Secreted phosphoprotein 1 as a potential prognostic and immunotherapy biomarker in multiple human cancers

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Pathological and Molecular Diagnosis of Uveal Melanoma

(1) Background: Uveal melanoma (UM) is a common malignant intraocular tumor that presents with significant genetic differences to cutaneous melanoma and has a high genetic burden in terms of prognosis. (2) Methods: A systematic literature search of several repositories on uveal melanoma diagnosis, prognosis, molecular analysis, and treatment was conducted. (3) Results: Recent genetic understanding of oncogene-initiation mutations in GNAQ, GNA11, PLCB4, and CYSLTR2 and secondary progression drivers of BAP1 inactivation and SF3B1 and EIF1AX mutations offers an appealing explanation to the high prognostic impact of adding genetic profiling to clinical UM classification. Genetic information could help better explain peculiarities in uveal melanoma, such as the low long-term survival despite effective primary tumor treatment, the overwhelming propensity to metastasize to the liver, and possibly therapeutic behaviors. (4) Conclusions: Understanding of uveal melanoma has improved step-by-step from histopathology to clinical classification to more recent genetic understanding of oncogenic initiation and progression.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

Revealing cell-cell communication pathways with their spatially coupled gene programs

Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.

Single-cell RNA sequencing highlights epithelial and microenvironmental heterogeneity in malignant progression of pancreatic ductal adenocarcinoma

Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). Single-cell transcriptomics provides a unique perspective for dissecting the epithelial and microenvironmental heterogeneity that accompanies progression from benign IPMNs to invasive PDAC. Single-cell RNA sequencing was performed through droplet-based sequencing on 35 693 cells from three high-grade IPMNs and two IPMN-derived PDACs (all surgically resected). Analysis of single-cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of noninvasive dysplasia to invasive cancer. For epithelial cells, we identified acinar-ductal cells and isthmus-pit cells enriched in IPMN lesions and profiled three types of PDAC-unique ductal cells. Notably, a proinflammatory immune component was distinctly observed in IPMNs, comprising CD4+ T cells, CD8+ T cells, and B cells, whereas M2 macrophages were significantly accumulated in PDAC. Through the analysis of cellular communication, the osteopontin gene (SPP1)-CD44 pathway between macrophages and epithelial cells were particularly strengthened in the PDAC group. Further prognostic analysis revealed that SPP1 is a biomarker of IPMN carcinogenesis for surveillance. This study demonstrates the ability to perform high-resolution profiling of single cellular transcriptomes during the progression of high-grade IPMNs to cancer. Notably, single-cell analysis provides an unparalleled insight into both epithelial and microenvironmental heterogeneity associated with early cancer pathogenesis and provides practical markers for surveillance and targets for cancer interception.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

SPP1 is associated with adverse prognosis and predicts immunotherapy efficacy in penile cancer

BACKGROUND

The effect of SPP1 in squamous cell carcinoma of the penis (PSCC) remained unknown. We attempted to clarify the function of the SPP1 gene in PSCC.

METHOD

Eight paired penile cancer specimens (including penile cancer tissue, paracancerous tissue, and positive lymph node tissue) subjected to whole transcriptome sequencing were analysed to identify differentially expressed genes. We used immunohistochemistry to detect the expression of SPP1 protein and immune cell related proteins in penile cancer tissue. Then, we performed weighted gene coexpression network analysis (WGCNA) to identify the genes related to SPP1 in penile cancer tissue and positive lymph node tissue. Based on the GSE57955 dataset, the CIBERSORT and ssGSEA algorithms were carried out to investigate the immune environment of PSCC. GSVA analysis was conducted to identify the signaling pathways related to SPP1 subgroups. Enzyme-linked immunosorbent assay (ELISA) method was adopted to detect SPP1 level in the serum of 60 patients with penile cancer.

RESULTS

Differential analysis indicated that SPP1 was the most differentially upregulated gene in both penile cancer tissues and positive lymph node tissues. Survival analysis suggested that the prognosis of the low-SPP1 group was significantly poorer than that of the high-SPP1 group. Subsequently, immune-related bioinformatics showed that SPP1 was significantly associated with B cells, CD8 + T cells, CD4 + T cells, macrophages, helper T cells, neutrophils and dendritic cells. The immunohistochemical results showed that the high-SPP1 group was characterized by relatively high expression of CD16 and relatively low expression of CD4. GSVA analysis indicated that high-SPP1 group was significantly associated with immune-related pathways such as PD-L1 expression and the PD-1 checkpoint pathway in cancer and the TNF signaling pathway. ELISA demonstrated that the serum level of SPP1 in patients with positive lymph node metastasis of penile cancer was significantly higher than that in patients with negative lymph node metastasis of penile cancer.

CONCLUSION

Our study shows that the SPP1 gene might be an effective biomarker for predicting the prognosis and the efficacy of immunotherapy in PSCC patients.

Cerebrospinal fluid exosomal protein alterations via proteomic analysis of NSCLC with leptomeningeal carcinomatosis

PURPOSE

Leptomeningeal carcinomatosis (LC) is a rare complication of non-small cell lung cancer (NSCLC) with highly mortality. Cerebrospinal fluid (CSF) as a special kind of tumor microenvironment (TME) better represents alterations than plasma. However, the clinical value of protein profiles of exosome in CSF as liquid biopsy remains unclear.

METHODS

In this study, CSF samples of NSCLC patients with (LC group) or without (NSCLC group) LC were collected and compared to patients without tumors (normal group). CSF exosomes were isolated by ultracentrifugation and protein profiles were performed by label-free proteomics. Differentially expressed proteins (DEPs) were detected by bioinformatics tools and verified by parallel reaction monitoring (PRM).

RESULTS

A total of 814 proteins were detected. Bioinformatics analysis revealed their shared function in the complement activation, extracellular region, and complement and coagulation cascades. Between LC and NSCLC group, 72 DEPs were found among which FN1 demonstrated the highest betweenness centrality (BC) after protein-protein interaction network analysis.

CONCLUSION

We investigated the application of label free and PRM based proteomics to detect key proteins related to LC. FN1 may serve as potential indicator to classify LC and NSCLC. Extracellular matrix (ECM) and epithelial-mesenchymal transition (EMT) are important in the process of LC. These data is promising for early prediction and diagnosis of LC.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Important oncogenic and immunogenic roles of SPP1 and CSF1 in hepatocellular carcinoma

The treatment and prognosis of liver cancer remain the focus of medical research. Studies have shown that SPP1 and CSF1 play important roles in cell proliferation, invasion, and metastasis. Therefore, this study analyzed the oncogenic and immunologic roles of SPP1 and CSF1 in hepatocellular carcinoma (HCC). We found that the expression levels of SPP1 and CSF1 in HCC were markedly increased and positively correlated. High SPP1 expression was significantly associated with poor OS, DSS, PFS, and RFS. It was not affected by gender, alcohol use, HBV, or race, whereas CSF1 was affected by these factors. Higher expression levels of SPP1 and CSF1 indicated higher levels of immune cell infiltration and a higher immune score with the R software package ESTIMATE. Further analysis revealed that many genes work co-expressed between SPP1 and CSF1 with the LinkedOmics database, which were mainly involved in signal transduction, the integral components of the membrane, protein binding, and osteoclast differentiation. In addition, we screened ten hub genes using cytoHubba, among which the expression of four genes was significantly associated with the prognosis of HCC patients. Finally, we demonstrated the oncogenic and immunologic roles of SPP1 and CSF1 using the vitro experiments. Reducing the expression of either SPP1 or CSF1 could significantly reduce the proliferation of HCC cells and the expression of CSF1, SPP1, and the other four hub genes. This study suggested that SPP1 and CSF1 interact with each other and have the potential to be therapeutic and prognostic targets for HCC.

Vitamin D deficiency triggers intrinsic apoptosis by impairing SPP1-dependent antiapoptotic signaling in chronic hematogenous osteomyelitis

Chronic hematogenous osteomyelitis (CHOM) is a common bone disease characterized by the development of sequestra after bacterial infection. Emerging evidence has shown that vitamin D (VD) deficiency raises the risk of osteomyelitis, but the underlying mechanisms remain obscure. Here, we establish a CHOM model in VD diet-deficient mice by intravenous inoculation of Staphylococcus aureus. Whole-genome microarray analyses using osteoblast cells isolated from sequestra reveal significant downregulation of SPP1 (secreted phosphoprotein 1). Molecular basis investigations show that VD sufficiency activates the VDR/RXR (VD receptor/retinoid X receptor) heterodimer to recruit NCOA1 (nuclear receptor coactivator 1) and transactivate SPP1 in healthy osteoblast cells. Secreted SPP1 binds to the cell surface molecule CD40 to activate serine/threonine-protein kinase Akt1, which then phosphorylates forkhead box O3a (FOXO3a), blocking FOXO3a-mediated transcription. By contrast, VD deficiency impairs the NCOA1-VDR/RXR-mediated overexpression of SPP1, leading to the inactivation of Akt1 and the accumulation of FOXO3a. FOXO3a then upregulates the expression of the apoptotic genes BAX (Bcl2-associated X-protein), BID (BH3 interacting death domain), and BIM (Bcl2-interacting mediator of cell death), to induce apoptosis. Administration of the NCOA1 inhibitor gossypol to the CHOM mice also promotes the occurrence of sequestra. VD supplementation can reactivate the SPP1-dependent antiapoptotic signaling and improve the outcomes of CHOM. Collectively, our data reveal that VD deficiency promotes bone destruction in CHOM by the removal of SPP1-dependent antiapoptotic signaling.

Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis

Background

Metastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood.

Methods

Thirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman’s analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype.

Result

Arecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein–protein interaction network construction, and Spearman’s correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis.

Conclusion

We found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.