A novel strategy for combination of clofarabine and pictilisib is synergistic in gastric cancer

Identification of SUMOylation-related biomarkers in papillary thyroid carcinoma

BACKGROUND

Small ubiquitin-like modifier (SUMO) modification is increasingly recognized as critical in tumorigenesis and progression. This study identifies biomarkers linked to SUMOylation in papillary thyroid carcinoma (PTC), aiming to advance therapeutic and prognostic strategies.

METHODS

Employing PTC datasets and SUMO related genes (SRGs), we utilized univariate Cox regression for prognosis-related SRGs, conducted differential expression analyses, and integrated findings to pinpoint candidate genes. These genes underwent further validation through survival, gene set enrichment, immune infiltration, and drug sensitivity analyses, including external validation via quantitative RT-qPCR. In our final step, we conducted immunohistochemical staining on tumor samples from PTC patients at our center and integrated this with their clinical data to validate BMP8A's effectiveness in predicting recurrence in PTC.

RESULTS

Three biomarkers-BMP8A, RGS8, and SERPIND1-emerged as significant. Gene Set Enrichment Analysis (GSEA) showed their involvement in immune-related pathways, with differential immune infiltration patterns and drug response correlations observed, underscoring their potential for targeted therapy. Lastly, we validated the efficacy of BMP8A in predicting the recurrence of PTC in patients using clinical and pathological data from our center.

CONCLUSION

The study identifies BMP8A, RGS8, and SERPIND1 as key biomarkers associated with SUMOylation in PTC. Their linkage to immune response and drug sensitivity highlights their importance as targets for therapeutic intervention and prognosis in PTC research.

Xenograft and organoid models in developing precision medicine for gastric cancer (Review)

Gastric cancer (GC), a highly heterogeneous disease, has diverse histological and molecular subtypes. For precision medicine, well‑characterized models encompassing the full spectrum of subtypes are necessary. Patient‑derived tumor xenografts and organoids serve as important preclinical models in GC research. The main advantage of these models is the retention of phenotypic and genotypic heterogeneity present in parental tumor tissues. Utilizing diverse sequencing techniques and preclinical models for GC research facilitates accuracy in predicting personalized clinical responses to anti‑cancer treatments. The present review summarizes the latest advances of these two preclinical models in GC treatment and drug response assessment.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma

Background

Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC.

Methods

Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments.

Results

By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs.

Conclusions

In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

Silencing Glypican-1 enhances the antitumor effects of Pictilisib via downregulating PI3K/Akt/ERK signaling in chemo-resistant esophageal adenocarcinoma

Poorly differentiated esophageal adenocarcinoma (PDEAC) has a dismal prognosis. Glypican-1(GPC-1) is known to be upregulated in several cancer types in contrast to healthy tissues, rendering it as a biomarker. Nevertheless, the potential therapeutic targeting of GPC-1 has not been explored in PDEAC. There is accumulating evidence that GPC-1, via upregulation of PI3K/Akt/ERK signaling, plays a crucial role in the progression and chemoresistance in cancer. Pictilisib, a class I pan PI3K inhibitor, has shown promising antitumor results in clinical trials, however, has not gained widespread success due to acquired drug resistance. This study investigated the role of GPC-1 in chemo-resistant PDEAC and appraises the impact of targeted silencing of GPC-1 on the antitumor effects of Pictilisib in PDEAC cell lines. Immunohistochemistry assays in PDEAC tissue specimens demonstrated a pronounced intensity of staining with GPC-1. Upregulation of GPC-1 was found to be correlated with advanced stage and poor prognosis. In-vitro studies examined the influence of GPC-1 knockdown and Pictilisib, both as individual agents and in combination, on cytotoxicity, cell cycle distribution, apoptosis, and gene expression profiles. Silencing GPC-1 alone showed significantly reduced cell viability, migration, colony formation, epithelial-mesenchymal transition, and stemness in PDEAC cells. Significantly, knockdown of GPC-1 combined with low-dose Pictilisib led to enhancement of cytotoxicity, cell cycle arrest, and apoptosis in ESO-26 and OE-33 cells. In the xenograft mouse model, the combination of Pictilisib and GPC-1 knockdown exhibited synergy. These findings suggest that GPC-1 represents a promising target to augment chemosensitivity in esophageal adenocarcinoma.

Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related prognostic biomarkers for predicting immunotherapeutic response in hepatocellular carcinoma

Introduction

Natural killer (NK) cells play an irreplaceable and important role as a subtype of innate immune cells in the contemporary setting of antitumor immunity.

Methods

We chose a total of 1,196 samples for this analysis from the public dataset’s six separate cohorts. To identify 42 NK cell marker genes, we first carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocellular carcinoma (HCC).

Results

Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns. This signature’s prognostic prediction ability was well verified across several validation cohorts. Patients with high scores had higher TIDE scores but lower immune cell infiltration percentages. Importantly, low-scoring patients had superior immunotherapy response and prognosis than high-scoring patients in an independent immunotherapy cohort (IMvigor210). Finally, we used CD56 and TUBA1B antibodies for immunohistochemical labeling of HCC tissue sections, and we discovered a lower number of CD56+ cells in the HCC tissue sections with high TUBA1B expression.

Discussion

In summary, our research created a unique prognostic profile based on NK cell marker genes that may accurately predict how well immunotherapy would work for HCC patients.