Background: Gastric cancer is a frequent and lethal solid tumor that has a poor prognosis and treatment result. Reprogramming of nucleotide metabolism is a characteristic of cancer development and progression. Methods: We used a variety of machine learning techniques to create a novel nucleotide metabolism-related index (NMRI) using gastric cancer sample data obtained from the TCGA and GEO databases. This index is based on genes associated to nucleotide metabolism. Gastric cancer patients were categorized into high and low NMRI groups based on NMRI results. The clinical features, tumor immune microenvironment, response to chemotherapy, and response to immunotherapy were then thoroughly examined. In vitro experiments were then used to confirm the biological role of SERPINE1 in gastric cancer. Results: The four nucleotide metabolism-related genes that make up NMRI (GAMT, ORC1, CNGB3, and SERPINE1) were verified in an external dataset and are a valid predictor of prognosis for patients with gastric cancer. The high NMRI group was more responsive to immunotherapy and had greater levels of immune cell infiltration than the low NMRI group. The proliferation and migration of stomach cancer was shown to be decreased by SERPINE1 knockdown in vitro. Conclusions: This study's NMRI can reliably predict a patient's prognosis for stomach cancer and pinpoint the patient group that will benefit from immunotherapy, offering important new information on the clinical treatment of stomach cancer.

The platelet-to-albumin ratio (PAR) was developed to evaluate inflammatory and nutritional status among patients. The primary goal of the current study was to gain insight into the prognostic role of PAR in critically ill patients with colorectal cancer (CRC). The secondary aim was to develop and verify a clinical model including PAR for the prediction of 28-day mortality. This observational, multicenter study used data from the Medical Information Mart for Intensive Care (MIMIC) IV, e-ICU databases, and Union cohort. Data from 776 critically ill patients with CRC were from the e-ICU database, 219 from the MIMC-IV database, and 135 from the Wuhan Union Hospital. Propensity score matching (PSM) analysis, along with inverse probability treatment weighting, was used to control the influence of confounding factors. Support vector machine (SVM) and LASSO Cox models were then applied to identify significant metrics associated with 28-day mortality in the test cohort. Receiver operating curve (ROC) analysis, along with sensitivity and specificity, was measured to assess the predictive performances of PAR and the survival nomogram. The threshold value for PAR was 8.6, and patients with high PAR (≥8.6) experienced higher 28-day mortality compared to those with low PAR (<8.6). ROC curve analyses revealed that the discriminative ability of PAR was better than platelet count and albumin alone. LASSO Cox regression along with SVM identified six significant metrics associated with 28-day mortality in critically ill patients with CRC, including PAR. The C-index of the critically ill CRC nomogram was 0.802 (0.744–0.859) in the e-ICU training cohort, 0.839 (0.779–0.899) in the e-ICU validation cohort, 0.787 (0.695–0.879) in the MIMIC-IV cohort, and 0.767 (0.703–0.831) in the Union cohort. PAR is a simple score that combines inflammatory and nutritional status. PAR was a reliable index to predict short-term survival outcome of critically ill patients with CRC. Moreover, a clinical nomogram incorporating PAR exhibited satisfactory performance for predicting 28-day mortality of critically ill patients with CRC.

Hepatocellular carcinoma (HCC) is a common and fatal malignancy characterized by poor patient prognosis and treatment outcome. The process of liquid–liquid phase separation in tumour cells alters the dysfunction of biomolecular condensation in tumour cells, which affects tumour progression and treatment. We downloaded the data of HCC samples from TCGA database and GEO database, and used a machine learning method to build a new liquid–liquid phase separation index (LLPSI) by liquid–liquid phase separation related genes. The LLPSI‐related column line Figure was constructed to provide a quantitative tool for clinical practice. HCC patients were divided into high and low LLPSI groups based on LLPSI, and clinical features, tumour immune microenvironment, chemotherapeutic response, and immunotherapeutic response were systematically analysed. LLPSI, which consists of five liquid–liquid phase separation‐associated genes (MAPT, WDR62, PLK1, CDCA8 and TOP2A), is a reliable predictor of survival in patients with HCC and has been validated in multiple external datasets. We found that the high LLPSI group showed higher levels of immune cell infiltration and better response to immunotherapy compared to the low LLPSI group, and LLPSI can also be used for prognostic prediction in various cancers other than HCC. In vitro experiments verified that knockdown of MAPT could inhibit the proliferation and migration of HCC. The LLPSI identified in this study can accurately assess the prognosis of patients with HCC and identify patient populations that will benefit from immunotherapy, providing valuable insights into the clinical management of HCC.

Background: Gastric cancer is a common malignant tumor characterized by poor prognosis and limited therapeutic options. The combination of Regulated cell death inducers and enhancement of the immune therapeutic effect plays an important role in cancer treatment.Methods: We downloaded and analyzed data from gastric cancer samples, collected 14 Regulated cell death-related genes and constructed a Regulated Cell Death-Related Index (RCDRI) by various machine learning methods. Based on the RCDRI, gastric cancer patients were divided into high RCDRI and low RCDRI groups, and the clinical characteristics, immune cell infiltration, chemotherapy response and immunotherapy response of gastric cancer patients were analyzed based on the RCDRI.Results: The newly constructed RCDRI consisted of four Regulated cell death-related genes (CD36, SERPINE1, TRIML2, and GRP) and has been shown to be an effective predictive marker for the survival of gastric cancer patients and was trained with multiple external datasets. The high RCDRI group had a higher level of immune cell infiltration and better response to immunotherapy than the low RCDRI group. In addition, through pan-cancer analysis, we found that RCDRI can also be used for prognosis and immunotherapy prediction in a variety of cancers. Finally, in vitro experiments revealed that TRIML2 knockdown inhibited the proliferation and migration of gastric cancer cells.Conclusions: The RCDRI identified in this study can accurately assess the prognosis and immunotherapy efficacy of gastric cancer patients, which lays a valuable foundation for future clinical treatment of gastric cancer.

Lysosomes play a crucial role in regulating the growth, invasion and metastasis of different tumour types. However, the specific function of lysosomes in hepatocellular carcinoma (HCC) remains uncertain. We retrieved gene expression and clinical data from the TCGA and GEO databases for HCC samples and established a new lysosome‐associated prognostic therapeutic index (LAPTI) based on lysosome‐related genes through machine learning. We then systematically analysed clinical characteristics, functional enrichment, tumour immune microenvironment, molecular docking, chemotherapy response and immunotherapy response in HCC. LAPTI, composed of four lysosome‐related genes (CTSV, LAPTM4B, DNAJC6, AP1M2), is a reliable prognostic indicator for hepatocellular carcinoma patients and is validated in external data sets. Compared with the low LAPTI group, the high LAPTI group showed poorer prognosis and higher immune cell infiltration levels. We also observed that knocking down CTSV in vitro inhibited the proliferation and migration of hepatocellular carcinoma. This study provides valuable insights into the future clinical treatment of hepatocellular carcinoma by accurately assessing the prognosis of patients with hepatocellular carcinoma.