Gallbladder cancer (GBC) is a leading cause of cancer-related death worldwide, and its prognosis remains poor, with 5-year survival of approximately 5%. In this study, we analyzed the involvement of a novel proteoglycan, Sparc/osteonectin, cwcv, and kazal-like domains proteoglycan 1 (SPOCK1), in the tumor progression and prognosis of human GBC. SPOCK1 expression levels were measured in fresh samples and stored specimens of GBC and adjacent nontumor tissues. The effect of SPOCK1 on cell growth, DNA replication, migration and invasion were explored by Cell Counting Kit-8, colony formation, EdU retention assay, wound healing, and transwell migration assays, flow cytometric analysis, western blotting, and in vivo tumorigenesis and metastasis in nude mice. SPOCK1 mRNA and protein levels were increased in human GBC tissues compared with those in nontumor tissues. Immunohistochemical analysis indicated that SPOCK1 levels were increased in tumors that became metastatic, compared with those that did not, which was significantly associated with histological differentiation and patients with shorter overall survival periods. Knockdown of SPOCK1 expression by lentivirus-mediated shRNA transduction resulted in significant inhibition of GBC cell growth, colony formation, DNA replication, and invasion in vitro. The knockdown cells also formed smaller xenografted tumors than control GBC cells in nude mice. Overexpression of SPOCK1 had the opposite effects. In addition, SPOCK1 promoted cancer cell migration and epithelial-mesenchymal transition by regulating the expression of relevant genes. We found that activation of the PI3K/Akt pathway was involved in the oncogenic functions of SPOCK1 in GBC. SPOCK1 activates PI3K/Akt signaling to block apoptosis and promote proliferation and metastasis by GBC cells in vitro and in vivo. Levels of SPOCK1 increase with the progression of human GBC. SPOCK1 acts as an oncogene and may be a prognostic factor or therapeutic target for patients with GBC.

Background. The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME‐related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. Methods. We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME‐related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME‐related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half‐maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. Results. High immune and stromal scores are significantly associated with poor overall survival (p < 0.05). We identified 773 differential TME‐related prognostic genes associated with survival; these genes were enriched in immune‐related pathways. Nine key prognostic genes were identified and were used to construct a TME‐related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high‐risk group with worse survival outcomes, based on Kaplan‐Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high‐risk and low‐risk groups. High‐risk patients had higher expression of PD‐1, PDL‐1, and CTLA‐4 and lower expression of LAG‐3 and VSIR. Infiltration of dendritic cells was higher in the high‐risk group. Conclusions. We identified a novel prognostic TME‐related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.

Colon cancer (CC) is one of the most frequent malignancies in the world, with a high rate of morbidity and death. In CC, necroptosis and long noncoding RNA (lncRNAs) are crucial, but the mechanism is not completely clear. The goal of this study was to create a new signature that might predict patient survival and tumor immunity in patients with CC. Expression profiles of necroptosis-related lncRNAs in 473 patients with CC were retrieved from the TCGA database. A consensus clustering analysis based on differentially expressed (DE) genes and a prognostic model based on least absolute shrinkage and selection operator (LASSO) regression analysis were conducted. Clinicopathological correlation analysis, expression difference analysis, PCA, TMB, GO analysis, KEGG enrichment analysis, survival analysis, immune correlation analysis, prediction of clinical therapeutic compounds, and qRT–PCR were also conducted. Fifty-six necroptosis-related lncRNAs were found to be linked to the prognosis, and consensus clustering analysis was performed. There were substantial variations in survival, immune checkpoint expression, clinicopathological correlations, and tumor immunity among the different subgroups. Six lncRNAs were discovered, and patients were split into high-risk and low-risk groups based on a risk score generated using these six lncRNAs. The survival time of low-risk patients was considerably longer than that of high-risk patients, indicating that these lncRNAs are directly associated with survival. The risk score was associated with the tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. After univariate and multivariate Cox regression analysis, the risk score and tumor stage remained significant. Cancer- and metabolism-related pathways were enriched by KEGG analyses. Immune infiltration was shown to differ significantly between high- and low-risk patients in a tumor immunoassay. Eight compounds were screened out, and qRT–PCR confirmed the differential expression of the six lncRNAs. Overall, in CC, necroptosis-related lncRNAs have an important function, and the prognosis of patients with CC can be predicted by these six necroptosis-related lncRNAs. They may be useful in the future for customized cancer therapy.

Background: The transcription factor Spi-1 proto-oncogene (SPI1, also known as PU.1) is a key regulator of signal communication in the immune system and is essential for the development of myeloid cells and lymphocytes. However, the potential role of SPI1 in gastric cancer (GC) and the correlations between SPI1 and immune infiltration remain unclear.Methods: In the present study, multiple databases including ONCOMINE, TIMER, Kaplan–Meier Plotter, and The Cancer Genome Atlas were used to explore the expression levels and prognostic value of SPI1 in GC. cBioPortal was used to explore the possible reasons for the increased expression of SPI1 in GC. The correlations between SPI1 expression and tumor-infiltrating immune cells (TICs) were analyzed using CIBERSORT and TIMER. Gene set enrichment analysis was used to determine the biological function of SPI1 in the development of GC. In addition, a risk signature based on SPI1-related immunomodulators was constructed to accurately evaluate the prognosis of patients with GC. The upregulation of SPI1 expression in GC was further confirmed through immunohistochemistry, western blotting, and real-time quantitative PCR (RT-qPCR) assay.Results: The expression of SPI1 was increased significantly in GC according to multiple databases, and high expression of SPI1 was related to poor prognosis and progression of GC. The main factor influencing the high expression of SPI1 mRNA in GC may be diploidy, not DNA methylation. Moreover, immunohistochemistry, western blotting, and RT-qPCR assays also confirmed the upregulated expression of SPI1 in GC. CIBERSORT analysis revealed that SPI1 expression was correlated with seven types of TICs (naive B cells, resting memory CD4 T cells, activated memory CD4 T cells, activated natural killer cells, resting natural killer cells, M2 macrophages, and resting dendritic cells). Gene set enrichment analysis indicated that SPI1 might be related to immune activation in GC and participate in cell cycle regulation. In addition, based on SPI1-related immunomodulators, we developed multiple-gene risk prediction signatures and constructed a nomogram that can independently predict the clinical outcome of GC.Conclusion: The results of the present study suggest that SPI1 has a critical role in determining the prognosis of GC patients and may be a potential immunotherapeutic target.