Abstract

   graphic file with name ao4c01215_0018.jpg

   Previous studies have demonstrated the regulatory roles of
   Transmembrane protein 147 (TMEM147) in various diseases, including
   cancer. However, systematic pan-cancer analyses investigating the role
   of TMEM147 in diagnosis, prognosis, and immunological prediction are
   lacking. An analysis of data from The Cancer Genome Atlas (TCGA)
   revealed differential TMEM147 expression across various types of cancer
   as well as within immune and molecular cancer subtypes. Moreover, high
   TMEM147 expression was associated with poor disease-specific survival
   (DSS), overall survival (OS), and progression-free interval (PFI)
   across cancers, suggesting its potential as a prognostic biomarker. Our
   study further revealed a significant correlation between TMEM147
   expression and T helper cell and Tcm cell infiltration in most cancer
   types. In the case of liver hepatocellular carcinoma (LIHC), the effect
   of TMEM147 on prognosis varied among different clinical subtypes.
   Additionally, functional enrichment analysis revealed an association
   between TMEM147 and metabolic pathways. Finally, experiments on the
   MIHA cell line and four LIHC cell lines confirmed the role of TMEM147
   in promoting liver cancer cell proliferation, further confirming the
   clinical value of TMEM147 in liver cancer diagnosis. Our findings
   suggest that TMEM147 may serve as a diagnostic and prognostic biomarker
   across cancers while also playing a significant role in LIHC.

Introduction

   Membrane proteins account for a significant proportion of total
   cellular proteins; these proteins are anchored to cell membranes and
   play crucial roles in regulating intercellular interactions and
   cellular interactions with the surrounding environment. Due to their
   inter- and intracellular biological signaling functions, numerous
   membrane proteins have been implicated in cancer development, cancer
   cell metastasis, and immune-related disorders.^[36]1 These poorly
   characterized transmembrane proteins belong to the Transmembrane
   protein (TMEM) family, which includes a diverse range of transmembrane
   proteins that are found in various cellular membranes, such as
   endoplasmic reticulum (ER), mitochondrial, and lysosomal membranes.
   TMEM proteins are involved in many biological processes,^[37]2 and TMEM
   proteins have been demonstrated to be involved in tumor metastasis. For
   instance, TMEM48 contributes to the invasive metastasis of lung cancer
   cells,^[38]3 while TMEM206 enhances the metastasis and invasion of
   colon cancer cells by activating the AKT and FAK pathways.^[39]4
   Additionally, TMEM176 expression has been shown to be increased in
   lymphoma, breast cancer, liver cancer, and other malignancies compared
   to normal tissues.^[40]5 These findings collectively indicate that TMEM
   proteins are promising potential biomarkers and prognostic indicators
   in cancer.

   TMEM147, which is a member of the TMEM family, is a transmembrane
   protein that includes seven transmembrane domains. TMEM147 has a
   molecular mass of 25 kDa and predominantly localizes to the ER membrane
   and the nuclear envelope. Moreover, TMEM147 is an integral component of
   the nicalin-TMEM147-NOMO protein complex.^[41]6,[42]7 Based on
   published studies, dysregulation of TMEM147 expression may contribute
   to the pathogenesis of various diseases via diverse mechanisms. A
   clinical study demonstrated that TMEM147 deficiency leads to the
   upregulation of CKAP4 (CLIMP-63) and RTN4 (NOGO), accompanied by ER
   redirection. Children with TMEM147 deficiency present consistent
   clinical features, such as coarse facial features, developmental
   delays, intellectual disability, and behavioral problems. These
   findings suggest that biallelic loss-of-function TMEM147 variants cause
   syndromic intellectual disability, ER translocations, and nuclear
   tissue dysfunction.^[43]6 TMEM147 is a membrane receptor that interacts
   with galactose lectin, thereby influencing both host innate immune
   responses and parasite immune responses by regulating cellular
   proliferation and phagocytosis. Additionally, TMEM147 mediates the
   transcription and production of nitric oxide, transforming growth
   factor β1 (TGF-β1), and interleukin 10 (IL-10).^[44]8 Furthermore,
   knockdown of TMEM147 substantially decreases the abundance of the
   nuclear envelope protein lamin B receptor (LBR) and causes its
   mislocalization within the ER. Additionally, TMEM147 physically
   interacts with DHCR7, which is an enzyme that is crucial for
   cholesterol biosynthesis; thus, TMEM147 is an important novel regulator
   of intracellular cholesterol homeostasis.^[45]9 Moreover, TMEM147 is a
   major activator of NF-κB in chondrocytes and is expressed at elevated
   levels in chondrocytes from individuals with rheumatoid arthritis (RA);
   these findings suggest its involvement in the pathogenesis of
   RA.^[46]10 Furthermore, TMEM147 exhibits differential expression
   patterns in colon cancer and osteosarcoma, making it a potential tumor
   biomarker.^[47]2,[48]11 Despite reports on the role of TMEM147 in
   diverse physiopathological processes, the functional characteristics of
   TMEM147 remain poorly understood; in particular, systemic analyses
   across cancers are lacking.

   By integrating data from The Cancer Genome Atlas (TCGA) and the Gene
   Expression Omnibus (GEO) databases, our recent study revealed
   differential expression of TMEM147 in more than 20 distinct types of
   cancer. This differential expression was evident even among various
   molecular or immune subtypes within the same type of cancer, suggesting
   that TMEM147 may serve as a pan-cancer biomarker. Furthermore, analyses
   of the associations between TMEM147 expression and overall survival
   (OS), disease-specific survival (DSS), and progression-free interval
   (PFI) revealed that elevated TMEM147 expression is associated with
   unfavorable prognosis. Moreover, Kyoto Encyclopedia of Genes and
   Genomes (KEGG), Gene Ontology (GO), and GSEA analyses were performed to
   determine shared biological pathways and to conduct enrichment analysis
   of the differentially expressed genes. Subsequently, we focused on
   liver hepatocellular carcinoma (LIHC) and revealed correlations between
   TMEM147 expression and various clinical characteristics. Finally,
   experiments on liver cell lines were conducted to confirm the role of
   TMEM147 in liver cancer. The current investigation of the role of
   TMEM147 across cancers will undoubtedly provide additional evidence to
   facilitate subsequent clinical translation.

Materials and Methods

Data Preprocessing and Gene Expression Analysis

   Transcriptional RNA sequencing (RNA-Seq) expression profiles and the
   corresponding clinical information for 33 types of cancer were obtained
   from the TCGA database sharing data portal
   ([49]https://portal.gdc.cancer.gov/) and the Genotype-Tissue Expression
   (GTEx) database ([50]https://commonfund.nih.gov/GTEx) from UCSC Xena
   ([51]https://xenabrowser.net/datapages/). The data were transformed to
   transcripts per kilobase million (TPM) via the Toil process.^[52]12 R
   software v3.6.3 was used for statistical analysis, and the ggplot2
   package was used for visualization. The Wilcoxon signed rank test was
   used to analyze differences among all the transcriptomic data, and P <
   0.05 was considered to indicate statistical significance (ns, P ≥ 0.05;
   *P < 0.05; **P < 0.01; ***P < 0.001).

Diagnostic Value Analysis

   Receiver operating characteristic (ROC) curves were used to assess the
   diagnostic value of TMEM147 in 33 types of cancer, and the pROC package
   was used for analysis. The area under the curve (AUC) ranged from 0.5
   to 1.0; 0.5–0.7 indicated low accuracy, 0.7–0.9 indicated medium
   accuracy, and 0.9 or higher indicated high accuracy.

Analysis of TMEM147 Expression in Different Immunological and Molecular
Subtypes of Human Cancers

   The TISIDB database ([53]http://cis.hku.hk/TISIDB/index.php), which is
   a web portal for information on tumor and immune system interactions
   that integrates multiple heterogeneous data types, was used to
   determine the relationships between TMEM147 expression and immune or
   molecular subtypes across cancers. Results with a P value <0.05 were
   considered statistically significant, and they are shown in
   [54]Results.

Interaction Network Analysis and Functional Clustering Analysis

   The String ([55]https://string-db.org/) database was used to construct
   a protein–protein interaction (PPI) network of TMEM147. The network was
   constructed by searching for the protein name of TMEM147 in Homo
   sapiens and setting the main parameters to medium confidence and active
   interaction sources. Cytoscape (v3.9.1) was used for visualization.

   GO and KEGG enrichment analyses were conducted with the clusterProfiler
   package (v3.14.3), and the results were visualized with the ggplot2
   package.^[56]13 The KEGG pathways and GO_BP terms were examined using
   the gene set enrichment analysis (GSEA) function module.

Prognosis Analysis

   The relationships between TMEM147 and OS and between DSS and the PFI
   were investigated using Kaplan–Meier plots. The Survminer package
   (v0.4.9) was used for visualization, and the survival package was used
   for statistical analysis of survival data.^[57]14 All the data were
   obtained from the TCGA database in HTSeq-FPKM format and transformed
   into the TPM format.

Clinical Correlation Analysis in LIHC

   Data from the TCGA-LIHC database in the level 3 HTSeq-fragments per
   kilobase per million (FPKM) format were downloaded from the TCGA
   database, converted to the transcripts per million reads (TPM) format,
   and then analyzed after log2 transformation. Ggplot2 (v3.3.3) was used
   for visualization. Different clinical characteristics related to LIHC,
   such as age, weight, AFP (ng/mL), and Child–Pugh score, were included.

Coexpression Analysis and Enrichment Analysis of TMEM147 in LIHC

   Genes that were coexpressed with TMEM147 were identified by R based on
   the expression profiles of the TCGA cohort. The top 50 genes that were
   positively and negatively related to TMEM147 in LIHC were identified,
   and they are shown. Ggplot2 was used for visualization. Then, we used
   all of the genes that were coexpressed with TMEM147 in LIHC for
   enrichment analysis.

Analysis of Immune Cell Infiltration across Cancer Types and in LIHC

   The markers of 24 kinds of immune cells were confirmed^[58]15 using the
   ssGSEA immune infiltration algorithm and GSVA package to determine the
   immune profiles across cancer types and in LIHC.^[59]16

Cell Culture

   The cell lines that were used in this study included the MIHA,
   BEL-7402, SMMC 7721, HepG2, and Huh-7 cell lines. MIHA cells were
   cultured in 1640 medium (Gibco, USA) supplemented with 10% fetal bovine
   serum (FBS) (Gibco, USA). The remaining cell lines were cultured in
   DMEM (Gibco, USA) supplemented with 10% FBS. All the cell cultures were
   maintained in a humidified incubator at 37 °C with 5% CO[2].
   Subculturing was routinely performed when the cells reached 80–90%
   confluence using trypsin-EDTA (Gibco, USA). Cell viability was assessed
   during each passage using the trypan blue exclusion assay (Gibco, USA).

Real-Time PCR and Western Blotting

   Real-time PCR and western blotting were used to analyze gene
   expression. For real-time PCR, cDNA was synthesized by reverse
   transcription, followed by amplification with gene-specific primers.
   The sequences of the synthetic primers are shown in [60]Table [61]1
   . Western blotting analysis included protein extraction, protein
   separation by SDS–PAGE, protein transfer to PVDF membranes, and protein
   detection using primary antibodies against the proteins of interest.
   The utilization of these established methodologies ensured the reliable
   and accurate evaluation of gene expression in our study.

Table 1. Sequences of Synthetic Primers and siRNAs.

   gene sequence
   Homo-TMEM147 forward: 5′- CGGGGCATTGAGTTTGACTG-3′; reverse: 3′-
   AGACGCGACGATGTAATGGA-5′
   siRNA-1 forward: 5′-CCUACUUCAUCACCUACAATT-3′; reverse: 3′-
   UUGUAGGUGAUGAAGUAGGTT-5′
   siRNA-2 forward: 5′-GCAGACCUGAUAGGUCUAATT-3′; reverse: 3′-
   UUAGACCUAUCAGGUCUGCTT-5′
   siRNA-3 forward: 5′- CCACUGCUGAGCUUAUUAUTT-3′; reverse: 3′-
   AUAAUAAGCUCAGCAGUGGTT-5′
   siRNA-4 forward: 5′- GUGUCUACAAGGCCUUUGUTT-3′; reverse: 3′-
   ACAAAGGCCUUGUAGACACTT-5′
   [62]Open in a new tab

Cell Colony Formation Assay

   HepG2 and Huh-7 cells were cultured in DMEM supplemented with 10% FBS
   and 1% penicillin–streptomycin. siRNAs were purchased from Biosune, and
   the sequences of the siRNAs are shown in [63]Table [64]1
   . The experiment was performed in six-well plates, and 4%
   paraformaldehyde and a 0.1% crystal violet staining solution were used
   in this experiment.

   Huh-7 and HepG2 cells were seeded at a density of 2 × 10^4 or 2 × 10^5
   cells per well, respectively, and cultured for 16 h. The cells were
   then transfected with siRNAs for 24 h using Lipofectamine 2000. The
   cells were subsequently fixed with 4% paraformaldehyde, stained with
   0.1% crystal violet, and observed under a microscope.

CCK-8 Assay

   Following siRNA transfection, the cells were incubated for 24, 72, 96,
   or 120 h. At each time point, the culture medium was replaced with
   fresh DMEM supplemented with CCK-8 reagent (Cell Counting Kit-8), and
   the cells were incubated for an additional 2 h at 37 °C in a humidified
   incubator. After the incubation period, the absorbance of the formazan
   dye, which is directly proportional to the number of viable cells, was
   measured at 450 nm using a microplate reader.

EdU Assay

   After 72 h of siRNA transfection, the proliferation of the cells was
   assessed using the Click-iT EdU Alexa Fluor 488 Imaging Kit. Briefly,
   the cells were labeled with EdU, which is a nucleoside analog that is
   incorporated into newly synthesized DNA during active DNA replication.
   The cells were incubated with EdU labeling medium for 2 h at 37 °C.
   Following incubation, the cells were fixed with 4% paraformaldehyde and
   permeabilized with 0.3% Triton X-100. The incorporated EdU was then
   detected by a Click-iT reaction with Alexa Fluor 488 dye. Nuclei were
   counterstained with 4′,6-diamidino-2-phenylindole (DAPI).

Results

TMEM147 Expression Levels Were Increased across Various Cancer Tissues

   The expression profiles of TMEM147 across many cancer types in the TCGA
   database were analyzed. As shown in [65]Figure [66]1
   A, among all 18 types of cancer that were investigated, TMEM147 was
   significantly more highly expressed in 13 types of cancer—bladder
   urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA),
   cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal
   carcinoma (ESCA), kidney renal papillary cell carcinoma (KIRP), liver
   hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung
   squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectal
   adenocarcinoma (READ), stomach adenocarcinoma (STAD), and uterine
   corpus endometrial carcinoma (UCEC)—than in cancer-adjacent normal
   tissues. Data from the GTEx database were used as controls, and TMEM147
   was also found to be highly expressed in 27 out of 33 cancer types
   ([67]Figure [68]1B).

Figure 1.

   [69]Figure 1
   [70]Open in a new tab

   Expression level of the TMEM147 gene in tumor tissues and normal
   tissues. (A) TMEM147 expression in tumor tissues and adjacent normal
   tissues from the TCGA database; (B) TMEM147 expression in tumor tissues
   and normal tissues from the TCGA database when data from the GTEx
   database were used as controls (*P < 0.05, ** P < 0.01, *** P < 0.001).

TMEM147 Expression in Different Molecular or Immune Subtypes across Types of
Cancer

   To further explore the potential role of TMEM147 across cancers, we
   used data from the TISIDB database and found that TMEM147 was
   differentially expressed in different molecular subtypes of nine
   cancers and in different immune subtypes of 10 cancers. Among the
   molecular subtypes, TMEM147 was expressed at the highest levels in the
   CIMP-high subtype (P = 2.99 × 10^–04) of ACC ([71]Figure [72]2
   A), in the basal subtype (P = 6.61× 10^–15) of BRCA ([73]Figure
   [74]2B), in the classical subtype (P = 2.19 × 10^–03) of HNSC
   ([75]Figure [76]2C), in the C2b subtype (P = 2.19 × 10^–03) of KIRP
   ([77]Figure [78]2D), in the classic-like subtype (P = 8.2 × 10^–55) of
   LGG ([79]Figure [80]2E), in the iCluster subtype (P = 1.03 × 10^–02) of
   HIHC ([81]Figure [82]2F), in the ortical admixture subtype (P = 2.33 ×
   10^–03) of PCPG ([83]Figure [84]2G), in the HM-SNV subtype (P = 3.74 ×
   10^–05) of STAD ([85]Figure [86]2H), and in the CN-HIGH subtype (P =
   1.89 × 10^–03) of UCEC ([87]Figure [88]2I).

Figure 2.

   [89]Figure 2
   [90]Open in a new tab

   Correlations between TMEM147 expression and molecular subtypes across
   TCGA tumors. (A) ACC; (B) BRCA; (C) HNSC; (D) KIRP; (E) LGG; (F) LIHC;
   (G) PCPG; (H) STAD; (I) UCEC.

   Furthermore, regarding the immune subtypes, the expression of TMEM147
   was related to different immune subtypes (C1: wound healing; C2:
   IFN-γ-dominant; C3: inflammatory; C4: lymphocyte-depleted; C5:
   immunologically quiescent; C6: TGF-b-dominant) of 10 cancers, including
   BRCA ([91]Figure [92]3
   A), KIRC ([93]Figure [94]3B), KIRP ([95]Figure [96]3C), LIHC
   ([97]Figure [98]3D), LIAD ([99]Figure [100]3E), PRAD ([101]Figure
   [102]3F), LGG ([103]Figure [104]3G), STAD ([105]Figure [106]3H), TGCT
   ([107]Figure [108]3I), and SKCM ([109]Figure [110]3J).

Figure 3.

   [111]Figure 3
   [112]Open in a new tab

   Correlations between TMEM147 expression and immune subtypes across
   tumors from the TCGA database. (A) BRCA; (B) KIRC; (C) KIRP; (D) LIHC;
   (E) LUAD; (F) PRAD; (G) LGG; (H) STAD; (I) TGCT; (J) SKCM.

High Diagnostic Value of TMEM147 across Cancers

   To confirm the diagnostic value of TMEM147 across cancers, we generated
   ROC curves using TCGA data. The ROC curves showed high predictive
   values (ROC > 0.7) of TMEM147 in 15 types of cancer, including READ
   (AUC = 0.941; CI = 0.916–0.966), STAD (AUC = 0.786; CI = 0.750–0.822),
   DLBC (AUC = 0.955; CI = 0.933–0.977), LUSC (AUC = 0.809; CI =
   0.780–0.838), PRAD (AUC = 0.817; CI = 0.780–0.855), UCEC (AUC = 0.841;
   CI = 0.794–0.887), BLCA (AUC = 0.877; CI = 0.813–0.941), CHOL (AUC =
   1.000; CI = 1.000–1.000), LUAD (AUC = 0.778; CI = 0.747–0.808), LIHC
   (AUC = 0.908; CI = 0.880–0.937), COAD (AUC = 0.934; CI = 0.911–0.958),
   BRCA (AUC = 0.915; CI = 0.898–0.932), ESCA (AUC = 0.837; CI =
   0.685–0.990), LAML (AUC = 0.933; CI = 0.981–1.000), and PAAD (AUC =
   0.936; CI = 0.908–0.964) ([113]Figure [114]4
   A–O).

Figure 4.

   [115]Figure 4
   [116]Open in a new tab

   Receiver operating characteristic (ROC) curve for TMEM147 expression
   across cancers. (A) READ; (B) STAD; (C) DLBC; (D) LUSC; (E) PRAD; (F)
   UCEC; (G) BLCA; (H) CHOL; (I) LUAD; (J) LIHC; (K) COAD; (L) BRCA; (M)
   ESCA; (N) LAML; (O) PAAD.

TMEM147 Expression Was Correlated with the Prognosis of Patients with
Different Cancers

   To determine the prognostic role of TMEM147 across cancers,
   Kaplan–Meier analysis was performed, and the results showed that higher
   expression of TMEM147 predicted a lower probability of survival among
   patients with LICH ([117]Figure [118]5
   A–C), LGG ([119]Figure [120]5D–F), ACC ([121]Figure [122]5G–I), and
   MESO ([123]Figure [124]5J–L). The TMEM147 expression level was
   correlated with the OS, DSS, and PFI of patients with these types of
   cancer.

Figure 5.

   [125]Figure 5
   [126]Open in a new tab

   Correlations between TMEM147 expression and cancer patient prognosis
   (OS, DSS, and PFI). (A–C) LIHC; (D–F) LGG; (G–I) ACC; (J–L) MESO.

TMEM147 Expression Was Correlated with Immune Cell Infiltration in Tumors

   Infiltrating immune cells can recognize and attack cancer cells to
   suppress tumor growth and metastasis. Activation of the immune system
   helps to control tumor development and results in better prognosis. In
   our study, we investigated the relationship between TMEM147 expression
   and infiltrating immune cells. The heatmap showed that in most tumors,
   the expression of TMEM147 was negatively correlated with the
   infiltration of T helper cells and Tcm cells ([127]Figure [128]6
   A). Immune checkpoint-related genes play a role in regulating the
   antitumor immune response and affect the immune escape and progression
   of tumors in patients with liver cancer. Typically, immune checkpoint
   molecules balance the immune response and prevent overactivated immune
   cells from damaging normal tissues. We studied the relationships
   between the expression of six immune checkpoint genes (CD274, CTLA4,
   HAVCR2, LAG3, PDCD1, PDCD1LG2, and TIGIT) and TMEM147. Our results
   showed that TMEM147 expression was almost negatively correlated with
   immune checkpoint-related gene expression in BLCA, OV, SKCM, and THCA
   and positively correlated with immune checkpoint gene expression in LGG
   and LIHC, suggesting that TMEM147 is related to immune escape in cancer
   ([129]Figure [130]6B).

Figure 6.

   [131]Figure 6
   [132]Open in a new tab

   Correlation analysis of TMEM147 expression and immune cell infiltration
   across cancers. (A) Heatmap of the correlation of TMEM147 expression
   with 24 types of tumor-infiltrating immune cells. (B) Correlation
   analysis of TMEM147 expression and immune checkpoint expression in
   pan-cancer tissues.

Gene Functional Enrichment Analysis of TMEM147 across Cancers

   To investigate the mechanism by which TMEM147 functions during
   tumorigenesis, PPI network analysis was performed by utilizing the
   STRING database and Cytoscape. As shown in [133]Figure [134]7
   A, an interaction network of 50 TMEM147-binding proteins that were
   identified by experimental evidence was visualized. GO enrichment
   analysis of the 50 TMEM147-binding proteins was conducted ([135]Figure
   [136]7B), and the results showed that 19 pathways were enriched,
   including the structural constituent of ribosome, rRNA binding, protein
   transporter activity, protein transmembrane transporter activity,
   ubiquitin-protein transferase regulator activity, 5S rRNA binding,
   ribosome, cytosolic part, ribosomal subunit, large ribosomal subunit,
   cytosolic ribosome, cytosolic large ribosomal subunit, protein
   targeting, protein localization to endoplasmic reticulum, establishment
   of protein localization to membrane, protein targeting to membrane,
   cotranslational protein targeting to the membrane, mRNA catabolic
   process, nuclear-transcribed mRNA catabolic process, translational
   initiation, viral gene expression, viral transcription,
   nuclear-transcribed mRNA catabolic process, and nonsense-mediated decay
   ([137]Figure [138]7C). KEGG pathway enrichment analysis revealed that
   the most highly enriched pathways were ribosome, phagosome, Vibrio
   cholerae infection, and protein export.

Figure 7.

   [139]Figure 7
   [140]Open in a new tab

   PPI network, GO analysis, and KEGG analysis of 50 TMEM147-binding
   proteins. (A) PPI network; (B) visual network of GO and KEGG analyses;
   (C) GO analysis; (D) KEGG analysis.

High TMEM147 Expression Was Correlated with Poor Prognosis in Patients with
Different Clinical Characteristics

   According to the results of the pan-cancer study described above,
   TMEM147 was differentially expressed in liver cancer and may be used as
   a biomarker for liver cancer; therefore, we conducted a more in-depth
   study on LIHC. The higher expression of TMEM147 was associated with
   worse OS in patients with the following characteristics: age ≤ 60 years
   ([141]Figure [142]8
   A); N stage: N0 ([143]Figure [144]8B); M stage: M0 ([145]Figure
   [146]8C); AFP (ng/mL): 400 ([147]Figure [148]8D); sex: male
   ([149]Figure [150]8E); tumor status: positive ([151]Figure [152]8F);
   height ≥ 170 ([153]Figure [154]8G); race: Asian ([155]Figure [156]8H);
   histological grade: G3 and G4 ([157]Figure [158]8I). The higher
   expression of TMEM147 was associated with worse DSS in patients with
   the following characteristics: tumor status: positive ([159]Figure
   [160]9A); race: Asian ([161]Figure [162]9B); pathological M stage: M0
   ([163]Figure [164]9C); pathological N stage: N0 ([165]Figure [166]9D);
   sex: male ([167]Figure [168]9E); histological type: hepatocellular
   carcinoma ([169]Figure [170]9F). The higher expression of TMEM147 was
   associated with worse PFI in patients with the following
   characteristics: M stage: M0 ([171]Figure [172]10A); tumor status:
   positive ([173]Figure [174]10B); race: Asian ([175]Figure [176]10C);
   pathological N stage: N0 ([177]Figure [178]10D); age ≤ 60 years
   ([179]Figure [180]10E); height ≥ 170 ([181]Figure [182]10F).

Figure 8.

   [183]Figure 8
   [184]Open in a new tab

   Associations between TMEM147 expression and OS in different clinical
   subgroups of patients with LIHC. (A) Age > 60; (B) N stage: N0; (C) M
   stage: M0; (D) AFP (ng/mL): >400; (E) sex: male; (F) tumor status:
   positive; (G) height: >170; (H) race: Asian; (I) histological grade: G3
   and G4.

Figure 9.

   [185]Figure 9
   [186]Open in a new tab

   Associations between TMEM147 expression and DSS in different clinical
   subgroups of LIHC patients. (A) Tumor status: positive; (B) race:
   Asian; (C) pathological M stage: M0; (D) pathological N stage: N0; (E)
   sex: male; (F) histological type: hepatocellular carcinoma.

Figure 10.

   [187]Figure 10
   [188]Open in a new tab

   Associations between TMEM147 expression and the PFI in different
   clinical subgroups of LIHC patients. (A) M stage: M0; (B) tumor status:
   positive; (C) race: Asian. (D) pathological N stage: N0. (E) Age ≤ 60
   years; (F) height ≥ 170.

TMEM147 Expression Was Correlated with the Clinicopathological
Characteristics of LICH Patients

   The associations between the expression of TMEM147 and the clinical
   characteristics of LICH patients were explored, and the results are
   shown in [189]Table [190]2
   . AFP (ng/mL) (P < 0.001), BMI (P = 0.019), weight (P = 0.002), OS (P =
   0.002), T stage (P = 0.005), pathological stage (P = 0.004), and
   histological grade (P < 0.001) were significantly related to TMEM147
   expression.

Table 2. Clinical Characteristics of LIHC Patients.

   characteristic low expression of TMEM147 high expression of TMEM147 P
   value
   n 187 187
   tumor status, n (%)     0.163
   tumor-free 110 (31%) 92 (25.9%)
   with tumor 71 (20%) 82 (23.1%)
   AFP (ng/mL), n (%)     <0.001
   ≤400 123 (43.9%) 92 (32.9%)
   >400 20 (7.1%) 45 (16.1%)
   BMI, n (%)     0.019
   ≤25 77 (22.8%) 100 (29.7%)
   >25 91 (27%) 69 (20.5%)
   weight, n (%)     0.002
   ≤70 78 (22.5%) 106 (30.6%)
   >70 96 (27.7%) 66 (19.1%)
   OS event, n (%)     0.002
   alive 137 (36.6%) 107 (28.6%)
   dead 50 (13.4%) 80 (21.4%)
   T stage, n (%)     0.005
   T1 108 (29.1%) 75 (20.2%)
   T2 41 (11.1%) 54 (14.6%)
   T3 30 (8.1%) 50 (13.5%)
   T4 6 (1.6%) 7 (1.9%)
   pathological stage, n (%)     0.004
   stage I 103 (29.4%) 70 (20%)
   stage II 38 (10.9%) 49 (14%)
   stage III 34 (9.7%) 51 (14.6%)
   stage IV 1 (0.3%) 4 (1.1%)
   histological grade, n (%)     <0.001
   G1 35 (9.5%) 20 (5.4%)
   G2 102 (27.6%) 76 (20.6%)
   G3 43 (11.7%) 81 (22%)
   G4 5 (1.4%) 7 (1.9%)
   age, median (IQR) 62 (54, 69) 60 (50, 68) 0.123
   [191]Open in a new tab

   Moreover, the results of Welch one-way ANOVA revealed that TMEM147 was
   highly expressed in the tumor-positive group ([192]Figure [193]11
   A), in the AFP > 400 ng/mL group ([194]Figure [195]11B), in the BMI ≤
   25 group ([196]Figure [197]11C), in the weight ≤70 kg group
   ([198]Figure [199]11D), in the Dead group ([200]Figure [201]11E), in
   the T2 stage group ([202]Figure [203]11F), and in the G2, G3, and G4
   groups ([204]Figure [205]11G) compared with the control group.

Figure 11.

   [206]Figure 11
   [207]Open in a new tab

   Associations between TMEM147 expression and different clinical
   characteristics in LIHC patients. (A) Tumor status; (B) AFP; (C) BMI;
   (D) weight; (E) OS; (F) T stage; (G) pathological stage; (H)
   histological grade. ns, P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

Analysis of Molecules that Were Correlated with TMEM147 in LIHC Patients

   Using data from the TCGA database, we identified 50 genes that were
   most highly positively or negatively related to TMEM147 in LIHC.
   [208]Figure [209]12
   A shows the gene coexpression heatmap of the top 50 positively
   correlated genes, and the scatter plot shows that CNPAS1 (r = 0.740)
   ([210]Figure [211]12B), RFXANK (r = 0.742) ([212]Figure [213]12C), SMG9
   (r = 0.718) ([214]Figure [215]12D), EIF3K (r = 0.712) ([216]Figure
   [217]12E), TOMM40 (r = 0.730) ([218]Figure [219]12F), SNRPD2 (r =
   0.768) ([220]Figure [221]12G), PPP2R1A (r = 0.745) ([222]Figure
   [223]12H), GSK3A (r = 0.719) ([224]Figure [225]12I), TMM50 (r = 0.744)
   ([226]Figure [227]12J), and UBE2M ([228]Figure [229]12K) (r = 0.750)
   were the top 10 positively related genes. Moreover, the heatmap of
   negatively related genes ([230]Figure [231]13A) revealed the top 10
   genes, which included ABAT (r = −0.617) ([232]Figure [233]13B), C8A (r
   = −0.614) ([234]Figure [235]13C), DMGDH (r = −0.587) ([236]Figure
   [237]13D), EHHADH (r = −0.600) ([238]Figure [239]13E), CYP8B1 (r =
   −0.581) ([240]Figure [241]13F), F11 (r = −0.584) ([242]Figure
   [243]13G), PFKFB1 (r = −0.565) ([244]Figure [245]14H), GLYATL1 (r =
   −0.587) ([246]Figure [247]13I), ACSM2A (r = −0.603) ([248]Figure
   [249]13J), and GYS2 (r = −0.647) ([250]Figure [251]13K).

Figure 12.

   [252]Figure 12
   [253]Open in a new tab

   Top 50 genes that were positively correlated with TMEM147 expression in
   LIHC. (A) Gene coexpression heatmap of the top 50 genes that were
   positively correlated with TMEM147 in LIHC; (B–K) correlation analysis
   of the top 10 genes that were positively correlated with TMEM147 in the
   heatmap.

Figure 13.

   [254]Figure 13
   [255]Open in a new tab

   Top 50 genes that were negatively correlated with TMEM147 expression in
   LIHC. (A) Gene coexpression heatmap of the top 50 genes that were
   negatively correlated with TMEM147 in LIHC; (B–K) correlation analysis
   of the top 10 genes that were negatively correlated with TMEM147 in the
   heatmap.

Figure 14.

   [256]Figure 14
   [257]Open in a new tab

   PPI network construction and GO and KEGG analyses of DEGs between LIHC
   samples with high TMEM147 expression and LIHC samples with low TMEM147
   expression. (A) Volcano map of DEGs (red: upregulated; blue:
   downregulated); (B) GSEA; (C, D) GO and KEGG analyses of DEGs.

Differentially Expressed Genes (DEGs) Between LIHC Samples with High TMEM147
Expression and LIHC Samples with Low TMEM147 Expression

   To elucidate gene expression profiles, ggplot2 was used to identify
   sets of genes that were significantly up- and downregulated. Using |log
   (fold change)| > 1 and adjusted P value <0.05 as cutoff criteria, a
   total of 3818 upregulated genes and 1408 downregulated genes were
   identified ([258]Figure [259]14
   A). Then, we conducted GSEA and GO and KEGG enrichment analyses
   ([260]Figure [261]14B–D) of the DEGs, and the GSEA results revealed
   that genes that are related to arachidonic acid metabolism, butanoate
   metabolism, drug metabolism, cytochrome p450, drug metabolism, other
   enzymes, fatty acid metabolism, and metabolism of xenobiotics by
   cytochrome p450 were enriched ([262]Figure [263]14B). GO analysis
   revealed that the fatty acid catabolic process, fatty acid oxidation,
   lipid oxidation, regulation of chromosome segregation, lipid
   modification, membrane lipid metabolic process, branched-chain amino
   acid metabolic process, lipid catabolic process, sphingolipid metabolic
   process, membrane lipid biosynthetic process, and fatty acid metabolic
   process were enriched in BP, the chromosomal region was enriched in CC,
   and transferase activity, transferring acyl groups, coenzyme binding,
   and fatty acid binding were enriched in MF ([264]Figure [265]14C). The
   KEGG pathway enrichment showed that the DEGs were mainly related to
   propanoyl-CoA metabolism, propanoyl-CoA, beta-oxidation, and fatty acid
   degradation ([266]Figure [267]14D).

Analysis of the Association between TMEM147 Expression and Immune Cell
Infiltration in LIHC

   To better confirm the association between immune cell infiltration and
   TMEM147 expression in LIHC, the ssGSEA immuno-infiltration algorithm in
   GSVA was used. The Wilcoxon rank sum test showed that TH2 cells, TFH
   cells, and NK CD56 bright cells were significantly positively
   correlated with the expression of TMEM147 (P < 0.001) ([268]Figure
   [269]15
   A). The scatter plots in [270]Figure [271]15B show that Th2 cells (r =
   0.318, P < 0.001), TFH cells (r = 0.285, P < 0.001), and NK CD56 bright
   cells (r = 0.280, P < 0.001) show this positive correlation, but no
   significant associations were observed for macrophages, activated
   dendritic cells (aDCs), or Th1 cells. Moreover, Th17 cells, Tcm cells,
   neutrophils, Tregs, DCs, and cytotoxic cells were significantly
   negatively associated with TMEM147 expression ([272]Figure [273]16A).
   The scatter plots in [274]Figure [275]16B show this negative
   correlation of Th17 cells (r = 0.275, P < 0.001), Tcm cells (r = 0.260,
   P < 0.001), neutrophils (r = 0.217, P < 0.001), Tregs (r = 0.185, P <
   0.001), DCs (r = 0.182, P < 0.001), and cytotoxic cells (r = 0.144, P =
   0.005) with TMEM147 expression.

Figure 15.

   [276]Figure 15
   [277]Open in a new tab

   Analysis of the association of immune cell infiltration with TMEM147
   expression in LIHC revealed 6 immune cell types that were positively
   related to TMEM147 expression. (A) Enrichment scores of TMEM147
   expression in Th2 cells, TFH cells, NK CD56 bright cells, macrophages,
   aDCs, and Th1 cells. Correlation between TMEM147 expression and Th2
   cells, TFH cells, NK CD56 bright cells, macrophages, aDCs, and Th1
   cells (B); ns, P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 16.

   [278]Figure 16
   [279]Open in a new tab

   Analysis of the association of immune cell infiltration with TMEM147
   expression in LIHC revealed 6 immune cell types that were positively
   related to TMEM147 expression. (A) Enrichment scores of TMEM147
   expression in Th17 cells, Tcm cells, neutrophils, Tregs, DCs, and
   cytotoxic cells. (B) Correlation between TMEM147 expression and Th17
   cells, Tcm cells, neutrophils, Tregs, DCs, and cytotoxic cells; ns, P ≥
   0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

TMEM147 Promoted Cell Proliferation in Liver Cancer

   PCR and western blotting experiments revealed that TMEM147 was
   overexpressed in liver cancer cells, with higher expression levels
   observed in HepG2 and Huh-7 cells ([280]Figure [281]17
   A,B). Subsequent investigations were performed using the HepG2 and
   Huh-7 cell lines. Four siRNAs were designed and used to knock down
   TMEM147 expression in the cells, and the results demonstrated that
   siRNA-4 most effectively knocked down TMEM147 expression in HepG2
   cells, while siRNA-3 had the greatest effect on Huh-7 cells
   ([282]Figure [283]17C). CCK-8 and colony formation assays indicated a
   significant reduction in cell proliferation after TMEM147 knockdown
   ([284]Figure [285]17D,E). Moreover, EDU experiments demonstrated that
   downregulation of TMEM147 reduced DNA replication in cells, thereby
   affecting cell proliferation ([286]Figure [287]17F).

Figure 17.

   [288]Figure 17
   [289]Open in a new tab

   TMEM147 promoted cell proliferation in liver cancer. (A, B) TMEM147 is
   overexpressed in liver cancer cells. (C) siRNA was used to knock down
   TMEM147 expression in HepG2 and Huh-7 liver cancer cells. (D) The
   results of the CCK-8, (E) colony formation, and (F) EdU assays were
   obtained.

Discussion

   Cancer is a major public health problem worldwide, and it is the second
   leading cause of death in the United States.^[290]17 Researchers have
   continuously worked to develop treatments for cancer. Currently, there
   are many methods and approaches for treating tumors, such as surgery,
   radiotherapy, chemotherapy, biological therapy, and immunotherapy; each
   of these methods is efficacious and has its own advantages. However,
   investigations of tumor biomarkers are still very limited, and further
   research is warranted.

   The TMEM family is a group of proteins that span the entire width of
   the lipid bilayer and are permanently anchored in membranes. Many TMEMs
   act as channels, allowing the transport of specific substances across
   biological membranes.^[291]18 Because they allow specific substances to
   be transported through biofilms, facilitate communication between
   intracellular and extracellular spaces, and perform other unknown
   biological functions, TMEM family members play important roles in the
   migration, metastasis, and progression of a variety of
   tumors.^[292]19,[293]20 TMEM45A and TMEM205 have been implicated in
   tumor progression and invasion but also in tumor
   chemoresistance.^[294]21 Few studies have systematically explored the
   expression of TMEM147, which is a member of the TMEM family, across
   cancers via bioinformatics. This study aimed to investigate the role of
   TMEM147 across cancers and to use LIHC as a specific example to
   illustrate the potential value of TMEM147.

   In the present study, by using high-throughput RNA sequencing data, we
   found that TMEM147 is differentially expressed across cancers, and ROC
   curves of TMEM147 expression in 15 kinds of cancer revealed a high AUC,
   indicating that TMEM147 might be a potential diagnostic biomarker. The
   heterogeneity or intrinsic diversity of cancer is the main reason why
   cancers can adapt to pharmacological pressures and then evolve;^[295]22
   for instance, LIHC is a heterogeneous disease at both the molecular and
   histological levels.^[296]23 Heterogeneity is a major obstacle to the
   development of effective targeted therapies, and studying the
   heterogeneity of tumors can help clinicians implement individualized
   and differential treatments for patients.^[297]24,[298]25 The subtype
   analysis in our study revealed that TMEM147 was differentially
   expressed in many molecular and immune subtypes of cancer, which may
   provide directions for treatment in the context of tumor heterogeneity.
   Notably, TMEM147 is closely correlated with both molecular and immune
   subtypes in BRCA, KIRP, LGG, LIHC, and STAD. Furthermore, Kaplan–Meier
   survival curves confirmed the high prognostic value of TMEM147 among
   patients with LIHC, LGG, ACC, and MESO. This result shows that TMEM147
   could be a biomarker for the prognosis of patients with different kinds
   of cancer. To further understand the role of TMEM147 across cancers,
   enrichment analysis of 50 TMEM147-binding proteins was performed. The
   results revealed that ribosome-related functions and membrane
   protein-related functions, which are associated with the transmembrane
   protein activity of TMEM147, were highly enriched. These results
   demonstrated the potential role of TMEM147 across cancers.

   Among all malignant diseases, liver cancer is the fourth most common
   cause of cancer-related deaths worldwide and ranks sixth in terms of
   incidence.^[299]26 Viral hepatitis, alcoholism, and fungal toxin
   exposure are major risk factors for the development of liver cancer,
   but in recent years, obesity, insulin resistance, and type 2
   diabetes-associated fatty liver disease (NAFLD) have been recognized as
   triggers of liver cancer, especially in developed countries.^[300]27
   Despite its prevalence and high mortality rate, reliable treatments to
   reduce mortality are still scarce,^[301]28 and predictive biomarkers
   are urgently needed to inform the selection of molecular and
   immune-based treatments in addition to surgery, liver transplantation,
   and local ablative and intra-arterial therapies.^[302]29

   In addition to poor prognosis of patients with different kinds of
   cancers, multiple clinical subgroups of patients with LIHC who also had
   high TMEM147 expression had poor DSS and PFI. Moreover, the expression
   of TMEM147 differed according to different clinical characteristics.
   More importantly, in the functional analysis, the KEGG, GO, and GSEA
   analyses revealed enrichment of metabolism-related pathways, such as
   fatty acid metabolism, arachidonic acid metabolism, lipid modification,
   and membrane lipid metabolic processes. Abnormal metabolic processes,
   including abnormal levels of lipid metabolism, can affect tumor
   metastasis, recurrence, and drug resistance and induce tumor immune
   evasion by influencing the antitumor response of immune cells.^[303]30
   In the tumor microenvironment, T cells, macrophages, DCs, NK cells, and
   other immune cells can regulate immune checkpoints, resulting in the
   inhibition of tumor cell elimination.^[304]31 The liver is a major
   regulatory organ that manages metabolism throughout the body and
   maintains metabolic homeostasis; once metabolic homeostasis is
   disrupted, a variety of cancers, including LIHC, may occur.^[305]32
   Previous studies have shown that disorders of glucose
   metabolism,^[306]33 lipid metabolism,^[307]34 and nucleotide
   metabolism^[308]35 may lead to the occurrence of liver cancer via
   different pathways. Understanding the changes that occur in the
   metabolic profile during hepatocarcinogenesis is important for
   elucidating the pathogenic mechanisms of cancer and identifying new
   diagnostic and prognostic biomarkers. In our study, analysis of the
   correlation between TMEM147 expression and immune cell infiltration in
   LIHC revealed that TMEM147 was positively related to Th2 cells, TFH
   cells, and NK CD56 bright cells and negatively related to Th17 cells,
   Tcm cells, neutrophils, Tregs, DCs, and cytotoxic cells. Experiments
   that were performed with the normal human liver cell line MIHA and four
   LIHC cell lines (BEL-7402, SMMC-7721, HepG2, and Huh7 cells) confirmed
   the role of TMEM147 in promoting liver cancer cell proliferation,
   further confirming the clinical value of TMEM147 in the diagnosis of
   liver cancer.

   In conclusion, this is the first study to investigate the oncogenic
   role of TMEM147 across cancers as well as its carcinogenic role in
   liver cancer. The identification of the diagnostic and prognostic
   significance of TMEM147 across cancers, and further exploration of its
   importance in LIHC, can provide new insights for the comprehensive
   understanding of the key role of TMEM147 in tumor promotion and
   inhibition, provide a comprehensive basis for in-depth verification of
   its function in molecular biology experiments, and even provide
   integrated analysis results for the development of future cancer
   treatments for use in the clinic.

Acknowledgments