Clustering analysis revealed the autophagy classification and potential autophagy regulators' sensitivity of pancreatic cancer based on multi-omics data

Multi-omics cluster defines the subtypes of CRC with distinct prognosis and tumor microenvironment

BACKGROUND

Colorectal cancer (CRC) is a complex malignancy characterized by diverse molecular profiles, clinical outcomes, and limited precision in prognostic markers. Addressing these challenges, this study utilized multi-omics data to define consensus molecular subtypes in CRC and elucidate their association with clinical outcomes and underlying biological processes.

METHODS

Consensus molecular subtypes were obtained by applying ten integrated multi-omics clustering algorithms to analyze TCGA-CRC multi-omics data, including mRNA, lncRNA, miRNA, DNA methylation CpG sites, and somatic mutation data. The association of subtypes with prognoses, enrichment functions, immune status, and genomic alterations were further analyzed. Next, we conducted univariate Cox and Lasso regression analyses to investigate the potential prognostic application of biomarkers associated with multi-omics subtypes derived from weighted gene co-expression network analysis (WGCNA). The function of one of the biomarkers MID2 was validated in CRC cell lines.

RESULTS

Two CRC subtypes linked to distinct clinical outcomes were identified in TCGA-CRC cohort and validated with three external datasets. The CS1 subtype exhibited a poor prognosis and was characterized by higher tumor-related Hallmark pathway activity and lower metabolism pathway activity. In addition, the CS1 was predicted to have less immunotherapy responder and exhibited more genomic alteration compared to CS2. Then a prognostic model comprising five genes was established, with patients in the high-risk group showing substantial concordance with the CS1 subtype, and those in the low-risk group with the CS2 subtype. The gene MID2, included in the prognostic model, was found to be correlated with epithelial-mesenchymal transition (EMT) pathway and distinct DNA methylation patterns. Knockdown of MID2 in CRC cells resulted in reduced colony formation, migration, and invasion capacities.

CONCLUSION

The integrative multi-omics subtypes proposed potential biomarkers for CRC and provided valuable knowledge for precision oncology.

Multiple functions and dual characteristics of RAB11A in cancers

Vesicle trafficking is an unceasing and elaborate cellular process that functions in material transport and information delivery. Recent studies have identified the small GTPase, Ras-related protein in brain 11A (RAB11A), as a key regulator in this process. Aberrant RAB11A expression has been reported in several types of cancers, suggesting the important functions and characteristics of RAB11A in cancer. These discoveries are of great significance because therapeutic strategies based on the physiological and pathological status of RAB11A might make cancer treatment more effective, as the molecular mechanisms of cancer development have not been completely revealed. However, these studies on RAB11A have not been reviewed and discussed specifically. Therefore, we summarize and discuss the recent findings of RAB11A involvement in different biological processes, including endocytic recycling regulation, receptors and adhesion molecules recycling, exosome secretion, phagophore formation and cytokinesis, as well as regulatory mechanisms in several tumor types. Moreover, contradictory effects of RAB11A have also been observed in different types of cancers, implying the dual characteristics of RAB11A in cancer, which are either oncogenic or tumor-suppressive. This review on the functions and characteristics of RAB11A highlights the value of RAB11A in inducing multiple important phenotypes based on vesicle trafficking and therefore will offer insights for future studies to reveal the molecular mechanisms, clinical significance, and therapeutic targeting of RAB11A in different cancers.

How A Patient with Resectable or Borderline Resectable Pancreatic Cancer should Be Treated-A Comprehensive Review

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with high morbidity and mortality in which long-term survival rates remain disastrous. Surgical resection is the only potentially curable treatment for early pancreatic cancer; however, the right patient qualification is crucial for optimizing treatment outcomes. With the rapid development of radiographic and surgical techniques, resectability decisions are made by a multidisciplinary team. Upfront surgery (Up-S) can improve the survival of patients with potentially resectable disease with the support of adjuvant therapy (AT). However, early recurrences are quite common due to the often-undetectable micrometastases occurring before surgery. Adopted by international consensus in 2017, the standardization of the definitions of resectable PDAC (R-PDAC) and borderline resectable PDAC (BR-PDAC) disease was necessary to enable accurate interpretation of study results and define which patients could benefit from neoadjuvant therapy (NAT). NAT is expected to improve the resection rate with a negative margin to provide significant local control and eliminate micrometastases to prolong survival. Providing information about optimal sequential multimodal NAT seems to be key for future studies. This article presents a multidisciplinary concept for the therapeutic management of patients with R-PDAC and BR-PDAC based on current knowledge and our own experience.

Characterization of neuroendocrine regulation- and metabolism-associated molecular features and prognostic indicators with aid to clinical chemotherapy and immunotherapy of patients with pancreatic cancer

The worldwide prevalence of pancreatic cancer has been rising in recent decades, and its prognosis has not improved much. The imbalance of substance and energy metabolism in tumour cells is among the primary causes of tumour formation and occurrence, which is often controlled by the neuroendocrine system. We applied Cox and LASSO regression analysis to develop a neuroendocrine regulation- and metabolism-related prognostic risk score model with three genes (GSK3B, IL18 and VEGFA) for pancreatic cancer. TCGA dataset served as the training and internal validation sets, and GSE28735, GSE62452 and GSE57495 were designated as external validation sets. Patients classified as the low-risk population (category, group) exhibited considerably improved survival duration in contrast with those classified as the high-risk population, as determined by the Kaplan-Meier curve. Then, we combined all the samples, and divided them into three clusters using unsupervised clustering analysis. Unsupervised clustering, t-distributed stochastic neighbor embedding (t-SNE), and principal component analysis (PCA) were further utilized to demonstrate the reliability of the prognostic model. Moreover, the risk score was shown to independently function as a predictor of pancreatic cancer in both univariate and multivariate Cox regression analyses. The results of gene set enrichment analysis (GSEA) illustrated that the low-risk population was predominantly enriched in immune-associated pathways. "ESTIMATE" algorithm, single-sample GSEA (ssGSEA) and the Tumor Immune Estimation Resource (TIMER) database showed immune infiltration ratings were enhanced in the low-risk category in contrast with the high-risk group. Tumour immune dysfunction and exclusion (TIDE) database predicted that immunotherapy for pancreatic cancer may be more successful in the high-risk than in the low-risk population. Mutation analysis illustrated a positive link between the tumour mutation burden and risk score. Drug sensitivity analysis identified 44 sensitive drugs in the high- and low-risk population. GSK3B expression was negatively correlated with Oxaliplatin, and IL18 expression was negatively correlated with Paclitaxel. Lastly, we analyzed and verified gene expression at RNA and protein levels based on GENPIA platform, HPA database and quantitative real-time PCR. In short, we developed a neuroendocrine regulation- and metabolism-associated prognostic model for pancreatic cancer that takes into account the immunological microenvironment and drug sensitivity.