Knockdown of TACC3 inhibits tumor cell proliferation and increases chemosensitivity in pancreatic cancer

Integrative analysis of T cell-mediated tumor killing-related genes reveals KIF11 as a novel therapeutic target in esophageal squamous cell carcinoma

BACKGROUND

Immune checkpoint inhibitors (ICIs) are emerging promising agents for the treatment of patients with esophageal squamous cell carcinoma (ESCC), however, there are only a small proportion respond to ICI therapy. Therefore, selecting candidate patients who will benefit the most from these drugs is critical. However, validated biomarkers for predicting immunotherapy response and overall survival are lacking. As the fundamental principle of ICI therapy is T cell-mediated tumor killing (TTK), we aimed to develop a unique TTK-related gene prognostic index (TTKPI) for predicting survival outcomes and responses to immune-based therapy in ESCC patients.

METHODS

Transcriptomic and clinical information of ESCC patients were from the GSE53625, GSE53624, GSE47404 and TCGA datasets. TTK-related genes were from the TISIDB database. The LASSO Cox regression model was employed to create the TTKPI. The prediction potential of the TTKPI was evaluated using the KM curve and time-dependent ROC curve analysis. Finally, the relationship between TTKPI and immunotherapy efficacy was investigated in clinical trials of ICIs (GSE91061, GSE135222, IMvigor210 cohort). The role of KIF11 in accelerating tumor progression was validated via a variety of functional experiments, including western blot, CCK-8, colony formation, wound healing scratch, and xenograft tumor model. The KIF11 expression was detected by multiplex fluorescent immunohistochemistry on tissue microarray from ESCC patients.

RESULTS

We constructed the TTKPI based on 8 TTK-related genes. The TTKPI low-risk patients exhibited better overall survival. TTKPI was significantly and positively correlated with the main immune checkpoint molecules levels. Furthermore, the low-risk patients were more prone to reap the benefits of immunotherapy in the cohort undergoing anti-PD-L1 therapy. Moreover, we performed functional experiments on KIF11, which ranked as the most significant prognostic risk gene among the 8 TTK-related genes. Our findings identified that KIF11 knockdown significantly hindered cell proliferation and mobility in ESCC cells. The KIF11 expression was negatively related with CD8+ T cell infiltration in ESCC patient samples.

CONCLUSIONS

The TTKPI is a promising biomarker for accurately determining survival and predicting the effectiveness of immunotherapy in ESCC patients. This risk indicator can help patients receive timely and precise early intervention, thereby advancing personalized medicine and facilitating precise immuno-oncology research. KIF11 plays a crucial role in driving tumor proliferation and migration and may act as a potential tumor biomarker of ESCC.

Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging

BACKGROUNDS

Non-small cell lung cancer (NSCLC) prognosis remains poor despite treatment advances, and classical prognostic indicators often fall short in precision medicine. Transforming acidic coiled-coil protein-3 (TACC3) has been identified as a critical factor in tumor progression and immune infiltration across cancers, including NSCLC. Predicting TACC3 expression through radiomic features may provide valuable insights into tumor biology and aid clinical decision-making. However, its predictive value in NSCLC remains unexplored. Therefore, we aimed to construct and validate a radiomic model to predict TACC3 levels and prognosis in patients with NSCLC.

MATERIALS AND METHODS

Genomic data and contrast-enhanced computed tomography (CT) images were sourced from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and The Cancer Imaging Archive (TCIA). A total of 320 cases of lung adenocarcinoma from TCGA and 122 cases of NSCLC from GEO were used for prognostic analysis. Sixty-three cases from TCIA and GEO were included for radiomics feature extraction and model development. The radiomics model was constructed using logistic regression (LR) and support vector machine (SVM) algorithms. We predicted TACC3 expression and evaluated its correlation with NSCLC prognosis using contrast-enhanced CT-based radiomics.

RESULTS

TACC3 expression significantly influenced NSCLC prognosis. High TACC3 levels were associated with reduced overall survival, potentially mediated by immune microenvironment and tumor progression regulation. LR and SVM algorithms achieved AUC of 0.719 and 0.724, respectively, which remained at 0.701 and 0.717 after five-fold cross-validation.

CONCLUSION

Contrast-enhanced CT-based radiomics can non-invasively predict TACC3 expression and provide valuable prognostic information, contributing to personalized treatment strategies.

The complement C3a/C3aR pathway is associated with treatment resistance to gemcitabine-based neoadjuvant therapy in pancreatic cancer

Gemcitabine is a standard first-line drug for pancreatic cancer chemotherapy. Nevertheless, gemcitabine resistance is common and significantly limits its therapeutic efficacy, impeding advancements in pancreatic cancer treatment. In this study, through a comprehensive analysis of gemcitabine-resistant cell lines and patient samples, 39 gemcitabine resistance-associated risk genes were identified, and two distinct gemcitabine response-related phenotypes were delineated. Through a combination of bioinformatics analysis and in vivo and in vitro experiments, we identified the C3a/C3aR signaling pathway as a pivotal player in the development of gemcitabine resistance in pancreatic cancer. We found that activation of the C3a/C3aR signaling pathway promoted the proliferation, migration and gemcitabine resistance of pancreatic cancer cells, while the C3aR antagonist SB290157 effectively counteracted these effects by impeding the activation of the C3a/C3aR pathway. Our study reveals the fundamental role of complement C3a in the progression of pancreatic cancer, suggesting that complement C3a may serve as a promising biomarker in pancreatic cancer.

A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer

Background

Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis.

Methods

Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed.

Results

A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells.

Conclusions

This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.