Water blooms caused by the invasive cyanobacterium Raphidiopsis raciborskii occur in many reservoirs in the tropical and subtropical regions of China. In recent decades, this species has spread rapidly to temperate regions. Phenotypic plasticity and climate warming are thought to promote the worldwide dispersion of R. raciborskii. However, investigations into the genetic and phenotypic diversities of this species have revealed significant intraspecific heterogeneity. In particular, competition between R. raciborskii and Microcystis aeruginosa was highly strain dependent. Although the concept of an ecotype was proposed to explain the heterogeneity of R. raciborskii strains with different geographic origins, microevolution is more reasonable for understanding the coexistence of different phenotypes and genotypes in the same environment. It has been suggested that intraspecific heterogeneity derived from microevolution is a strong driving force for the expansion of R. raciborskii. Additionally, temperature, nutrient fluctuations, and grazer disturbance are critical environmental factors that affect the population establishment of R. raciborskii in new environments. The present review provides new insights into the ecological mechanisms underlying the invasion of R. raciborskii in Chinese freshwater ecosystems.

Background/Objectives: Lipid metabolism plays a critical role in tumor progression and immunotherapy efficacy in oral squamous cell carcinoma (OSCC). However, clinically applicable lipid metabolism-based models for predicting prognosis and immunotherapy response remain limited. This study aimed to develop and validate such a model in OSCC. Methods: Using transcriptomic data of OSCC from the TCGA database and a set of lipid metabolism-related genes (LMRGs), we constructed an LMRG-based risk score model via LASSO regression to predict patient survival. This model was subsequently validated using the independent GEO dataset GSE41613. Results: Patients in the high-risk group exhibited significantly poorer overall survival than those in the low-risk group (training cohort: p < 0.0001; validation cohort: p = 0.0086). We also developed a nomogram incorporating the risk score and clinical characteristics, and the risk score was identified as an independent prognostic factor for OSCC patients. Furthermore, the risk score was significantly associated with the tumor immune microenvironment; samples with a lower risk score showed elevated CD8⁺ T cell infiltration and a better response to immunotherapy. Additionally, the high-risk group exhibited an increased tumor mutation burden and resistance to most chemotherapeutic agents. Notably, several drugs (e.g., obatoclax mesylate) showed significant efficacy in the high-risk group, representing promising therapeutic candidates. Conclusions: This study reveals that the LMRG signature could serve as a valuable tool for prognosis assessment, risk stratification, and therapy guidance in OSCC.

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy characterized by frequent recurrence and metastasis, which poses a significant global health problem. One of the prominent hallmarks of cancer is glucose metabolic reprogramming, wherein glycolysis is preferred over oxidative phosphorylation for macromolecule biosynthesis and energy production, even in the presence of oxygen. Non-coding RNAs (ncRNAs) are defined as a class of RNAs that are not translated into proteins, which include microRNAs, long non-coding RNAs, and circular RNAs. Recent studies have found that ncRNAs are crucial in regulating glycolysis in OSCC, wherein they reshape the metabolic landscape by modulating the expression of glucose transporters, essential enzymes, and transcription factors, ultimately influencing tumorigenesis. This comprehensive review systematically summarizes the regulatory mechanisms of ncRNAs involved in glucose metabolic reprogramming in OSCC, evaluates their potential as diagnostic biomarkers and therapeutic targets, and identifies clinically relevant ncRNAs through an integrative analysis of patient-derived data. These insights provide a mechanistic understanding of the metabolic alterations that drive progression in OSCC, as well as knowledge that can facilitate the development of clinically translatable targeted interventions for this aggressive malignancy.