A Pan-Cancer Analysis Reveals the Prognostic and Immunotherapeutic Value of Stanniocalcin-2 (STC2)

Vimentin, inversely correlating with infiltration of CD8 + T lymphocytes, promotes nuclear translocation of PD-L1 in esophageal squamous cell carcinoma

Vimentin has been considered a canonical marker of epithelial-mesenchymal transition (EMT) and is associated with tumor escape characterized by aberrant PD-L1 expression. However, whether there is a relationship between vimentin and PD-L1 in esophageal squamous cell carcinoma (ESCC) remains poorly understood. The immunological involvement of vimentin in ESCC was first analyzed by multiplex immunofluorescence staining in ESCC tissue microarray followed by a xenografted mouse model. In vivo, C57BL/6 mice were subcutaneously transplanted with AKR cells after stable silencing of vimentin. In vivo results showed that in addition to PD-L1 and PD-L2 expression, vimentin expression was inversely correlated with CD8+ T-cell infiltration. Mechanistically, vimentin can directly interact with PD-L1 and promote nuclear translocation of PD-L1 in AKR cells. In addition, SEMA6C, STC-2 and TRAILR2 were identified as cytokines modulated by vimentin. Blockade of STC-2 and TRAILR2 in co-culture with their own primary antibodies was shown to recruit more CD8+ T cells than controls. Together, these data strongly suggest targeting Vimenin to overcome the immune cycle in ESCC.

STC2 knockdown inhibits cell proliferation and glycolysis in hepatocellular carcinoma through promoting autophagy by PI3K/Akt/mTOR pathway

BACKGROUND

The pathogenesis exploration and timely intervention of hepatocellular carcinoma (HCC) are crucial due to its global impact on human health. As a general tumor biomarker, stanniocalcin 2 (STC2), its role in HCC remains unclear. We aimed to analyze the effect and mechanism of STC2 on HCC.

METHODS

STC2 expressions in HCC tissues and cell lines were measured. si-STC2 and oe-STC2 transfections were utilized to analyze how STC2 affected cell functions. Functional enrichment analysis of STC2 was performed by Gene Set Enrichment Analysis (GSEA). The regulatory mechanism of STC2 on HCC was investigated using 2-DG, 3-MA, IGF-1, Rap, and LY294002. The impact of STC2 on HCC progression in vivo was evaluated by the tumor formation experiment.

RESULTS

Higher levels of STC2 expression were observed in HCC tissues and cell lines. Besides, STC2 knockdown reduced proliferation, migration, and invasion, while inducing cell apoptosis. Further analysis indicated a positive correlation between STC2 and glycolysis. STC2 knockdown inhibited glycolysis progression and down-regulated the expressions of PKM2, GLUT1, and HK2 in HCC cells. However, treatment with glycolysis inhibitor (2-DG) prevented oe-STC2 from promoting the growth of HCC cells. Additionally, STC2 knockdown up-regulated the levels of LC3II/LC3I and Beclin1 and reduced the phosphorylation of PI3K, AKT, and mTOR. Treatment with 3-MA, IGF-1, Rap, and LY294002 altered the function of STC2 on proliferation and glycolysis in HCC cells. Tumor formation experiment results revealed that STC2 knockdown inhibited HCC progression.

CONCLUSIONS

STC2 knockdown inhibited cell proliferation and glycolysis in HCC through the PI3K/Akt/mTOR pathway-mediated autophagy induction.

Stanniocalcin Protein Expression in Female Reproductive Organs: Literature Review and Public Cancer Database Analysis

Stanniocalcin (STC) 1 and 2 serve as antihyperglycemic polypeptide hormones with critical roles in regulating calcium and phosphate homeostasis. They additionally function as paracrine and/or autocrine factors involved in numerous physiological processes, including female reproduction. STC1 and STC2 contribute to the pathophysiology of several diseases, including female infertility- and pregnancy-associated conditions, and even tumorigenesis of reproductive organs. This comprehensive review highlights the dynamic expression patterns and potential dysregulation of STC1 and STC2, restricted to female fertility, and infertility- and pregnancy-associated diseases and conditions, such as endometriosis, polycystic ovary syndrome (PCOS), abnormal uterine bleeding, uterine polyps, and pregnancy complications, like impaired decidualization, preeclampsia, and preterm labor. Furthermore, the review elucidates the role of dysregulated STC in the progression of cancers of the reproductive system, including endometrial, cervical, and ovarian cancers. Additionally, the review evaluates the expression patterns and prognostic significance of STC in gynecological cancers by utilizing existing public datasets from The Cancer Genome Atlas to help decipher the multifaceted roles of these pleiotropic hormones in disease progression. Understanding the intricate mechanisms by which STC proteins influence all these reviewed conditions could lead to the development of targeted diagnostic and therapeutic strategies in the context of female reproductive health and oncology.

Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning

Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here, we comprehensively analysed the hypoxia signalling in OS and further constructed novel hypoxia-related gene signatures for OS prediction and prognosis. This study employed Gene Set Enrichment Analysis (GSEA), Weighted correlation network analysis (WGCNA) and Least absolute shrinkage and selection operator (LASSO) analyses to identify Stanniocalcin 2 (STC2) and Transmembrane Protein 45A (TMEM45A) as the diagnostic biomarkers, which further assessed by Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and calibration curves in training and test dataset. Univariate and multivariate Cox regression analyses were used to construct the prognostic model. STC2 and metastasis were devised to forge the OS risk model. The nomogram, risk score, Kaplan Meier plot, ROC, DCA, and calibration curves results certified the excellent performance of the prognostic model. The expression level of STC2 and TMEM45A was validated in external datasets and cell lines. In immune cell infiltration analysis, cancer-associated fibroblasts (CAFs) were significantly higher in the low-risk group. And the immune infiltration of CAFs was negatively associated with the expression of STC2 (P < 0.05). Pan-cancer analysis revealed that the expression level of STC2 was significantly higher in Esophageal carcinoma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Lung squamous cell carcinoma (LUSC), and Stomach adenocarcinoma (STAD). Additionally, the higher expression of STC2 was associated with the poor outcome in those cancers. In summary, this study identified STC2 and TMEM45A as novel markers for the diagnosis and prognosis of osteosarcoma, and STC2 was shown to correlate with immune infiltration of CAFs negatively.

The Role and Clinical Relevance of Glycolysis-Associated Genes on Immune Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) poses a significant challenge with dismal survival rates, necessitating a deeper understanding of its molecular mechanisms and the development of improved therapies. Metabolic reprogramming, particularly heightened glycolysis, plays a crucial role in HCC progression. Glycolysis-associated genes (GAGs) emerge as key players in HCC pathogenesis, influencing the tumor microenvironment and immune responses. This study aims to investigate the intricate interplay between GAGs and the immune landscape within HCC, offering valuable insights into potential prognostic markers and therapeutic targets to enhance treatment strategies and patient outcomes. Through the exploration of GAGs, we have identified two distinct molecular glycolytic subtypes in HCC patients, each exhibiting significant differences in both the immune microenvironment and prognosis. A risk model comprising five key GAGs was formulated and subsequently evaluated for their predictive accuracy. Our findings underscore the diverse tumor microenvironment and immune responses associated with the varying glycolytic subtypes observed in HCC. The identified key GAGs hold promise as prognostic indicators for evaluating HCC risk levels, predicting patient outcomes, and guiding clinical treatment decisions, particularly in the context of anticipating responses to immunotherapy drugs.

Stanniocalcin-2 expression in glioblastoma - A novel prognostic biomarker: An observational study

The objective of this study was to assess the prognostic relevance of Stanniocalcin-2 (STC2) expression, as determined via immunohistochemistry in tumor tissue, in a cohort of 83 patients diagnosed with glioblastoma who underwent maximal safe surgical resection followed by radiotherapy concurrent with adjuvant temozolomide. STC2 expression levels were categorized using a 3-tiered semiquantitative system: negative expression (level 0-), low expression (level 1+), and high expression (levels 2 + and 3+). Patients were categorized into 2 distinct groups according to their STC2 expression levels: negative STC2 (-/+) and positive STC2 (++/+++). The primary outcome measure was the relationship between STC2 expression and progression-free survival (PFS), with overall survival (OS) serving as the secondary endpoint. Kaplan-Meier survival analysis confirmed that patients exhibiting high STC2 expression had significantly shorter OS (8 vs 20 months, P < .001) and PFS (6 vs 18 months, P < .001) than those with low or negative STC2 expression. Multivariate analysis revealed that STC2 expression was an independent prognostic factor for both OS (hazard ratio: 0.4; 95% confidence interval: 0.2-0.8; P < .05) and PFS (hazard ratio: 0.3; 95% confidence interval: 0.2-0.4; P < .05) in patients with glioblastoma. Furthermore, elevated STC2 expression in GBM was correlated with several established aggressive clinicopathological characteristics, including advanced age (≥65 years), low ECOG PS (≥2), and isocitrate dehydrogenase mutation negativity. These findings underscore that heightened STC2 expression within the tumor tissue of GBM patients functions as an adverse prognostic marker, correlating with an elevated risk of progression and reduced OS. Therapeutic interventions targeting the AKT-mTOR, ERK1-2, and mitogen-activated protein kinase pathways as well as immune checkpoint inhibitors and vascular endothelial growth factor blockade, as well as potential forthcoming antibody-drug conjugates targeting the STC2 molecule, have the potential to broaden the scope of combined treatment strategies.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis

BACKGROUND

The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome.

MATERIALS AND METHODS

To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein-protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed.

RESULTS

Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs.

CONCLUSION

In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.

Single-cell analysis reveals landscape of endometrial cancer response to estrogen and identification of early diagnostic markers

BACKGROUND

The development of endometrial cancer (EC) is closely related to the abnormal activation of the estrogen signaling pathway. Effective diagnostic markers are important for the early detection and treatment of EC.

METHOD

We downloaded single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) data of EC from public databases. Enrichment scores were calculated for EC cell subpopulations using the "AddModuleScore" function and the AUCell package, respectively. Six predictive models were constructed, including logistic regression (LR), Gaussian naive Bayes (GaussianNB), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and neural network (NK). Subsequently, receiver-operating characteristics with areas under the curves (AUCs) were used to assess the robustness of the predictive model.

RESULT

We classified EC cell coaggregation into six cell clusters, of which the epithelial, fibroblast and endothelial cell clusters had higher estrogen signaling pathway activity. We founded the epithelial cell subtype Epi cluster1, the fibroblast cell subtype Fib cluster3, and the endothelial cell subtype Endo cluster3 all showed early activation levels of estrogen response. Based on EC cell subtypes, estrogen-responsive early genes, and genes encoding Stage I and para-cancer differentially expressed proteins in EC patients, a total of 24 early diagnostic markers were identified. The AUCs values of all six classifiers were higher than 0.95, which indicates that the early diagnostic markers we screened have superior robustness across different classification algorithms.

CONCLUSION

Our study elucidates the potential biological mechanism of EC response to estrogen at single-cell resolution, which provides a new direction for early diagnosis of EC.

Investigation of LGALS2 expression in the TCGA database reveals its clinical relevance in breast cancer immunotherapy and drug resistance

Breast cancer (BRCA) is known as the leading cause of death in women worldwide and has a poor prognosis. Traditional therapeutic strategies such as surgical resection, radiotherapy and chemotherapy can cause adverse reactions such as drug resistance. Immunotherapy, a new treatment approach with fewer side effects and stronger universality, can prolong the survival of BRCA patients and even achieve clinical cure. However, due to population heterogeneity and other reasons, there are still certain factors that limit the efficacy of immunotherapy. Therefore, the importance of finding new tumor immune biomarker cannot be emphasized enough. Studies have reported that LGALS2 was closely related to immunotherapy efficacy, however, it is unclear whether it can act as an immune checkpoint for BRCA immunotherapy. In the current study, changes in LGALS2 expression were analyzed in public datasets such as TCGA-BRCA. We found that LGALS2 expression was associated with immune infiltration, drug resistance and other characteristics of BRCA. Moreover, high LGALS2 expression was closely related to immunotherapy response, and was associated with methylation modifications and clinical resistance for the first time. These findings may help to elucidate the role of LGALS2 in BRCA for the development and clinical application of future immunotherapy strategies against BRCA.

Development and validation of a combined hypoxia and ferroptosis prognostic signature for breast cancer

Background

Hypoxia is involved in tumor biological processes and disease progression. Ferroptosis, as a newly discovered programmed cell death process, is closely related to breast cancer (BC) occurrence and development. However, reliable prognostic signatures based on a combination of hypoxia and ferroptosis in BC have not been developed.

Method

We set The Cancer Genome Atlas (TCGA) breast cancer cohort as training set and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) BC cohort as the validation set. Least Absolute Shrinkage and Selection Operator (LASSO) and COX regression approaches were used to construct ferroptosis-related genes (FRGs) and hypoxia-related genes (HRGs) prognostic signature (HFRS). The CIBERSORT algorithm and ESTIMATE score were used to explore the relationship between HFRS and tumor immune microenvironment. Immunohistochemical staining was used to detect protein expression in tissue samples. A nomogram was developed to advance the clinical application of HFRS signature.

Results

Ten ferroptosis-related genes and hypoxia-related genes were screened to construct the HFRS prognostic signature in TCGA BC cohort, and the predictive capacity was verified in METABRIC BC cohort. BC patients with high-HFRS had shorter survival time, higher tumor stage, and a higher rate of positive lymph node. Moreover, high HFRS was associated with high hypoxia, ferroptosis, and immunosuppression status. A nomogram that was constructed with age, stage, and HFRS signature showed a strong prognostic capability to predict overall survival (OS) for BC patients.

Conclusion

We developed a novel prognostic model with hypoxia and ferroptosis-related genes to predict OS, and characterize the immune microenvironment of BC patients, which might provide new cures for clinical decision-making and individual treatment of BC patients.

RNA Extraction Method Impacts Quality Metrics and Sequencing Results in Formalin-Fixed, Paraffin-Embedded Tissue Samples

Archived formalin-fixed, paraffin-embedded (FFPE) tissue samples are being increasingly used in molecular cancer research. Compared with fresh-frozen tissue, the nucleic acid analysis of FFPE tissue is technically more challenging. This study aimed to compare the impact of 3 different RNA extraction methods on yield, quality, and sequencing-based gene expression results in FFPE samples. RNA extraction was performed in 16 FFPE tumor specimens from patients with diffuse large B-cell lymphoma and in reference FFPE material from microsatellite-stable and microsatellite-instable cell lines (3 replicates each) using 2 silica-based procedures (A, miRNeasy FFPE; C, iCatcher FFPE Tissue RNA) and 1 isotachophoresis-based procedure (B, Ionic FFPE to Pure RNA). The RNA yield; RNA integrity, as reflected by the distribution value 200; and RNA purity, as reflected by the 260/280 and the 260/230 nm absorbance ratios, were determined. The RNA was sequenced on the NovaSeq 6000 instrument using the TruSeq RNA Exome and SMARTer Stranded Total RNA-Seq Pico v3 library preparations kits. Our results highlight the impact of RNA extraction methodology on both preanalytical and sequencing-based gene expression results. Overall, methods B and C outperformed method A because these showed significantly higher fractions of uniquely mapped reads, an increased number of detectable genes, a lower fraction of duplicated reads, and better representation of the B-cell receptor repertoire. Differences among the extraction methods were generally more explicit for the total RNA sequencing method than for the exome-capture sequencing method. Importantly, the predicative value of quality metrics varies among extraction kits, and caution should be applied when comparing and interpreting results obtained using different methods.