Nanodrug delivery systems have revolutionized tumor therapy like never before. By overcoming the complexity of the tumor microenvironment (TME) and bypassing drug resistance mechanisms, nanotechnology has shown great potential to improve drug efficacy and reduce toxic side effects. This review examines the impact of the TME on drug resistance and recent advances in nanomedicine delivery systems to overcome this challenge. Characteristics of the TME such as hypoxia, acidity, and high interstitial pressure significantly reduce the effectiveness of chemotherapy and radiotherapy, leading to increased drug resistance in tumor cells. Then, this review summarizes innovative nanocarrier designs for these microenvironmental features, including hypoxia-sensitive nanoparticles, pH-responsive carriers, and multifunctional nanosystems that enable targeted drug release and improved drug penetration and accumulation in tumors. By combining nanotechnology with therapeutic strategies, this review offers a novel perspective by focusing on the innovative design of nanocarriers that interact with the TME, a dimension often overlooked in similar reviews. We highlight the dual role of these nanocarriers in therapeutic delivery and TME modulation, emphasize their potential to overcome drug resistance, and look at future research directions.

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

Background: Bladder cancer (BCa), among the world’s most common malignant tumors in the urinary system, has a high morbidity and mortality. Though cuproptosis is a new type of cell death mediated by lipoylated tricarboxylic acid (TCA) cycle proteins, the role of cuproptosis-related long noncoding RNAs (crlncRNAs) in bladder tumors awaits further elucidation. In this paper, we tried to explore how important crlncRNAs are for BCa.Methods: The crlncRNAs were first obtained through Pearson correlation analysis of the RNA-seq data and corresponding clinical data downloaded from The Cancer Genome Atlas (TCGA). Then, three lncRNAs were acquired by Cox regression and Lasso regression to build a prognostic model of crlncRNAs for verification. In the meantime, clinicopathological correlation analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, principal component analysis (PCA), immunoassay, and half-maximal inhibitory concentration prediction (IC50) were carried out. Then, an entire tumor was classified into two clusters by crlncRNA expression to further discuss the differences in prognosis, immune status and drug susceptibility among different subgroups.Results: We obtained a total of 152 crlncRNAs and built a risk model for screened crlncRNAs. We validated the model and found that calibration charts feature a high consistency in verifying nomogram prediction. Receiver operating characteristic (ROC) curve and univariate and multivariate Cox regression suggested that this model can be applied as an independent prognostic factor of bladder cancer due to its high accuracy. According to KEGG analysis, high-risk groups were enriched in cancer and immune-related pathways. During tumor immunoassay, noticeable differences were observed in both immune infiltration and checkpoints between high- and low-risk patients. Of the two subgroups divided among patients by consensus clustering, cluster 2 had a better prognosis, whereas cluster 1 had higher immunoreactivity scores, more immune cell infiltrations and immune checkpoint expressions, and different sensitivities to drugs.Conclusion: The research findings demonstrate that crlncRNAs can be used to predict the prognosis and immune microenvironment of patients suffering from BCa, and differentiate between BCa subgroups to improve the individual therapy of BCa.

The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance.

With the development of comprehensive treatment, locoregional transarterial chemotherapy has become an alternative conversion therapy, palliative therapy, and neoadjuvant therapy for many solid malignant tumors. Locoregional transarterial chemotherapy, which is most frequently used for treating liver cancer, has the characteristics of high regional efficacy and few systemic adverse reactions. In recent years, the number of relevant reports of locoregional chemotherapy for treating initially inoperable colorectal cancer (CRC), including non-metastatic and metastatic CRC, has gradually increased. However, the specific treatment options for such locoregional therapy are not the same, and its indications, medication regimens and combined treatments have not reached any consensus. In this review, the application status of locoregional transarterial chemotherapy in primary and metastatic CRC patients has been reviewed and summarized to provide a reference for future clinical work and scientific research.

Hypoxia plays a crucial role in the pathogenesis of various cancers, especially lung adenocarcinoma (LUAD), by altering cancer metabolism to promote escape mechanisms. Anoikis, a specialized form of programmed cell death, is evaded by LUAD cells during tumor progression and metastasis through upregulation of anti-apoptotic proteins. Investigating the impact of hypoxia-anoikis-related genes on prognosis and therapy prediction in LUAD is essential. Gene expression and clinical data from 489 LUAD patients and 49 normal tissues in The Cancer Genome Atlas (TCGA) dataset were used as the training set, while GSE72094, GSE31210, and GSE30219 datasets were used for validation. Weighted Gene Co-Expression Network Analysis (WGCNA) identified genes associated with hypoxia and anoikis. Machine learning models were evaluated using the C-index. Kaplan-Meier survival analysis, immune cell infiltration, tumor mutational burden (TMB), and sensitivity to therapy were assessed based on risk scores. A total of 21 hypoxia-anoikis-related prognostic genes were identified. The Random Survival Forest (RSF) model had the highest C-index. High-risk patients had significantly lower survival rates. Immune analysis showed higher immune infiltration in the low-risk group, with lower immune escape potential in these patients. Risk scores were correlated with sensitivity to targeted therapy and chemotherapy. MCF2 was identified as a key prognostic gene, and its knockdown inhibited LUAD cell proliferation and metastasis. These 21 genes offer insights into LUAD prognosis and therapy response, guiding personalized treatment strategies for LUAD patients.