Integrating machine learning and single-cell trajectories to analyze T-cell exhaustion to predict prognosis and immunotherapy in colon cancer patients.

Introduction: The incidence of colon adenocarcinoma (COAD) has recently increased, and patients with advanced COAD have a poor prognosis due to treatment resistance. Combining conventional treatment with targeted therapy and immunotherapy has shown unexpectedly positive results in improving the prognosis of patients with COAD. More study is needed to determine the prognosis for patients with COAD and establish the appropriate course of treatment. Methods: This study aimed to explore the trajectory of T-cell exhaustion in COAD to predict the overall survival and treatment outcome of COAD patients. Clinical data were derived from the TCGA-COAD cohort through "UCSC", as well as the whole genome data. Prognostic genes driving T-cell trajectory differentiation were identified on the basis of single-cell trajectories and univariate Cox regression. Subsequently, T-cell exhaustion score (TES) was created by iterative LASSO regression. The potential biological logic associated with TES was explored through functional analysis, immune microenvironment assessment, immunotherapy response prediction, and in vitro experiments. Results: Data showed that patients with significant TES had fewer favorable outcomes. Expression, proliferation, and invasion of COAD cells treated with TXK siRNA were also examined by cellular experiments. Both univariate and multivariate Cox regression indicated that TES was an independent prognostic factor in patients with COAD; in addition, subgroup analysis supported this finding. Functional assay revealed that immune response and cytotoxicity pathways are associated with TES, as the subgroup with low TES has an active immune microenvironment. Furthermore, patients with low TES responded better to chemotherapy and immunotherapy. Conclusion: In this study, we systematically explored the T-cell exhaustion trajectory in COAD and developed a TES model to assess prognosis and provide guidelines for the treatment decision. This discovery gave rise to a fresh concept for novel therapeutic procedures for the clinical treatment of COAD.

New diterpenoids from the rhizomes of Hedychium forrestii.

A novel hexanorditerpenoid, hedychin C (1), and a new diterpenoid, hedychin D (2), were isolated from the rhizomes of Hedychium forrestii. Their structures and absolute configurations were unambiguously established by means of extensive spectroscopic data (IR, UV, HRMS, and NMR) and electronic circular dichroism (ECD) calculations. This is the first report of naturally occurring labdane-type hexanorditerpenoid. Compound 1 was proved to have moderate cytotoxicity against XWLC-05 cell line with an IC50 value of 53.6 µM.

Identification of Key mRNAs and Pathways in Colorectal Cancer.

Colorectal cancer (CRC) is the third most cancer-related death worldwide. This work aimed to identify potential hub genes and dysregulated pathways in the CRC by bioinformatics analysis. Three gene expression datasets were collected from GEO datasets, including tumor sample (N = 242) and adjacent nontumor tissue sample (N = 59). RankProd was used to discover the differential expressed genes between tumor and adjacent nontumor tissues for datasets generated by different laboratories. The gene set enrichment analysis conducted on the DE genes, followed by the protein-protein interaction (PPI) network. In total, 2,007 significant differential expression (DE) genes between tumor and adjacent nontumor tissues, include 1,090 upregulated genes and 917 downregulated genes in the tumor. The DE mRNAs are involved in cancer-related pathways. We comprehensively identified the CRC-related key mRNAs. Our data demonstrated combined different resources of transcriptomes will promote the understanding of the molecular mechanisms underlying CRC development and may be useful in discovering candidate molecular biomarkers for diagnosing, prognosis, and treating of CRC.

Genome-Wide Network-Based Analysis of Colorectal Cancer Identifies Novel Prognostic Factors and an Integrative Prognostic Index.

BACKGROUND/AIMS: Colorectal cancer (CRC) is one of leading cancers in both incidence and mortality rate. The 5-year survival rate varies considerably depending on the pathological stage of the tumor. Although prominent progress has been made through screening for survival-associated factors from a certain type of genetic or epigenetic modifications, few attempts have been made to apply a network-based approach in prognostic factor identification, which could prove valuable for a complex, multi-faceted disease such as CRC. METHODS: In this study, a TCGA dataset of 379 CRC patients was subjected to a network-based analysis strategy consisting of multivariate regression, co-expression network and gene regulatory network analyses, and survival analyses. Both genetic and epigenetic aberrations, including those in gene expression and DNA methylation at specific sites, were screened for significant association with patient survival. A prognostic index (PI) integrating all potential prognostic factors was subsequently validated for its prognostic value. RESULTS: A collection of six miRNAs, eleven mRNAs, and nine DNA methylation sites were identified as potential prognostic factors. The low- and high-risk patient groups assigned based on PI level showed significant difference in overall survival (hazard ratio = 1.32, 95% confidence interval 1.29-1.36, p < 0.0001). Patients in the low- and high-risk groups can be further divided into a total of four subgroups, based on pathological staging. In the two high-risk subgroups (PI > 0), there was significant different (Cox p < 0.0001) in OS between the earlier (stages I/II) and later stages (stages III/IV). However, in the two low-risk subgroups (PI < 0), earlier (stages I/II) and later stages (stages III/IV) showed no significant difference in OS (Cox p = 0.185). On the other hand, there were significant differences in OS between the low- and high-risk subgroups when both subgroups were of earlier stages (Cox p < 0.001) or of later stages (Cox p < 0.0001). CONCLUSION: The novel network-based, integrative analysis adopted in this study was efficient in screening for prognostic predictors. Along with PI, the set of 6 miRNAs, 11 mRNAs, and 9 DNA methylation sites could serve as the basis for improved prognosis estimation for CRC patients in future clinical practice.

MiR-370 promotes apoptosis in colon cancer by directly targeting MDM4.

MicroRNA (miR)-370 functions as a tumor suppressor or promoter in several cancers. However, the expression and biological role of miR-370 in colon cancer remains undefined. In the present study, miR-370 expression in both normal and malignant colon tissues was quantified by quantitative polymerase chain reaction. An in vitro cell viability and apoptosis assay and an in vitro xenograft tumor model were employed to investigate the role of miR-370 on colon cancer growth. Furthermore, the potential direct target of miR-370 was identified using a luciferase assay. Our results demonstrate that down-regulation of miR-370 expression occurs in malignant tissues and miR-370 expression is inversely correlated with tumor grade. Moreover, we determined that miR-370 functions as a tumor suppressor in colon cancer by inhibiting cell proliferation and promoting cell apoptosis. In addition, overexpression of miR-370 impairs xenograft tumor growth in nude mice. Mechanistically, mouse double minute 4 (MDM4) was demonstrated to be a potential direct target of miR-370, inducing apoptosis in colon cancer. Collectively, these findings suggest that upregulation of miR-370 may impair colon tumor growth by directly targeting MDM4. These findings provide a new direction for the diagnosis and treatment of colon cancer.

ATP-Dependent Lon Protease Contributes to Helicobacter pylori-Induced Gastric Carcinogenesis.

Helicobacter pylori infection is the strongest risk factor for development of gastric cancer. Host cellular stress responses, including inflammatory and immune responses, have been reported highly linked to H. pylori-induced carcinogenesis. However, whether mitochondrial regulation and metabolic reprogramming, which are potently associated with various cancers, play a role in H. pylori-induced gastric carcinogenesis is largely unknown. Here we revealed that Lon protease (Lonp1), which is a key inductive of mitochondrial unfolded protein response (UPR(mt)) and is required to maintain the mitochondrial quality, was greatly induced in H. pylori infected gastric epithelial cells. Importantly, we uncovered that knockdown of Lonp1 expression significantly diminished the metabolic switch to glycolysis and gastric cell proliferation associated with low multiplicity of H. pylori infection. In addition, Lonp1 overexpression in gastric epithelial cells also promoted glycolytic switch and cell overgrowth, suggesting H. pylori effect is Lonp1 dependent. We further demonstrated that H. pylori induced Lonp1 expression and cell overgrowth, at least partially, via HIF-1α regulation. Collectively, our results concluded the relevance of Lonp1 for cell proliferation and identified Lonp1 as a key regulator of metabolic reprogramming in H. pylori-induced gastric carcinogenesis.