and molecular modeling studies showing that this sequence forms a highly stable G-quadruplex in vitro, which is further stabilized by QN-302. We also report transcriptome analyses showing that S100P expression is highly upregulated in tissues from human pancreatic cancer tumors, compared to normal pancreas material. The extent of upregulation is dependent on the degree of differentiation of tumor cells, with the most poorly differentiated, from more advanced disease, having the highest level of S100P expression. The experimental drug QN-302 is currently in pre-IND development (as of Q1 2023), and its ability to downregulate S100P protein expression supports a role for this protein as a marker of therapeutic response in pancreatic cancer. These results are also consistent with the hypothesis that the S100P promoter G-quadruplex is a potential therapeutic target in pancreatic cancer at the transcriptional level for QN-302.

SIX3 function in cancer: progression and comprehensive analysis

The homeobox gene family encodes transcription factors that are essential for cell growth, proliferation, and differentiation, and its dysfunction is linked to tumor initiation and progression. Sine oculis homeobox (SIX) belongs to the homeobox gene family, with SIX3 being a core member. Recent studies indicate that SXI3 functions as a cancer suppressor or promoter, which is mainly dependent on SIX3's influence on the signal pathways that promote or inhibit cancer in cells. The low expression of SIX3 in most malignant tumors was confirmed by detailed studies, which could promote the cell cycle, proliferation, migration, and angiogenesis. The recovery or upregulation of SIX3 expression to suppress cancer is closely related to the direct or indirect inhibition of the Wnt pathway. However, in some malignancies, such as esophageal cancer and gastric cancer, SIX3 is a tumor-promoting factor, and repressing SIX3 improves patients' prognosis. This review introduces the research progress of SIX3 in tumors and gives a comprehensive analysis, intending to explain why SIX3 plays different roles in different cancers and provide new cancer therapy strategies.

Microenvironment components and spatially resolved single-cell transcriptome atlas of breast cancer metastatic axillary lymph nodes

As an indicator of clinical prognosis, lymph node metastasis of breast cancer has drawn great attention. Many reports have revealed the characteristics of metastatic breast cancer cells, however, the effect of breast cancer cells on the microenvironment components of lymph nodes and spatial transcriptome atlas remains unclear. In this study, by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, we investigate the transcriptional profiling of six surgically excised lymph node samples and the spatial organization of one positive lymph node. We identify the existence of osteoclast-like giant cells (OGC) which have high expressions of CD68 and CD163, the biomarkers of tumor-associated macrophages (TAMs). Through a spatially resolved transcriptomic method, we find that OGCs are scattered among metastatic breast cancer cells. In the lymph node microenvironment with breast cancer cell infiltration, TAMs are enriched in protumoral pathways including NF-κB signaling pathways and NOD-like receptor signaling pathways. Further subclustering demonstrates the potential differentiation trajectory in which macrophages develop from a state of active chemokine production to a state of active lymphocyte activation. This study is the first to integrate scRNA-seq and spatial transcriptomics in the tumor microenvironment of axillary lymph nodes, offering a systematic approach to delve into breast cancer lymph node metastasis.

S100 Proteins in Fatty Liver Disease and Hepatocellular Carcinoma

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent and slow progressing hepatic pathology characterized by different stages of increasing severity which can ultimately give rise to the development of hepatocellular carcinoma (HCC). Besides drastic lifestyle changes, few drugs are effective to some extent alleviate NAFLD and HCC remains a poorly curable cancer. Among the deregulated molecular mechanisms promoting NAFLD and HCC, several members of the S100 proteins family appear to play an important role in the development of hepatic steatosis, non-alcoholic steatohepatitis (NASH) and HCC. Specific members of this Ca²⁺-binding protein family are indeed significantly overexpressed in either parenchymal or non-parenchymal liver cells, where they exert pleiotropic pathological functions driving NAFLD/NASH to severe stages and/or cancer development. The aberrant activity of S100 specific isoforms has also been reported to drive malignancy in liver cancers. Herein, we discuss the implication of several key members of this family, e.g., S100A4, S100A6, S100A8, S100A9 and S100A11, in NAFLD and HCC, with a particular focus on their intracellular versus extracellular functions in different hepatic cell types. Their clinical relevance as non-invasive diagnostic/prognostic biomarkers for the different stages of NAFLD and HCC, or their pharmacological targeting for therapeutic purpose, is further debated.

Combining autophagy and immune characterizations to predict prognosis and therapeutic response in lung adenocarcinoma

Background

Autophagy, a key regulator of programmed cell death, is critical for maintaining the stability of the intracellular environment. Increasing evidence has revealed the clinical importance of interactions between autophagy and immune status in lung adenocarcinoma. The present study evaluated the potential of autophagy-immune-derived biomarkers to predict prognosis and therapeutic response in patients with lung adenocarcinoma.

Methods

Patients from the GSE72094 dataset were randomized 7:3 to a training set and an internal validation set. Three independent cohorts, TCGA, GSE31210, and GSE37745, were used for external verification. Unsupervised hierarchical clustering based on autophagy- and immune-associated genes was used to identify autophagy- and immune-associated molecular patterns, respectively. Significantly prognostic autophagy-immune genes were identified by LASSO analysis and by univariate and multivariate Cox regression analyses. Differences in tumor immune microenvironments, functional pathways, and potential therapeutic responses were investigated to differentiate high-risk and low-risk groups.

Results

High autophagy status and high immune status were associated with improved overall survival. Autophagy and immune subtypes were merged into a two-dimensional index to characterize the combined prognostic classifier, with 535 genes defined as autophagy-immune-related differentially expressed genes (DEGs). Four genes (C4BPA, CD300LG, CD96, and S100P) were identified to construct an autophagy-immune-related prognostic risk model. Survival and receiver operating characteristic (ROC) curve analyses showed that this model was significantly prognostic of survival. Patterns of autophagy and immune genes differed in low- and high-risk patients. Enrichment of most immune infiltrating cells was greater, and the expression of crucial immune checkpoint molecules was higher, in the low-risk group. TIDE and immunotherapy clinical cohort analysis predicted that the low-risk group had more potential responders to immunotherapy. GO, KEGG, and GSEA function analysis identified immune- and autophagy-related pathways. Autophagy inducers were observed in patients in the low-risk group, whereas the high-risk group was sensitive to autophagy inhibitors. The expression of the four genes was assessed in clinical specimens and cell lines.

Conclusions

The autophagy-immune-based gene signature represents a promising tool for risk stratification in patients with lung adenocarcinoma, guiding individualized targeted therapy or immunotherapy.

Expression of S100A9 and KL-6 in common interstitial lung diseases

By evaluating S100 calcium binding protein A9 (S100A9) and Klebs von den Lungen-6 (KL-6) expression in patients with 4 common interstitial lung diseases (ILDs), we aimed to investigate whether S100A9 or KL-6 can be of any value in the differential diagnosis of these ILDs and simultaneously signal the disease progression.We collected the data of patients diagnosed with the 4 ILDs and underwent fiber-optic bronchoscopy and BAL in the First Affiliated Hospital, China Medical University from January 2012 to December 2020. The data related to BGA, C-reactive protein, pulmonary function test, total number and fraction of cells, T lymphocyte subsets in bronchoalveolar lavage fluid (BALF), and the expression of S100A9 and KL-6 in BALF and serum were collected. We analyzed, whether S100A9 or KL-6 could serve as a biomarker for differential diagnosis between the 4 common ILDs; whether the levels of S100A9 and KL-6 correlated with each other; whether they were correlated with other clinical parameters and disease severity.This study included 98 patients, 37 patients with idiopathic pulmonary fibrosis (IPF), 12 with hypersensitivity pneumonitis, 13 with connective tissue disease-associated ILD, and 36 with sarcoidosis (SAR): stage I (18), stage II (9), stage III (5), and stage IV (4). The expression of KL-6 in BALF was significantly higher in IPF patients than other 3 groups (all P-value < .05). However, there was no significant difference in the levels of S100A9 in BALF and serum between the 4 groups (P-value > .05). The levels of S100A9 in BALF of IPF patients was positively and significantly correlated with KL-6 expression and the percentage of neutrophils in BALF (P-value < .05). Along with the stage increase of SAR patients, the level of S100A9 in BALF gradually increased, which was negatively and significantly correlated with the forced vital capacity/predicted, carbon monoxide diffusing capacity/predicted%, and PaO2 (all P-value < .05).The expression of KL-6 in BALF can be used as a biomarker to differentiate IPF from the other 3 common ILDs. While, this was not the case with expression of S100A9 in BALF and serum. However, the expression S100A9 in BALF is useful to indicate the progression of SAR. Thus, simultaneous measurement of KL-6 and S100A9 levels in BALF makes more sense in differential diagnosing of the 4 common ILDS.

Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers Using Machine Learning Techniques Applied to Lung Cancer

Background:

Nowadays, gene expression analysis is one of the most promising pillars for understanding and uncovering the mechanisms underlying the development and spread of cancer. In this sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained from older technologies, such as Microarray, which could still be useful to extract relevant knowledge.

Methods:

Throughout this research, a complete machine learning methodology to cross-evaluate the compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented. In order to show a real application of the designed pipeline, a lung cancer case study is addressed by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic datasets considered for our study have been obtained from the public repositories NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples belonging to these cancer subtypes have been developed.

Results:

The predictive models built using one technology are capable of discerning samples from a different technology. The classification results are evaluated in terms of accuracy, F1-score and ROC curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.

Conclusion:

Our method has the capability of finding strong gene signatures which are also independent of the transcriptomic technology used to develop the analysis. In addition, our article highlights the potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies, increasing the statistical significance of the results.

Identification S100A9 as a potential biomarker in neuroblastoma

BACKGROUND

More than half of Neuroblastoma (NB) patients presented with distant metastases and the relapse of metastatic patients was up to 90%. It is urgent to explore a biomarker that could facilitate the prediction of metastasis in NB patients.

METHODS AND RESULTS

In the present study, we systematically analyzed Gene Expression Omnibus datasets and focused on identifying the critical molecular networks and novel key hub genes implicated in NB metastasis. In total, 176 up-regulated and 19 down-regulated differentially expressed genes (DEGs) were identified. Based on these DEGs, a PPI network composed of 150 nodes and 452 interactions was established. Through PPI network identification combined with qRT-PCR, ELISA and IHC, S100A9 was screened as an outstanding gene. Furthermore, in vitro tumorigenesis assays demonstrated that S100A9 overexpression enhanced the proliferation, migration and invasion of NB cells.

CONCLUSIONS

Taken together, our findings suggested that S100A9 could participate in NB tumorigenesis and progression. In addition, S100A9 has the potential to be used as a promising clinical biomarker in the prediction of NB metastasis.